Does an early childhood caries intervention among Aboriginal Australian children have long-term effectiveness?At the 3-year follow-up of a randomized clinical trial involving 448 mothers and their children, children who received the intervention during pregnancy or early infancy had statistically less clinically detected untreated dental caries than their counterparts in the delayed intervention group.A multipronged, culturally safe early childhood caries intervention delivered earlier rather than later in infancy may confer greater benefits among this population. This secondary analysis of a randomized clinical trial investigates the long-term effectiveness of an intervention that aimed to reduce dental caries among South Australian Aboriginal children. Testing the long-term usefulness of a childhood intervention and determining the best age of implementation are important for translation and policy change.To investigate among children aged 3 years the long-term effectiveness an intervention that aimed to reduce dental caries among South Australian Aboriginal children and to assess if children in the delayed intervention (DI) group had any benefit from the intervention from ages 2 to 3 years and if the intervention usefulness was greater when delivered between pregnancy and age 2 years (immediate intervention [II] vs ages 2 to 3 years [DI]).Secondary analysis of a randomized clinical trial. The study enrolled 448 pregnant women across South Australia, Australia, at baseline , with 223 randomly allocated to the II group and 225 to the DI group. Three-year follow-up data were collected November 2014 to February 2016.The intervention comprised dental treatment to mothers, fluoride varnish application to children, and motivational interviewing delivered together with anticipatory guidance. This was delivered during pregnancy and at child ages 6, 12, and 18 months for the II group and at child ages 24, 30, and 36 months for the DI group.The mean number of decayed teeth measured at child age 3 years.P = .05).There were 324 children at age 3 years . The mean number of decayed teeth at age 3 years was 1.44  for the II group and 1.86  for the DI group . The predicted mean number of decayed teeth at age 3 years for the DI group was 2.15. Between ages 2 and 3 years, the caries increment for the II group was 0.82 , compared with 0.97  for the DI group (At the 3-year follow-up, II children had less dental caries than DI children, DI children developed dental caries at a lower trajectory than predicted had the intervention not been received at ages 2 to 3 years, and the caries increment was less between ages 2 to 3 years among II children compared with DI children. This study suggests that the best time to implement the intervention is earlier rather than later infancy.ACTRN12611000111976Australian and New Zealand Clinical Trial Registry Ideintifier:  Dental disease in childhood may contribute to poor nutrition, alter ability to sleep and learn and play, negatively influence quality of life, and lead to increased financial stress in the family. Poor oral health in childhood is 1 of the key determinants of poor oral health in adulthood.2 A key global health target is reducing its prevalence.3 Most public health interventions to date that aim to reduce early childhood caries have focused on healthy diet,4 application of therapeutic agents that include topical fluorides and antimicrobials,6 effective toothbrushing,7 and behavioral strategies.9 However, these approaches may be insufficient because early childhood caries is more than just a biological response to sugar; it involves both biological and social constructs. To our knowledge, the strategies demonstrated as being effective have not been combined and tested in a programmatic approach. Also, to our knowledge, there has also been neither long-term follow-up of these interventions nor a test to demonstrate at what age in childhood the interventions might have the most effectiveness.Poor oral health in childhood is socially patterned.10 Indigenous children aged 0 to 4 years represented 11% of the total Indigenous population, while similarly aged non-Indigenous children represented 6% of the total non-Indigenous population.10 Owing to sustained government policies of forced child removals, discrimination, and disempowerment, contemporary Indigenous Australians reflect 1 of the most disenfranchised groups at an international level. In 2015, Australia ranked second (after Norway) in the Human Development Index.11 However, this ranking dropped to 122nd when Indigenous Australian populations were separately analyzed.11Indigenous Australians identify as being of Aboriginal and/or Torres Strait Islander descent. In the 2016 Census, they represented 3% of the total Australian population.16 The literature suggests that many of the conditions experienced in Indigenous childhood are antecedents to chronic disease in later life.17 Dental disease in Indigenous Australian children is common and, in many cases, severe. It frequently causes acute distress, with care under a hospital-based general anesthetic being the only treatment option available. In the National Child Oral Health Survey, 44% of Indigenous children had 1 or more deciduous teeth with untreated dental caries compared with 26% of non-Indigenous children.18 Indigenous children in some locations have dental disease levels that are 5 times that of non-Aboriginal children.19 Lack of access to service providers is frequently cited as a reason for this inequity together with specific behavioral risk factors and social determinants.20Indigenous Australian children score lower on most indicators of general health and well-being compared with non-Aboriginal Australian children.21) among Indigenous Australian children, which comprised provision of dental treatment to mothers during pregnancy, application of fluoride varnish to teeth of children , and motivational interviewing (MI) delivered together with anticipatory guidance. The primary outcome was reported at child age 2 years, at which time the mean number of decayed teeth (mean dt) was significantly less in the intervention group compared with the control group. After the 2-year follow-up, the control group received the intervention (a requirement of our ethics committee), with a further follow-up of both groups conducted at child age 3 years. This provided an opportunity to test (1) if the usefulness of the intervention among the immediate intervention (II) group persisted at the 3-year follow-up, (2) if the predicted trajectory of untreated dental decay in the delayed intervention (DI) group decreased after the intervention from ages 2 and 3 years, and (3) if the intervention was more effective in pregnancy or early infancy compared with later infancy or early childhood.Results have previously been reported from an early childhood caries intervention  that consequences of the trial would be demonstrated at the 3-year follow-up , (2) that children in the DI group would have some benefit from the intervention from ages 2 to 3 years , and (3) that the intervention would be more effective  in pregnancy or early infancy (II group) than in later infancy or early childhood (DI group).21 The primary outcome of the original trial was the mean number of teeth with untreated dental decay in children at age 2 years. The trial protocol for the initial study was previously published.22This study was a 1-year follow-up to Baby Teeth Talk, a 2-arm parallel, outcome assessor–masked, randomized clinical trial conducted in South Australia, Australia .21 The p23 and Good Clinical Practice guidelines were followed for both the original trial and the follow-up. The University of Adelaide Human Research Ethics Committee, the Aboriginal Health Council of South Australia, the government of South Australia, and the human research ethics committees of participating South Australian hospitals all provided ethical approval for the study. This study followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.The ethical standards of the Declaration of Helsinki21 Recruitment was mainly through the antenatal clinics of South Australian hospitals and through Aboriginal Community Controlled Health Organisations. All participants provided written informed consent. We randomly allocated 448 women to either an II group or a DI group.Baby Teeth Talk enrolled women who identified as being pregnant with an Aboriginal and/or Torres Strait Islander child between February 1, 2010, and May 30, 2011. Three-year follow-up data were collected November 2014 to February 2016. The subsequent sample comprised two-thirds of those who were eligible during the recruitment phase and was representative by maternal age, socioeconomic characteristics, and tobacco smoking status.24 Study staff who had been trained in its use applied the varnish. The child’s head would be on the lap of the study staff, with a knee-to-knee position adopted. Fluoride varnish was applied beginning with the back teeth (if present) and then progressing forward to the front teeth (after teeth had been cleaned and dried with gauze). It was suggested to caregivers to not provide the children with food or drink for the half hour immediately after fluoride varnish application. Third was anticipatory guidance. This occurred in conjunction with the MI during pregnancy and when children were aged 6, 12, and 18 months. The educational packages were tailored to include explicit oral health information relevant to pregnancy gingivitis and dental care provision (delivered during pregnancy), focus on first solid foods on eruption care for baby teeth (child age 6 months), focus on fluoride and toothbrushing and avoiding food and beverages with high sugar levels (child age 12 months), and focus on eruption of molar teeth at the child’s first dental checkup (child age 18 months). Fourth was MI. As mentioned, the MI component was delivered in conjunction with anticipatory guidance. A basic 2-day MI training course was attended by all study staff, followed by a 1-day follow-up course that was more intense in its delivery and purpose. One-day follow-up courses continued monthly for 6 months, followed by bimonthly single-day coaching and telephone coaching, when needed, for another year. The sessions were customized to meet the needs of each participant, with each session conducted on a one-to-one basis in participants’ homes or other venues where participants felt comfortable . Each session lasted around 30 to 90 minutes. As recommended by Venner and colleagues,25 pictorial prompts and plain English were used. Fidelity was acceptable25 and was assessed by a member of the Motivational Interviewing Network of Trainers.The II group received 4 interventions. First was provision of dental care to mothers during pregnancy. Publicly funded dental care through South Australia’s Dental Service (SADS) was provided to mothers allocated to the intervention arm who held a government means-tested health care card. Study staff, in conjunction with the SADS’s Aboriginal Liaison Program, coordinated transportation and appointments. Six dental providers in the private sector, who were partners in the project, provided care to participants who were not eligible for publicly funded dental care (no costs incurred to the participant). Although cosmetic dentistry, endodontics, or orthodontics was not included, participants did receive, where necessary, extractions (including wisdom teeth), restorations, scale and cleans, radiographs, and checkups. Second, at child ages 6, 12, and 18 months, fluoride varnish was applied to teeth. The fluoride varnish protocol was adapted from that created by Slade and colleagues.Mothers in the DI group received dental care when their child was aged 24 months. Fluoride varnish application to the teeth of the children, anticipatory guidance, and MI were delivered at child ages 24, 30, and 36 months, respectively.Oral examinations were conducted by calibrated and masked examiners at child ages 24 and 36 months. These examiners followed a standardized protocol to record dental disease experience, which included predecayed and decayed (noncavitated plus cavitated lesions), missing, and filled surfaces. Children were examined in the knee-to-knee position on their mother’s lap, as is considered appropriate for this age group. Teeth were dried with cotton pads before examination. The light source was a fiber-optic light, and standard infection control procedures were followed. Only visual criteria were used to assess diagnoses, with measures including untreated dental caries, teeth missing owing to dental disease experience, and teeth filled owing to dental disease experience. The SADS was the provider referred to when any child was diagnosed as having untreated dental caries.In the present analysis, the primary outcome was the mean number of teeth with untreated dental caries in the child at a mean age of 3 years. Dental decay was computed at the threshold of precavitation, an area of demineralization without loss of surface continuity and cavitation  . We also examined the mean levels of missing and filled teeth, as well as the prevalence of untreated dental decay and missing and filled teeth .We collected descriptive data at baseline. These included maternal age, education, income, government means-tested health care card status and residential location (sociodemographic characteristics), usual reason for visiting a dentist, maternal toothbrushing behavior, self-rated oral health, and self-rated general health .Based on a median split, maternal age was dichotomized into 14 to 24 years vs 25 or more years , while education was split into high school or less vs trade/technical or university. Income was characterized by job vs Centrelink (welfare) and owning of a government means-tested health care card (yes vs no). In Australia, welfare payments are made to the unemployed through Centrelink. Metropolitan (Adelaide and outer suburbs) vs nonmetropolitan  were the descriptors used to characterize residential location.“What is your usual reason for seeing a dentist?” and “Did you brush your teeth yesterday?” were the questions used to characterize dental behavior, with response options including problem vs checkup for the former and yes vs no for the latter. The question “How do you think your general/dental health is?” was used to characterize self-rated oral health and general health status. Responses were dichotomized to excellent, very good, or good vs fair or poor.26 a sample size of 362  was estimated to be necessary to detect a 20% difference  in early childhood caries prevalence between the 2 groups, at a 2-sided .05 significance criterion and 80% power. Assuming an attrition rate of 20% after 24 months, 450 participants at baseline would be necessary. We recruited 448 at baseline.Based on an intervention focusing on early childhood caries among Australian Northern Territory–based Indigenous children,27 we decided a priori to also investigate usefulness according to metropolitan vs nonmetropolitan areas.Intent-to-treat principles underpinned the data analyses approach. For both the II and DI groups, the number and percentage of participant characteristics at baseline were calculated. General linear regression models with Poisson distribution were used to compare the usefulness of the intervention at child age 3 years (characterized by the mean dt) between the II and DI groups. This was after adjusting for maternal sociodemographic, dental behavior, and health status characteristics at baseline. The mean number of teeth per child in which prevention of dental caries occurred owing to the intervention is characterized by the usefulness estimate. The estimate’s precision is demonstrated by the 95% CIs, with the difference between groups considered to be statistically significant if the 95% CIs do not include zero. Owing to differences reported in the literature of Aboriginal child experience of dental caries by residential location,t tests to test the 3 hypotheses. To test for hypothesis 1, the difference in outcome (mean dt) at age 3 years between the II and DI groups was calculated. To test for hypothesis 2, the predicted mean dt (if no intervention was received) within the DI group was compared with the actual mean dt estimates in the II group at age 3 years. To test for hypothesis 3, the predicted mean dt (if received or had the same intervention effect) within the DI group was compared with the actual mean dt estimates in the II group at age 3 years.We predicted the mean untreated decay (dt) in the DI group under the following 2 assumptions and scenarios: (1) if the caregiver or child in the DI group did not receive the intervention at all and (2) if the intervention’s effectiveness would be same in both the II and DI groups based on the estimated mean untreated decay (dt) of both groups at 2 and 3 years’ follow-up. We then drew an observed trajectory . We usediY(dt3_p1_DI) = iY(dt3_DI) + βNIX , βNI = 0.89 − 0.62 = 0.27. For predicted 2 (p2), if the intervention effectiveness would be the same in both the II and DI groups, then the number of dt would be as follows: iY(dt3_p2_DI) = iY(dt3_DI) − βEIX (if iY(dt3_DI) = 0, then iY(dt3_p2) = 0), , βEI = (1.86 − 0.89) − (1.44 − 0.62). Slope was βNI and βEI. For intercept, Yi(dt3_DI) was the observed or estimated mean number of dt in the DI group at 3 years.Formulas were used for the predicted number of teeth with untreated decay (dt). For predicted 1 (p1), if a caregiver or child in the DI group would not receive the intervention, then the number of dt would be as follows: t test after adjusting for confounders.To test for hypothesis 1, the difference in outcome (mean dt) at age 3 years between the II and DI groups was calculated after adjusting for other confounders. To test for hypothesis 2, the predicted trajectory of the mean dt at age 3 years within the DI group was compared with the actual dt estimates after receipt of the intervention between ages 2 and 3 years. To test for hypothesis 3, the difference in the mean dt from ages 2 to 3 years was calculated using a paired preventive fraction. The preventive fraction is obtained by dividing the absolute value of the usefulness estimate by the mean of the dependent variable under consideration in the control group . A fully conditional specification method with logistic regression for binary variables and linear regression for continuous variables was used to impute missing data assuming data were missing at random. Missing data from both baseline and outcome variables were imputed, excluding deceased infant outcomes. The II and DI groups were imputed separately. Fifty data sets were imputed using 50 iterations first. Then, the MIANALYZE procedure combined results of the analyses from each imputed data set to generate univariate valid statistical inference.28 The primary results are from imputed analyses.The proportion of cases able to be prevented if a group takes part in an intervention compared with a group that does not take part is termed the 21 To impute and analyze data, we used a statistical software program .The missing not at random (MNAR) adjustment statement was used in sensitivity analyses to impute our primary outcome variable  under the missing at random (MAR) assumption. This included maximum and minimum value imputations, as well as different percentages. The randomization and concealment of allocation processes have been described elsewhere.We recruited 448 mothers pregnant with an Aboriginal child between February 1, 2011, and May 30, 2012; of these, 223 were randomly allocated to the II group, and 225 were randomly allocated to the DI group . At baseThe first follow-up visit was conducted at child mean age 2 years, with clinical dental data from 324 children (159 II and 165 DI) available. The second follow-up visit was conducted at child mean age 3 years, with clinical dental data from 324 children (157 II and 167 DI) available . Mothers of children lost to follow-up by 3 years compared with those remaining in the study shared largely the same distribution of baseline characteristics. The exceptions were maternal age for children lost to follow-up and self-rated oral health for children at the 3-year follow-up  and dental caries experience  was more than 8% higher among the DI group than the II group at age 3 years . This inAt child age 3 years, the mean dt was 1.44  for the II group and 1.86  for the DI group ; the difAt child age 3 years, the prevalence of untreated dental caries and the number needed to treat (NNT) are listed in P = .02).The estimated untreated dental caries was less in the DI group after receipt of the intervention between ages 2 and 3 years than if no intervention had been received . The preP = .02).The estimated untreated dental caries was more in the DI group after receipt of the intervention between ages 2 and 3 years than if the same intervention was received earlier in infancy . The preP = .05).Between ages 2 and 3 years, the caries increment for the II group was 0.82 . This is compared with 0.97  for the DI group (In eTable 2 in the We tested 3 hypotheses in this study. These include that (1) usefulness of a standardized, robustly structured, culturally safe, and carefully administered early childhood caries trial delivered to an Australian Aboriginal population between pregnancy and child age 18 months would be demonstrated at the 3-year follow-up; (2) the trajectory of untreated dental decay among children in the DI group would be lower than predicted after receipt of the intervention at age 2 years; and (3) the intervention would be more effective  in pregnancy or early infancy than in later infancy or early childhood.Our first hypothesis proved true. At the 3-year follow-up, children in the II group who received the intervention during pregnancy or early infancy had statistically less clinically detected untreated dental caries than their counterparts in the DI group. In other words, approximately four-fifths of children in the II group had no experience of dental caries at age 3 years compared with only two-thirds of children in the DI group. Our second hypothesis also proved true, with children in the DI group developing further dental caries between ages 2 and 3 years, but at a lower trajectory than that predicted had the intervention not been received. Our third hypothesis also proved true; the caries increment was less between ages 2 to 3 years among children in the II group compared with the DI group.Our study has numerous strengths. These include strict adherence to (and success of) the randomization process, inclusion criteria that were broad in their scope , appropriate sample size, extensive follow-up, and masking of those involved in outcome assessments. We may have missed child death recordings, and there were some missing data on outcome items, meaning that internal validity may have been weakened. Furthermore, it was not possible to obtain data from administrative sources on postnatal characteristics that might have influenced the primary outcome . However, the randomization process was likely to have distributed factors external to the trial evenly among the II and DI groups, although we appreciate that this does not always hold true. In addition, our outcome data held true after sensitivity analyses, and there were no concerning differences between the baseline characteristics of those lost to follow-up and those who remained in the study. We consider our findings robust, representative, and suggestive of an association between a culturally safe and context-specific oral health intervention and improved and sustained child oral health outcomes for the following 3 main reasons: (1) biological plausibility, (2) consistency among both a range of indicators and time, and (3) constant strength of association.The main limitation of the study is that the influence of the third intervention delivered at 36 months to the DI group  could not be evaluated. The reason is because this was the same time point at which clinical outcomes were assessed.29 Although encouraging, these results should not be interpreted to be effective among all Aboriginal child groups in Australia, who each have important geographical, historical, and contextual differences.Therefore, the study results support the hypothesis that an early childhood caries intervention delivered earlier rather than later in infancy confers greater benefits. However, it is worth highlighting that the overall prevalence of untreated dental decay was still much higher than reports among the general child population in South Australia.During the study duration, there were no secular trends in Aboriginal child dental health outcomes in the study area or major changes to dental health policy or structuring. However, the recommendation for fluoride varnish to be implemented by nondental staff was mandated in many Australian jurisdictions during our analyses phase. In addition, caregivers of Aboriginal children in South Australia are now eligible for free or low-cost publicly funded dental care through the South Australian Dental Service, including a waiting list waiver and transportation services. This was not available at the time of our study.Our findings suggest the following: (1) that at the 3-year follow-up children in the II group who received the intervention during pregnancy or early infancy had statistically less clinically detected untreated dental caries than their counterparts in the DI group, (2) that children in the DI group developed dental caries at a lower trajectory than predicted had the intervention not been received at ages 2 to 3 years, and (3) that the caries increment was less between ages 2 and 3 years among children in the II group compared with the DI group, indicating that the best time to implement the intervention is earlier rather than later infancy. Our findings have value for Aboriginal Community Controlled Health Organisations in Australia with an interest in including dental care and oral health promotion in their scope of health service provision and indeed at an international level because Indigenous children worldwide are recognized as having poorer oral health outcomes than their non-Indigenous peers.


To resolve this issue, we attached a small photo multiplier tube (PMT) tightly to the body surface of transgenic mice carrying a reporter gene such that the photon input window faced target organs such as the liver and kidney and succeeded in long-term continuous measurement of circadian gene expression in semi-freely moving mice over periods of several weeks. Using this simple method, we clearly showed that lithium causes circadian period elongation in peripheral clock gene expression rhythms in vivo. Further development of our detection system to maturity will aid a wide range of research fields in medicine and biology.Lithium has been used as a mood stabilizer to treat human bipolar disorders for over half a century. Several studies have suggested the possibility that the efficacy of lithium treatment results in part from the amelioration of circadian dysfunction. However, the effect of lithium on clock gene expression has not yet been investigated  One piece of evidence for the putative action of lithium on circadian rhythms is that lithium administration lengthens the circadian period in behavior and physiology3. In addition to this behavioral and physiological effect, administration of lithium to cultured cells and tissues causes circadian period elongation in clock gene expression rhythms6, suggesting that lithium directly acts on cell-autonomous core circadian machinery in vitro and ex vivo. However, a significant question is why the medium concentrations of lithium required for circadian period elongation in clock gene expression rhythms in vitro and ex vivo are much higher than the plasma concentrations required for period elongation in behavioral rhythms in vivo5. The effect of lithium on circadian period of clock gene expression has not yet been investigated in vivo.Several studies have suggested the possibility that mood stabilizing effects of lithium result in part from the amelioration of circadian dysfunctionin vivo using traditional experimental approaches is technically difficult. Obtaining biochemical data on sequential changes in gene expression over a period of a few days requires extensive experimentation: for example, if organs are harvested 16 times with a sampling interval of 3 hours over a period of 48 hours and measurements are done in triplicate, the total number of mice used will be 48. Following this overnight organ collection, vast numbers of frozen organs are individually homogenized, and gene expression levels are quantified by biochemical analysis. Despite this extensive experimentation, the 3-h interval expression data thereby obtained are not sufficient for reliable evaluation of circadian period length because of low time resolution and short measurement period. Increasing sampling frequency and extending the sampling period may solve this problem, but it is time- and cost-prohibitive. Some of the technical limitations mentioned above can be solved with in vivo imaging technology7. Photons from anesthetized mice carrying the luciferase gene, the expression of which is controlled by a target gene promoter, are detectable with a high-sensitivity camera as relative expression levels of the target gene. However, images must be taken at set time points overnight, which limits efforts to extend the measurement period and increase time resolution by narrowing measurement intervals. In addition, repeated exposure to anesthesia may have a marked impact on animal physiology.Detection of changes in period length of clock gene expression rhythms in vivo, we aimed to monitor the sequential expression of circadian clock genes in real time. A few studies have reported real-time monitoring of bioluminescence driven by a clock gene promoter in animal organs. For example, Yamaguchi et al. reported a fascinating method for real-time detection of bioluminescence emitted from the hypothalamic suprachiasmatic nuclei (SCN), in which an optical fiber was inserted into a mouse brain8. However, this method requires a specific technique, and whether or not long-term monitoring using this method is possible remains unclear, as the brain tissue is partially but severely damaged. On the other hand, a recent study reported the real-time monitoring of liver clock gene expression in freely moving mice in dark boxes equipped with a highly sensitive camera or photo multiplier tube (PMT)9. This method overcomes many of the problems mentioned above and allows real-time monitoring in the liver for up to 10 days. In addition to this measurement method, a very recent study reported another in vivo imaging technique to perform long-term imaging of circadian gene expression by using dual-focal 3D tracking10. However, these two systems would be too cumbersome and expensive. In the present study, we therefore aimed to develop a simple and low-cost method for real-time monitoring of clock gene expression rhythms in vivo.In the present study, to investigate the effect of lithium on circadian period length of clock gene expression with high time resolution Period2luciferase (Per2Luc) knock-in mice and Bmal1-Eluc (enhanced green-emitting luciferase) transgenic mice were gifts from Dr. Joseph Takahashi and Dr. Yoshihiro Nakajima, respectively12. Animals were bred and maintained on a 12-hour light-dark (LD) cycle (lights on at 9:00 A.M.) and allowed ad libitum access to food and water. Real-time monitoring of bioluminescence was done using male C57BL/6 background Per2Luc and Bmal1-Eluc mice depilated of body hair around target organs with depilatory cream or male C57BL/6 background Tyrosinase mutant (Tyrc-Brd) homozygous Per2Luc mice. Pigment is completely absent from skin, hair and eyes in mice homozygous for Tyr. The number and age of mice used in each experiment are indicated in the figure legends. All protocols for animal experiments were approved by the Animal Research Committee of Yamaguchi University. Animal studies were performed in compliance with the Yamaguchi University Animal Care and Use guidelines.in vivo imaging. Mice were injected with in vivo-grade luciferin  s.c. on the back near the neck at a dose of 8.3 mg/kg. Images were then taken at 5 min after luciferin injection with 5-min exposure from the dorsal aspect under isoflurane anesthesia inside a dark box.The Lumazone CMS imaging system  was utilized for ad libitum.Real-time detection of bioluminescence was performed by attaching a PMT  onto the body surface. The photon input window of the PMT directly and tightly faced the target organ. In this experimental approach, the distance from the PMT to the target organ was not only nearly constant but also as short as possible, and ultraweak bioluminescence therefore became detectable without the need for enhancing signals by strong overexpression of luciferase. The weight of the PMT was about 10 g, the physical stress was therefore reduced by elastically hanging it from a top board of the dark box. Although tight attachment of the PMT to the body surface prevented the mice from moving with complete freedom, this was considered beneficial for monitoring, as any vigorous movements caused data noise due to vibrations. Mice were able to access food and water in vivo grade, 157 mM) was continuously supplied from an osmotic pump  implanted intraperitoneally or subcutaneously with a release rate of 1 microliter per hour and a release duration of up to one week, according to manufacturer’s instructions. However, to enhance bioluminescence and extend the measurement period, a syringe pump  was used for continuous subcutaneous supply of luciferin . The release rate of luciferin (7.85 mM) was 30 µL/h. After operation for subcutaneous insertion of a silicone tube  under continuous anesthetization with vaporized isoflurane (1.8–2.8%), mice were jacketed to ensure that the PMT was tightly attached to the body surface. Specifically, an open cylinder made of sponge-like material was fixed as a socket for the PMT on the mouse’s back with an elastic medical band. When the kidney was a target organ, these were localized around the lowest rib on the back, where the center of the cylinder was set. The cables and silicone tube were covered with a hard plastic protector. Although photon counting was initiated under constant dark conditions immediately after these manipulations and surgical operation, data for the first 2 days were not used for circadian analysis, given the need for recovery from surgical damage, habituation to the measurement environment and stabilization of the blood concentration of luciferin.Detailed information on measurement procedures is as follows. Black mice can be used if body hair around the target organs is depilated but are not suitable for long-term monitoring of bioluminescence because body hair regrows in a week. Luciferin (https://www.circadian.org/softwar.html).Mice were separated and kept singly in a dark box (349[W] × 468[D] × 368[H] mm) specified for photon counting. The back wall of the box was doubled, and each layer had a ventilation hatch. The hatches were localized away from each other to avoid the entry of outside light. To further reduce background noises to detect ultraweak photon emission, a third ventilation hatch was installed at the end of a hairpin-curved air duct. Continuous real-time photon counting was performed with a counting unit . Peak times of bioluminescence were calculated by cosine fitting and plotted as a function of time. To assess the presence or absence of significant circadian rhythmicity in bioluminescence data, we performed a cosine-fitting procedure and calculated the index of goodness of fit, with smaller index values reflecting a better-fit data set. To calculate this index, expression data were analyzed with the software Acro. P-values of < 0.05 were considered statistically significant. To calculate the average circadian period length based on several-day bioluminescence data, cosinor analysis was performed using the Cosinor software. Both of the Acro and Cosinor software were kindly provided by Dr. Refinetti  supplemented with 10 mM HEPES. Each tissue slice was placed onto a Millicell insert  in a 35 mm dish and cultured in DMEM  supplemented with 0.035% sodium bicarbonate, 10 mM HEPES, 4.5 g/L D-glucose, 1.0% penicillinstreptomycin and 10% FBS. After preculture for approximately 48 h, all explants were treated with 100 nM dexamethasone (Dex) for 60 min, and cultured in fresh DMEM containing 0.1 mM D-Luciferin  and different concentrations of lithium. Bioluminescence was measured at 36 °C and integrated for 1 min at intervals of 15 min using an LM2400 . For statistical analysis, data were detrended by subtracting the 24-h moving average from raw data. The value of the first peak was regarded as 100 (time = 0). Cosinor analysis was performed using the software Cosinor.luciferase gene driven by circadian promoter/enhancer elements was constructed using the ViraPower Adenoviral Gateway Expression Kit . Together with the luciferase gene, the transcription-regulatory region of the hBmal1 gene was excised from the previously constructed vector hBmal1 (−3465–+57) -pGL313 and the insert was subcloned into the pENTR-1A vector. The resulting vector hBmal1 (−1683–+57) -luc-pENTR-1A was used for recombination with the pAd/PL-DEST vector. The resulting pAd/PL-DEST vector was transfected into HEK293A cells to produce an adenovirus vector carrying hBmal1-Luc. The adenovirus was amplified and purified using the Vivapure AdenoPACK20 Kit , and 1 × 1011~12 viral particles were then infected to mice by tail vein injection. Successful luciferase expression was confirmed by in vivo imaging 24 h after infection.An adenovirus carrying the Per2Luc mice) to develop a detection system for long-term real-time monitoring of gene expression in animal organs11. Autonomous and cosine-like oscillation of clock gene expression is at the center of the molecular machinery driving the circadian clock. As the goal of the present development, we therefore aimed to obtain long-term circadian oscillation data from mice with nearly free movement. The circadian clock is cell-autonomous, and clock gene expression therefore oscillates in every cell except for a few tissues15. In vivo imaging data of the mouse’s dorsal side identified strong signals from the kidney , the expression of which was driven by the promoter/enhancer of the Bmal1 gene12. As with Per2Luc mice, clear circadian rhythms in bioluminescence were detected by real-time counting of photons emitted from the kidney of semi-freely moving Bmal1-Eluc mice over a period of several days , and the circadian period length of clock gene expression rhythms was calculated and evaluated . Together, 1 mM lithium, a plasma concentration sufficient for circadian period elongation in locomotor activity rhythms in vivo, is insufficient to lengthen the circadian period of clock gene expression rhythms in the kidney ex vivo.Before performing ted Fig. . As indiin vivo. As mentioned above, although it is known that high doses of lithium induce circadian period elongation in clock gene expression under ex vivo and in vitro culture conditions, lengthening of molecular rhythms in response to lithium have not, yet, been documented in vivo. Accordingly, being able to detect the pharmacological efficacy of lithium via modification of circadian function in vivo, particularly in peripheral clocks, is a valuable asset that can provide a better understanding of its therapeutic mechanisms. We therefore used our monitoring method to examine the in vivo effect of lithium on circadian period length of clock gene expression in the mouse kidney , clearly indicating that this effect of lithium was reversible.Circadian gene expression in the kidney was monitored in real time as bioluminescence for periods of 6–7 days before administration of lithium, 10–11 days during treatment with lithium and 6–7 days after removal of lithium Fig. . Circadiin vivo real-time monitoring of bioluminescence using mice infected with an adenovirus carrying the luciferase gene whose expression was driven by the transcription-regulatory region of the Bmal1 gene. This experimental approach enables real-time monitoring of Bmal1 expression as bioluminescence in a liver-specific manner because it is well known that an adenovirus introduced into mice by tail vein injection infects the liver. In fact, liver-specific bioluminescence signals were detected in both ventral and dorsal images by in vivo imaging , a kinase whose activity is modified by lithium24, phosphorylates circadian clock proteins and regulates their function and stability28.Circadian dysfunction is considered to play a role in the pathogenesis and progression of human bipolar disordersin vivo. In this context, a significant question is why the medium concentrations of lithium required for circadian period elongation in clock gene expression rhythms in vitro and ex vivo (10 mM and more) are much higher than the plasma concentrations required for period elongation in behavioral rhythms in vivo (less than 1 mM)5. Consistent with this, we found that 1 mM lithium was insufficient to lengthen the circadian period of clock gene expression rhythms in the kidney ex vivo  lengthen circadian period elongation in the suprachiasmatic nucleus (SCN) under ex vivo culture conditions30, this conclusion remain controversial5. The effect of lithium on core-clock gene expression, particularly of peripheral clocks, has not yet been investigated in vivo because, as already explained, detection of changes in period length in vivo using traditional experimental approaches is technically difficult. To overcome this technical limitation, in the present study, we developed a simple and low-cost method for real-time monitoring of clock gene expression rhythms in the kidney and liver in vivo. Using this method, we clearly found that lithium causes circadian period elongation in clock gene expression rhythms in vivo. Our data also indicate that the extended period length gradually returns to the original after removal of lithium from drinking water: in other words, the period elongation effect of lithium is reversible.The effect of lithium on clock gene expression rhythms of cultured cells and tissues is thus consistent with that on circadian rhythms in behavior: lithium lengthens both rhythms. Presently, however, it remains unproven whether lithium causes circadian period elongation in clock gene expression rhythms ivo Fig. . Given t5. Two reasons may explain this inconsistency. First, lithium-induced aftereffects in the previous study may have been stronger than those in our experiments because the former examined the effects of more than 30 days exposure to lithium compared to 10–11 days in the latter. Therefore, it would have taken longer to eliminate the aftereffects in the previous study. Interestingly, chronic exposure of mice to light and dark cycles with a period shorter or longer than 24 h induces prolonged aftereffects in circadian period length via epigenetic modifications in the SCN31. It is possible that similar biological processes may occur in the SCN of mice receiving lithium for a long period of time because lithium imposes a non-24 h circadian period on mice. Second, although brain and blood concentrations of lithium are similar32, a few reports, as described above, have indicated that the SCN clock is more sensitive to lithium than peripheral clocks30, suggesting that a longer period of time is needed to eliminate the effect of lithium on the SCN clock compared to peripheral clocks. The incomplete reversibility of the effect of lithium on circadian period length in locomotor activity observed in the previous report may be due to prolonged aftereffects on the SCN clock.Our present data showed that the effect of lithium on circadian period length in peripheral circadian gene expression was completely reversible. This is inconsistent with a previous report that showed that the effect in locomotor activity was incompletely reversible, with the period length remaining significantly longer than the original even four weeks after removal of lithiumin vivo effect of lithium on the kidney, as a representative peripheral clock, it is likely that similar lithium-induced period lengthening occurs in clock gene expression of other peripheral clocks, as well as on the SCN. However, it cannot be excluded that other organs and tissues may exhibit only minor, or negligible, period lengthening in vivo, on the basis of a report which indicated that the magnitude of the effect of lithium on clock gene expression varies among organs and tissues ex vivo5. Given that low doses of lithium do not affect circadian period in peripheral clock gene expression in vitro or ex vivo, some different mechanisms from those in vitro and ex vivo may be involved in lithium-induced period elongation in clock gene expression rhythms in vivo. As potential mechanisms, we consider two cases. First, a factor or factors which enhances lithium efficacy exists in vivo, explaining that low doses of lithium do not exert circadian period elongation activity because of the lack of the factor(s) under culture conditions. Second, lithium lengthens circadian period in vivo not via cell-autonomous regulation, but rather by unknown systemic regulation.Although in this study we only focused on the Biochemical or histochemical measurement of gene expression in animal organs with high time resolution over periods of a few weeks is markedly difficult, and we are unaware of any method that enables this, particularly in a simple and low-cost manner. Although some optical monitoring approaches using transgenic mice carrying a reporter gene are therefore required, existing measurement systems are currently expensive and complicated to implement. In the present study, we developed a method for continuous real-time measurement of gene expression in semi-freely moving mice over periods of up to six weeks, which involves the simple attachment of a small PMT tightly onto the body surface. Advantages of this system include the simplicity of manipulation, the low invasiveness, the lack of requirement of continuous anesthesia, and the automated nature of measurement. Further optimization is required to increase the success rate of long-term measurement. Our detection unit is small and inexpensive and therefore expandable to a high-throughput platform. The present study focused on clock gene expression, but the method is applicable to real-time detection of the expression of a wide range of genes.The PMT method overcomes weak points in the measurement systems reported previously, but still has some limitations. First, the PMT method has a disadvantage in tissue-specific monitoring of gene expression. However, the use of tissue-specific promoters to express a reporter gene enables tissue-specific monitoring with the PMT method in a more precise manner, and the use of bright luminescent reporter proteins enables the measurement of photons from deep-lying organs. Second, although not detected in this study, continuous luciferin administration may cause unexpected secondary effects on other biological processes. Third, although a strongpoint of our PMT method is that it enables the long-term overview of gene expression in a highly time-resolved manner with only small number of mice and without killing them, the data obtained show only relative changes in expression levels. While this limitation did not affect circadian phase and period evaluation in the present study, the drawing of any firm conclusions on quantitative changes such as circadian amplitude may require traditional quantitative biochemical analyses.Further development of our detection system to maturity will aid a wide range of research fields in medicine and biology. In addition to mechanical improvements, development might also extend to enhancing bioluminescence intensity and the use of tissue-specific promoters, which would help expand the application of the system. Among examples, our system may help investigate developmental stages in which a target gene is expressed without killing a large number of pregnant mice. This simple long-term monitoring method will also be powerful in examining the chronic effect of environmental stimuli on expression of a target gene in organs. In particular, our method may greatly aid in the development of new medicines. When performed with traditional methods, long-term monitoring of the impact of drug treatment on target gene expression with high time resolution requires vast numbers of mice. However, our method offers marked advantages in throughput performance and would therefore be useful even for large-scale experiments, such as the screening of drugs that affect target gene expression. Finally, our method may accelerate research on circadian rhythms. While biochemical examination of circadian gene expression in organs around the clock is a standard experiment in this field, long-term overnight organ sampling imposes a severe burden on researchers. Our method will help researchers deal with this issue.Figure S1


Healthcare-associated infections (HAIs) are a major health concern and have substantial effects on morbidity and mortality and increase healthcare costs. We investigated the effect of a hospital-wide program for the prevention of HAIs on additional length of stay (LOS).We analyzed data from a prospective, single-center, quasi-experimental study with two surveillance periods before and after implementation of an infection prevention intervention program. HAI diagnosis was made according to surveillance definition criteria established by the US Centers for Disease Control and Prevention. A multistate model was used to estimate additional LOS for patients with HAI in both surveillance periods.During the first and second periods, 1,568 and 2,336 HAIs were identified among 26,943 and 35,211 patients, respectively. For HAI patients exclusively treated in a general ward, additional LOS was 8.4  days in the first period and 9.6 (95% CI: 8.3–11.0) days in the second period (p = 0.26). For HAI patients treated in both an intensive care unit (ICU) and a general ward, additional LOS was 8.1 (95% CI: 6.3–9.9) days in the first period to 7.3 (95% CI: 6.1–8.5) days in the second period (p = 0.47).Healthcare-associated infections prolong LOS. A hospital-wide infection control program did not alter the prolongation of LOS. Healthcare-associated infections (HAIs) are a major health concern and have substantial effects on morbidity and mortality. HAIs prBecause the cost of HAIs is strongly dependent on patients’ LOS, many studies have estimated additional LOS due to HAIs. Additional hospital stay must be calculated to assess how many bed-days might be gained from prevention measures. GeneralTherefore, we hypothesised that the extra length of stay associated with HAIs can be changed with the help of an infection prevention intervention program. We aimed to assess the impact of HAIs on LOS using multistate models in the surveillance period 1 and the surveillance periods 2 of a hospital-wide infection control program at Jena University Hospital.We analyzed data on HAIs from the ALERTS study , a prospThe multifaceted intervention program for infection control in the ALERTS study involved the hospital-wide promotion of hand hygiene and the implementation of prevention bundles for specific HAIs . This inA multistate model was used to estimate additional LOS due to HAIs considering HAIs as a potential intermediate state between admission and discharge or death to address the time-dependent bias.Clostridioides difficile-associated infection (CDI), other infections, and multiple infections (grouped under the category “Multiple”). Model approach 2 estimated the prolongation of LOS associated with HAIs stratified by department because the costs of bed-days largely differ across departments. Moreover, from the health economic perspective is important to know the extra LOS explicitly for patients stayed in ICU, because the daily costs of intensive care are higher than the treatment costs in general wards. Therefore, we estimated additional LOS separately for patients treated in general wards  and for patients treated in both a general ward and an ICU. The department or unit that discharged the patient from the hospital was considered as the main ward.In this model, all patients were regarded as “non-exposed patient” as long as they did not acquire an HAI during their stay in the hospital. If a patient acquired more than one HAI, the time of the occurrence of the first HAI was used in the multistate model. The prolongation of LOS was derived as a function of the hazard of transition in the next short time between the health states, 5, 13. In this study, we used multistate models that describe the daily risk of transition between multiple health states. The expected LOS associated with HAI (in days) was computed by a function of these transition probabilities. The Aal02 and P12] and for death [P03 and P13] for different landmark time points (s) based on a range of landmarks s in the first surveillance period  ≥ P12, meaning that the probabilities of discharged alive for patients without HAI are in different range of landmarks higher than for patients with HAI. The effect was most pronounced for early landmarks. Conversely, this analysis for death [P03 and P13] for the same range of landmarks in the first period  ≥ P03, meaning that the probabilities of death for patient with HAI are higher than for patient without HAI in the same range of landmarks. The results were very similar in the second surveillance period , the additional LOS due to HAIs was clearly lower than the estimated additional LOS associated with HAIs in model approach 1 when the inpatient units were ignored in analyses. The results also showed that prolongation of LOS due to multiple HAIs was 2 times greater than that due to SSI and 7 times greater than that due to UTI during the first surveillance period, and similar results were found in the second surveillance period.However, the incidence of HAIs in the second surveillance period did not differ significantly after the intervention prevention program. Only a reduction in severe HAIs were observed in the second surveillance period in the ICUs. FurtherA landmark method was applied to illustrate the prolonging effect of HAI for exposed patients in comparison with non-exposed patients. The results showed that the probabilities of discharged alive for patients with HAI were lower than patients without HAI at the same landmark. Furthermore, the probability of death for exposed patients is higher compared to non-exposed patients. Differences were higher at earlier landmark, meaning that the impact of occurrence of HAI in early days after admission on competing events  is much higher.A systematic review of published literature concluded that additional LOS due to HAIs in studies using time-fixed methods was 9.4 days on average longer than that in studies using multistate models. TherefoThis study has several limitations. We were unable to conduct an economic analysis because the infection control program did not reduce the incidence rate of HAIs. Our study analyzed data from a single-center study in Germany; thus, the results may not be generalizable to other settings. In addition, our data does not allow for an adjustment according to sex, age or comorbidities because of low number of patients with HAI in some categories.The role of hand hygiene as the most important factor in the prevention of HAIs is accepted, and several studies have reported that the rate of adherence to hand hygiene can increase significantly with a multifaceted intervention, which can reduce the risk of HAIs; howeverIn conclusion, healthcare-associated infections prolong LOS and can therefore increase the hazard of hospital mortality and costs of care. Our analyses demonstrated that additional LOS varied across HAI sites. This information can help health policy makers determine which hospital infection prevention measures to invest in to improve health and save costs.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


Background: Weak or inconsistent hand preference has been postulated to be a risk factor for developmental language delay. Following on from our Registered Stage 1 report this study assessed the extent to which variations in language skills are associated with the strength of hand preference.Methods: Data are drawn from a large sample (N = 569) of 6- to 7-year-old children unselected for ability, assessed at two time points, 6 months apart. Hand preference was assessed using the Quantitative Hand Preference (QHP) task and five uni-manual motor tasks. Language skills  were assessed with standardized measures.Results: We found QHP scores did not distinguish children with weaker language skills from those with stronger language skills and the correlation between QHP scores and language ability was negligible in this study. Hand preference on the QHP task was significantly stronger among right-handed than left-handed children and left-handed children were typically inconsistent in the hand used across different tasks. Conclusions: The findings presented here fail to provide any support for the theory that weak cerebral lateralisation (as assessed here by the QHP task) places children at risk of language difficulties. Stage 1 report: https://doi.org/10.12688/wellcomeopenres.15077.1 Humans typically show cerebral lateralisation for language. For the majority of the population, language processing appears to depend predominantly on left hemisphere systems. Much of the early evidence for cerebral lateralisation came from studies of adult stroke patients which showed that damage to the left hemisphere is more commonly associated with language deficits than damage to the right hemisphere . Patterng = −0.03,p = 0.22). A follow up analysis of studies that included only children reported a very small effect favouring right-handed children (Hedges’g = −0.09) though this effect was reduced to nonsignificant levels after excluding two studies with disproportionately large sample sizes (Hedges’g = −0.06). It seems clear from the Somerset al. meta-analyses that any association between hand preference  and language ability in the general population is trivial in size, irrespective of age.Studies of cerebral lateralisation in adults lead naturally to ideas about possible links between the development of cerebral lateralisation and language skills in children. If the development of cerebral lateralisation is critical for the development of language, handedness  might be expected to relate to developmental language difficulties  is a risk factor for language difficulties, it should be possible to detect such effects in large representative samples of children. Within such samples, children with the weakest language skills should be expected to show evidence of less clearly defined hand preference on the QHP task. In the current study we use observations of hand preference and the QHP task with a large unselected sample of children seen on two occasions. Analyses were performed treating language as a continuously distributed trait, as well as examining whether measures of handedness differ between children with particularly poor language skills versus controls.The Australian Catholic University Human Research Ethics Committee provided ethical approval (2015-269H). Informed written consent was sought from the principals of the schools involved for all children enrolled in their first year of school (Preparatory Year) in January 2016. An opt-out procedure was followed. Parent information leaflets and opt-out consent forms were distributed to the parents of enrolled children (via both electronic and written hard copy format for each participant). All children in each class participated in the study unless parents signed the opt-out consent form for their child before the study’s commencement date.SD of the mean) is 900 to 1100). The three remaining schools have higher ICSEA values (1112–1153) reflecting a student population with slightly above average levels of social advantage. Children were assessed at two time points: Within the final half of Year 1 , and again approximately 6 months later during the first half of Year 2 .A total of 569 children from 11 schools in Brisbane, Australia participated as part of a larger longitudinal study  task.Unimanual hand preference motor tasks. Measures of the hand used while children completed the following 5 motor tasks at both time 4 and time 5 were recorded. (1)Lace Threading  TheApples Selection task (n = 30) printed on a page within a 60 second time limit while ignoring white distractor apples and red distractor strawberries. 4) TheTwo-Match Shifting task . A series of six practice items were provided first in which children were corrected if they gave an incorrect response. The child’s score was the total number of correct answers for both inflected and derived words.Stata (v 15.1R (v3.5.2). Our data are accessible on OSF . We used independent sample t-tests to assess whether children with language difficulties  make a lower number of reaches with the preferred hand than children without language difficulties (the rest of the sample). We initially performed these tests separately for the time 4 and time 5 data and separately for left- versus right-handers (four separate independent samples t-tests). Because the patterns for left- and right-handers looked comparable we combined the two samples at each time point to give tests of greater statistical power.3.In the population as a whole, do variations in language skills correlate with strength of hand preference on the QHP task? We assessed whether there is a relationship between language factor scores (treated as a continuous variable) and the number of reaches with the preferred hand on the QHP task. We performed linear regression analyses 1. For right-handers; 2. For left-handers and (assuming the relationships seem similar for the two samples); 3. For the combined sample. In all of these regression models we examined the adequacy of a linear model and checked for any undue influence of outliers.4.t-tests comparing the mean number of reaches with the preferred hand 1. For the right-handed sample, 2. For the left-handed sample; 3. For the combined sampleDoes the strength of hand preference (number of reaches with the preferred hand) increase with age (is it higher at time 5 than time 4?). Any such increase could be taken as evidence of maturation or alternatively merely evidence of a practice effect. For children in the sample who were tested at both time points we computed 3 paired-samples5.Is the strength of hand preference (the number of reaches with the preferred hand) equivalent in right- and left-handers . We computed 2 independent samples t-tests comparing the proportion of reaches on the QHP task with the preferred hand in right- versus left- handers at both times of measurement (time 4 and time 5).6.Do QHP scores differ for right-handed children with a consistent versus inconsistent hand preference as found byThe dataset is a secondary registration of a pre-existing dataset. Data was collected across two time points separated by a six month interval: Time 4 in the last half of children’s second school year ; Time 5 in the first half of their third school year .N = 569 at time 1). At both time points considered here (time 4 and time 5) there were some missing data. Seventy children had missing data on all measures of handedness at both time points , leaving a total initial study sample ofn = 499. Missing data reflected absence from school, insufficient testing space to allow for the set-up of the QHP task or unfavourable environmental conditions if testing was conducted outside. Comparisons between the children with missing data on all handedness measures and those with data present at time 4 and time 5 showed that they did not differ from the complete sample in gender  or age at time 1,t = -1.34,p = .19,d = .16)We present analyses on a subset of data collected in a large scale longitudinal study .Analyses examining the test-retest reliability of the QHP task were restricted to those children who had handedness recorded on the Drawing Trails task at time 4 and time 5 and a QHP score recorded at both time points. For analyses of relationships between hand preference on the QHP task and language ability, factor scores for language at time 4 and at time 5 were created by running a Confirmatory Factor Analysis (CFA) in Stata 15 ; missingp =.27). The results of the analyses that we report below using pairwise deletion therefore should not be biased by patterns of missing data.The critical variables entered into the following analyses are: time 4 Language Factor Scores, time 5 Language Factor Scores, time 4 QHP scores and time 5 QHP scores. We assessed whether patterns of missing data for these variables could be considered to be missing completely at random (MCAR) and they could . At time 4, of the 442 children completing the Drawing Trails task, 46 (10%) were classified as left-handed and 396 (90%) as right-handed. Of the 426 children completing the Drawing Trails task at time 5, 61 (14%) were classified as left-handed and 365 (86%) as right-handed.Data were originally coded so that each reach with the right hand was scored as 1 and each reach with the left hand was scored as -1. For the purpose of analysis, data were recoded so that for each trial a reach with the preferred hand was coded as 1, and a reach with the non-preferred hand was coded as zero.summed across spatial positions. A score of 11 or above indicates that a child predominantly used their preferred hand, irrespective of handedness (i.e. left or right). The distribution of reaching scores is shown inTo obtain a score of the strength of hand preference for each child, the number of reaches made with the preferred hand wasThe histograms reveal that the distribution of preference scores for right-handers is bimodal; the numbers of left-handers are too small for distributional analysis, but the impression is of far less bias to the preferred hand.SD = 4.21; Range, 2 – 21) and for the right handed sample 13.87 . At time 5, for left handed children the mean QHP score was 9.93  and 14.23 for right handed children .The mean QHP score at time 4 for the left handed sample was 11.34 :r = 0.46,rho = 0.55; combined sample (n = 353):r = 0.36,rho = 0.39).Analyses examining the test-retest reliability of the QHP task were restricted to those children who had handedness recorded on the Drawing Trails task at time 4 and time 5 and a QHP score recorded at both time points. The QHP scores had low test-retest reliability . The factor scores were created by running a 1-factor CFA in Stata 15  and savit-tests were performed separately for right- versus left-handers at time 4 and time 5 . The results of these analyses are shown inTo determine whether children with language difficulties show weaker hand preference on the QHP task than children without language difficulties, independent sampleA series of linear regression analyses were conducted to assess the relationship between language factor scores treated as a continuous variable and the number of reaches with the preferred hand on the QHP. These were completed for the left- and right-handed samples at time 4 and time 5, as well as for the combined sample at both time points.The scatter plots for the right- and left-handed samples separately at each time point are shown int-tests to compare the number of reaches with the preferred hand at time 4 with the number at time 5. These analyses were restricted to those children who had the same preferred hand on the Drawing Trails task at both time 4 and time 5. There were 39 consistently left-handed children with QHP scores at both time points, and 298 consistently right-handed children with QHP scores at both time points .To test for any developmental effects on the QHP task, we computed three paired sampleFor both the right- and left-handed samples, the QHP scores were largely comparable at both time points, although there was a nonsignificant trend for the degree of hand preference on the QHP task to decline between time 4 and time 5 see. Effect t-tests. These analyses compared the number of reaches on the QHP task with the preferred hand in left versus right handers at both times of measurement . If children used the same hand for all five tasks at a single time point they were defined as consistent hand users at that time point. At time 4, all left handed children (n = 44) were defined as inconsistent left handers; of the right-handed children, 243 (61%) were defined as consistent right-handers and 153 (39%) as inconsistent right handers. At time 5, all left handed children (n = 61) were defined as inconsistent left handers; 321 (88%) right-handed children were defined as consistent right-handers and 44 (12 %) as inconsistent right-handers.Consistency of hand preference was defined by hand-use during five tasks at time 4 and time 5  might be considered a lower bound estimate of the reliability of the QHP task, since it is likely that with a shorter inter-test interval that correlation would have been higher. Nevertheless, the QHP task appears to have at best moderate to low reliability. The test-test retest reliability of the QHP can be compared to that for our language factor which shows a test-retest correlation ofr = 0.82 between time 4 and time 5 in this study. If the QHP task is to be used in future studies with children, it is recommended that more trials are used at each spatial position, as this might improve test-retest reliability.Does hand preference on the QHP task discriminate between children with and without language difficulties? We found no evidence that children with the weakest language skills show weaker hand preference on the QHP task than children with better language skills.Does hand preference on the QHP task correlate with individual differences in language ability in the population as a whole? We found no evidence that hand preference on the QHP task was related to language ability. All correlations between QHP scores and language factor scores were trivial in size.Does strength of hand preference on the QHP task increase with age? We found no support for this suggestion in the 6 month period assessed here (81 months to 87 months of age).Is the strength of hand preference on the QHP task higher in right- than left-handed children? Our one clear positive finding was that hand preference on the QHP task was significantly stronger among right-handed than left-handed children at both time points, with medium to large effect sizes. This pattern arguably fits with the finding reported above that left-handed children (those who used their left hand to hold the pen while performing the Drawing Trails task) frequently used their right hand for other uni-manual tasks .Is the strength of hand preference on the QHP task higher in children with consistent hand preference on other tasks? We found no evidence for stronger hand preference on the QHP task in children who showed consistent hand preference across our 5 measures of hand use .In summary, our major conclusion from these analyses is a negative one. Scores on the QHP task do not differ between children with weaker language skills and those with stronger language skills. Similarly, in the sample as a whole the correlation between language ability and QHP scores is negligible in size.The theoretical issue addressed here is whether differences in cerebral lateralisation are associated with variations in language development. It has been suggested that problems in establishing cerebral lateralisation for language may be causally related to language difficulties. Patterns of cerebral lateralisation for language are associated with measures of hand preference such that most adults with left-hemisphere dominance for language also show greater dexterity with the right hand (g = −0.09) on a composite measure of verbal ability, which was reduced to nonsignificant levels after excluding two studies with disproportionately large sample sizes (Hedges’g = −0.06). Though this was not the focus here, we can report that there was no difference in the language factor scores between right- and left-handed children at time 4  or at time 5.Possible links between hand preference  and language skill in children have been investigated in two ways: By comparing language skills in children who are right- or left-handed, and by comparing language skills in children who show inconsistent hand preferences . In relation to differences in language skill between right- and left-handers, a meta-analysis byThe focus of the current study was whether children who show weak or inconsistent hand preference are at risk for developmental language disorder (DLD). We assessed this hypothesis using the QHP task developed by Bishop and colleagues (r = 0.82) arguably the QHP task has fairly poor reliability (test-retestr = 0.36 for the whole sample). However, limitations of the reliability of the QHP task are unlikely to be the sole reason for its lack of relationship with language skills, since QHP scores showed strong differences between right and left handed children.One possible (uninteresting) explanation for this null result could be limitations in task reliability. Although the composite measure of language skill used here had good reliability . We believe given the large, representative sample studied here this finding is important. If researchers want to establish links between the consistency of hand preference and language ability, it seems other tasks would need to be developed. For the time being, the evidence presented here fails to provide any support for the theory that problems in language development are related to problems in establishing cerebral lateralisation for language and manual control.The protocol proposed in the Stage 1 report and reported here is a secondary analysis of an existing dataset. CH, VP, SM, MH, and KB had prior access to the data that was used as part of this study. No dissemination of the dataset or of any works relating to the dataset preceded this analysis plan.The statistical analysis plan proposed was developed in collaboration with DB. DB was blinded to the data during the development of the analysis plan.https://doi.org/10.17605/OSF.IO/PBYW5 Dataset: The relationship between handedness and language ability in children.csv (this file includes the dataset used in the study)Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the Authors carefully addressed missing data.Authors cautioned results appropriately considering the low reliability estimate for the QHP.Visual presentation of data  were helpful for understanding the pattern of the reaches tasks. The authors stated that a series of linear regression analyses were conducted to assess the relationship between the language factor score and the QHP, but no regression results are reported other than the scatter plots and correlation analyses in Figures 4 and 5. I suggest the authors present those regression results.This is a well-constructed research report with clear presentation of the problem, the method, and the analyses. Strengths include: Ultimately, the authors report a null finding which that challenges the theory of a relationship between cerebral lateralization and language development. However, the caveat in this case may be the low reliability of the QHP measure which could make interpretation of the results less reliable. The authors clearly acknowledge this limitation and present data and results fairly, responsibly, and as described in Stage 1.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This study is a well powered exploration of the theoretical association between handedness and language proficiency in a large cohort of healthy children, aged 6-7 years. Data analyses and interpretation have been conducted as planned and well executed.The low retest reliability of the QHP: how does this compare with other handedness inventories?p.8 - would the conclusion about no relationship between hand preference and language ability change if a more stringent criterion of <1.5SD or <2SD had been adopted ?p.11 - is there any information available on how many left-handed children had been instructed to use the right hand, and could this account for the changes in QHP scores over time (Table 4)?p.12 - is there any evidence for the link between inconsistent hand preference and weak language lateralization?p.13 - what improvements in test design should be considered for future studies, apart from item repetition? Minor additional points for the discussion could be:I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


A complementary metal-oxide-semiconductor (CMOS) chip biosensor was developed for cell viability monitoring based on an array of capacitance sensors utilizing a ring oscillator. The chip was packaged in a low temperature co-fired ceramic (LTCC) module with a flip chip bonding technique. A microcontroller operates the chip, while the whole measurement system was controlled by PC. The developed biosensor was applied for measurement of the proliferation stage of adherent cells where the sensor response depends on the ratio between healthy, viable and multiplying cells, which adhere onto the chip surface, and necrotic or apoptotic cells, which detach from the chip surface. This change in cellular adhesion caused a change in the effective permittivity in the vicinity of the sensor element, which was sensed as a change in oscillation frequency of the ring oscillator. The sensor was tested with human lung epithelial cells (BEAS-2B) during cell addition, proliferation and migration, and finally detachment induced by trypsin protease treatment. The difference in sensor response with and without cells was measured as a frequency shift in the scale of 1.1 MHz from the base frequency of 57.2 MHz. Moreover, the number of cells in the sensor vicinity was directly proportional to the frequency shift. Health effect evaluation of chemicals and medicines begins with cytotoxicity assays including cell cultures which are followed by animal testing. Proliferation of cells is evaluated after exposure to the assessed substance and usually includes laborious handwork required in the staining and fixing of cells for visual inspection under the microscope. Moreover, this is an end-point measurement method, which lacks real-time information on the health status of the cell population and is vulnerable to various sources of human error. In addition, marker-based cell studies require handling of potentially harmful chemicals. Animal tests are expensive, depend on specific facilities and staff, and require careful ethical considerations. Thus, there is a true demand for a cost-effective, real-time, label-free cell viability evaluation method with a high degree of automation, which highlights the need for intensive development in this technology .2) which aggravate proper shielding of electrical contacts  from moisture and the corrosive environment that is typical in biological systems. Due to the small size of the chip and limited possibilities in bonding, manufacturing of necessary microfluidic channels is also challenging [A lab-on-a-chip (LOC) is a device which combines sampling, analyzing, and data processing on a single miniaturized platform, which makes it cost-efficient and appealing for the development of automatic biological measurements. Complementary metal-oxide-semiconductor (CMOS) technology, which was invented more than 50 years ago, has enabled more sensing, processing and computing capacity in various electrical devices. Integration of CMOS technology with the lab-on-a-chip concept forms a special variety of LOCs  where the actual CMOS chip is utilized in both sensing and data processing, and enables complete electronic, label-free cell assays. Several CMOS-based devices for cell sensing have been presented  that havllenging . FinallyLow temperature co-fired ceramic (LTCC) technology has been applied for demanding sensor packages  and biosIn this article, we present an LTCC packaged CMOS biosensor chip, which is utilized in measuring the proliferation of a cell population. The sensor is based on capacitive sensing with a three-stage ring oscillator that generates an oscillatory signal which is modulated by an interdigitated electrode (IDE) coupled in parallel to the second-stage. The CMOS biosensor chip consists of a 4 × 4 array of these sensor elements. When adherent cells attach and proliferate  on the IDE area the effective permittivity of the sensor surroundings decreases because cells have lower capacitance compared to the cell media and the oscillation frequency of the sensor elements increases . The chaIt is crucial that the electrical contact areas are not exposed to biological cell growth media, since the electric potential in the conductors in the high humidity and temperature environment would cause corrosion. LTCC technology was combined with flip-chip bonding to obtain a highly reliable and stable packaging system. Conductive and non-conductive epoxies were used to make a robust barrier between the electrical connections and the active sensor area of the chip. A similar packaging strategy has been reported with PCB and alumina packages ,23. In tThe sensor package in this work was made with a commercial DuPont LTCC material system consisting of LTCC tape (951) and gold (5742) or silver (6142D) paste. Tapes and vias were cut using a laser cutting system (LPKF ProtoLaser U3).The CMOS chip was manufactured in a commercial foundry (X-FAB) using a 2-poly, 3-metal, 0.35 μm standard CMOS process. Flip-chip bonding was performed using a Finetech Electronics FINEPLACER Pico 145. The chip was attached to the LTCC substrate with Epo-Tek epoxies. Electrical connections were made with H20E-PFC silver epoxy adhesive and underfilled with EPO-TEK 377 insulating epoxy. Both epoxies were cured at 150 °C for 1 h.2 feed of 50 sccm.The cell cultivation chamber was made from a glass or polypropylene tube (diameter 30 mm) and attached onto the LTCC module with either Casco Superfix silicone or EPO-TEK 377 epoxy. Finally, the outside of the package was passivated with Kontakt Chemie Urethane 71 spray. Oxygen plasma treatment of the sensor chip was performed with Oxford Instruments Plasmalab 80 Plus at room temperature using 30 mT chamber pressure, RF power of 100 W and O2. Cells were then detached with 0.5% trypsin-EDTA solution in PBS (pH 7.4) and the cell concentration was measured with a Countess II cell counter (Thermo Fisher Scientific). A baseline measurement for the CMOS chip was obtained with serum-free DMEM for 30 min before the addition of 10,000 cells re-suspended in the same media. After a 24 h recording with the sensor chip, cells were stained with Hoechst 33342 DNA stain and imaged using an Olympus BX-51 fluorescence microscope with a 60× LUMPlan/IR immersion objective and CellM software with appropriate filter sets. Cells were then grown in the presence of 0.5% trypsin-EDTA in serum-free DMEM for an additional 24 h to detach the cells. Staining with α-tubulin was performed on cells grown 24 to 25 h on CMOS chips treated (or not) with 0.01% poly-L-lysine (Sigma-Aldrich) and fixed with 4% paraformaldehyde in PBS (pH 7.4). Cells were then blocked with 1% BSA in PBS (pH 7.4) supplemented with 0.05% saponin. Fixed cells were stained with anti- α-tubulin antibody (Sigma Aldrich) and Alexa 488 secondary antibodies (Thermo Fisher Scientific). Cells were imaged with a Zeiss Observer. Z1 microscope equipped with an LSM 700 confocal unit, Zen2009 software , 63× Plan-Apo oil-immersion or 20× Plan-Apo objective and appropriate filter sets for each dye.The human lung epithelial cell line BEAS-2B  was used for the experiments. The cells were grown in Dulbecco’s Modified Eagle Medium (DMEM), and supplemented with 10% fetal bovine serum (FBS). Cells were washed once with phosphate-buffered saline (PBS), pH 7.4 (Thermo Fisher Scientific). Cells were grown on 10 cm culture plates in a cell incubator at 37 °C and 5% COCleaning of the sensor module after the experiments was done by using Tergazyme enzyme detergent (Alconox Inc.) and 70% ethanol.2 in size and fabricated in the CMOS process’ top metal layer. They are thus sensitive to capacitive loading caused by changes in the dielectric permittivity of the area above the surface of the chip. The output frequency of each sensor is digitized by running an on-chip 32-bit counter using the sensor signal as the clock. The counter is active for a fixed duration of time and so the final count value can be used to compute the original sensor frequency. The sensors’ transistors are separated from the chip surface by several layers of insulation and a final passivation layer, and so are shielded from the contact with cell media.The CMOS sensor chip and one of the 16 interdigitated input electrodes (inset) are shown in The capacitance sensors are arranged in a 4 × 4-pixel array  and all the pixels are read out sequentially. Two similar reference sensors are located outside the cell cultivation area of the chip enabling per-pixel calibration and canceling out the temperature effects. A microcontroller (MicroPython PYBv1.1) drives the chip and communicates with it using an I2C communication protocol bus. An I2C bus was chosen due to its simple protocol implementation, and because it only requires two electrical wire connections. The chip is able to sense capacitance changes in the aF range and is immune to saturation of the output signal due to the frequency sensing mechanism. A more detailed description of chip function can be found in references ,27,28.The MATLAB user interface shows real time plots of all 16 sensors and 2 reference sensors. Moreover, it shows sequential raw data from the chip. All sensor pixels can be monitored individually.The acquired data is post-processed by using a filtering algorithm and the reference pixel data. The chip has an internal problem due to the performance of the I2C bus drivers which occasionally result in erroneous repetition of data from two sequential pixels. In such a case, this can be fixed by a cleaning algorithm that identifies and removes repeated values. The noise related to temperature changes and noise from the environment are adjusted by setting thresholds and the signal is also filtered with a 14-point percentile filter using the Origin software platform . In the During their life cycle, adherent cells such as human lung fibroblasts , adhere to growth surfaces, which also facilitates their movement. The cells divide mitotically and finally perish through programmed cell death (apoptosis) or necrosis. Viable, attached fibroblast cells have a flat, star-like morphology, whereas non-viable cells contract and finally detach from the growth surface . AttacheThe change in frequency with/without cells is up to 1.5 MHz from baseline frequency of 57.2 MHz. This change is relative to the unloaded reference sensor. All pixels can be measured individually, which allows for the mapping of location of cells. Moreover, it is possible to estimate the number of cells on individual IDEs.Lamination and sintering were performed according to the DuPont datasheet. An earlier presented packaging method  was modi2 feed of 50 SCCM.Cell adherence on top of the pristine, unpackaged CMOS sensor chip was studied and the cell adhesion was found to be insufficient. This adhesion problem on common silicon surfaces is known and reported earlier in the literature ,32,33,34The chip surface was first treated with lysine (poly-L-lysine) to improve the cell adhesion. Lysine makes the surface more charged, enhancing cell attachment. The effect of oxygen plasma surface treatment on cell adhesion was also studied. The plasma treatment increased cell adhesion by transforming the chip surface to be more hydrophilic  and thusPlasma treatment was the method chosen to enhance cell adhesion, because it does not leave excess material on the chip surface that can affect the measurements. Despite the fact that it removes contaminants and changes the surface hydrophilicity, it does not increase load capacitance.The effect of epoxy underfill on the cell adhesion was also explored to confirm that it does not interfere with the measurement results. The cells clearly adhere on top of the epoxy layer (EPO-TEK 377), and this was enhanced by oxygen plasma treatment . It is cHuman lung epithelial cell adhesion and proliferation were monitored using the capacitance sensor system. After 25 h of incubation and proliferation , 0.5% trypsin protease solution was added to detach the cells from the chip surface. In addition, as a baseline control, the data from a cell media loaded chip was recorded separately for 25 h to demonstrate that there is a clear difference in sensor response between empty and cell-loaded chips.r = 6.2, for cytoplasm medium εr = 60, for nucleus εr = 28, for envelope εr = 52 and for the cell growth medium εr = 80, respectively [The sensor response from four pixels (9–12) with about 100,000 seeded cells in a cell vial (diameter 30 mm), and corresponding fluorescence microscope images are shown in ectively . These dIn summary, the data shows a clear response when cells are added, as they grow on the surface and finally detach after the addition of trypsin. This verifies that the capacitance (dielectric permittivity) is lower compared to the surrounding media when cells have attached onto the finger structures of the sensors.In the field of cytotoxicity assessment, there is a real need for new biosensors, which are label-free but above all provide real-time data about the proliferation and other cell activities of the cell cultivations. Microelectronics offers many possibilities for biosensor applications, but the bottleneck is to combine electronics with the culture conditions of adherent mammalian cells  and to find combinations of materials that are biocompatible and reliable for electronics. In the presented approach we have implemented a CMOS chip with a capacitance sensing ring oscillator packaged in an LTCC module, which provides solutions for the challenges described above. Our preliminary results are promising, but the sensor requires long-term testing in order to evaluate the stability and reproducibility of the system. More experiments are needed to fully explain why the sensor signal is not fully reversible i.e. not fully recovering to the same level as before adding the cells. For example, The frequency change varies between 0 and 1.1 MHz depending on the number of cells on an individual sensor pixel. When scaled to a capacitance value, this corresponds to a range of 0–1.85 fF, which is well above the measured resolution of 17.5 aF , correspThere are residual particles left on the chip after experiments as clearly shown in Packaging made of LTCC offers many advantages because the ease to shield the bond pads/electrical connections under the packaging material and there is no need for wire bonding. Typically wire bonds are shielded with polymers and it is difficult to avoid contamination of the active area of the chip, needing to turn the chip upside down and put for example PDMS “plug” to prevent contamination . Also, tTo make full use of the advantages of LTCC packaging technology, fluidic channels can be integrated to the LTCC module making it possible to carry out flow through cell cultivation. This makes it possible to perform experiments without an incubator. However, this kind of system would also need environmental temperature control of the package. This would be possible with integrated heaters built into the package. The biggest drawback is that the fluidic channel made of for example PDMS is transparent and LTCC is opaque needing a window to enable microscopy. However, successful work has been performed to integrate a transparent window made of glass to the LTCC structure ,38.Another drawback is that it is difficult to achieve leveled surface between the chip and the substrate when using flip chip bonding, however this can be compensated by design of the fluidic channels so that the liquid flow is suited to the application.This work has examined and produced a biosensor applied to adherent cell viability monitoring. The biosensor is based on capacitive sensing using a ring oscillator on a CMOS chip packaged in an LTCC module. Viable adherent cells attach to the sensor chip surface, while compromised cells or protease-treated cells detach from the surface. This causes a change in effective dielectric permittivity at the sensor electrodes, resulting in a change in the capacitance monitored as a shift in oscillation frequency of the ring oscillator. The sensor surface was cleaned by oxygen plasma and treated with poly-L-lysine to improve cell attachment. Oxygen plasma was observed to be an optimal surface treatment as it does not introduce residual proteins onto the chip surface. Cell adherence on the CMOS chip was tested with human lung epithelial cells (BEAS2B). The sensor showed response to cell addition, proliferation, and other cell activity, and cell detachment induced by trypsin. The sensor response was consistent with the numbers of cells and their location on the sensor elements was verified by fluorescence and confocal microscopy. The frequency difference compared to reference electrodes varied up to 1.1 MHz, depending on the number of cells on an individual sensor pixel.