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Introduction: The Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) is part of the undergraduate dental curriculum. Online teaching has nowadays become common also in dentistry. Objective: To compare undergraduate students’ self-assessed ability and satisfaction with learning DC/TMD Axis I between traditional and online learning and to evaluate the possible gains of online teaching. Material and Methods: Third-year undergraduate dental students in 2018 (traditional learning, Group 1, n = 43/50) and in 2019 (online learning, Group 2, n = 34/50) at the University of Oulu, Finland evaluated their self-assessed ability and satisfaction with learning DC/TMD clinical examination and diagnostics on a 10-point scale. Additionally, those participating in online courses answered to two open-ended questions; Group 2 (n = 50) and another group from the University of Eastern Finland in 2019 and 2020 (n = 75, Group 3). Total of 105/125 students (84%) responded. Content analysis was used to open-ended responses. Results: The online course reported significantly higher self-assessed ability in measurements (p = 0.004), identifying referred pain (p = 0.043) and statement for the diagnostics (p = 0.017) and also higher self-assessed satisfaction in measurements (p = 0.046). According to the content analysis, essential gains of online teaching were efficient learning, videos and exercises, and adjustability to own timetable. Conclusion: The online learning course can be considered as a good option for traditional learning of the DC/TMD protocol.

Objective: To describe the occurrence of caries disease from a life course perspective using longitudinal data from the Swedish Quality Registry for Caries and Periodontal Disease (SKaPa). Material and Methods: Data from seven age cohorts (ages 30–90 years), each followed over 10 years, were retrieved from the SKaPa. Using a three-trajectory model, individuals were divided into three trajectories according to their caries development over time: high (15%), moderate (45%), or low (40%). Caries experience was expressed as the mean decayed, missing, and filled surfaces (DMFS) index. Results: Significant differences were found for all three trajectories and in all age groups over the 10 years. The mean DMFS index increase was significantly larger for the high trajectory group than for the moderate and low trajectory groups across all age cohorts. An increase in caries experience was observed for the older cohorts across all trajectories. Conclusions: A three-trajectory model appears useful for identifying and quantifying caries experiences in longitudinal studies. Increased caries disease occurs over time, especially in the highest trajectory group and among older cohorts. These findings emphasise the need for greater attention and more efficient caries prevention methods.

Abstract Studies linking genetics, behavior and life history in any species are rare. In Atlantic salmon (Salmo salar), age at maturity is a key life‐history trait and associates strongly with the vgll3 locus, whereby the vgll3*E allele is linked with younger age at maturity, and higher body condition than the vgll3*L allele. However, the relationship between this genetic variation and behaviors like boldness and exploration which may impact growth and reproductive strategies is poorly understood. The pace‐of‐life syndrome (POLS) framework provides predictions, whereby heightened exploratory behavior and boldness are predicted in individuals with the early maturation‐associated vgll3 genotype (EE). Here, we tested these predictions by investigating the relationship between vgll3 genotypes and exploration and boldness behaviors in 129 juveniles using the novel environment and novel object trials. Our results indicated that contrary to POLS predictions, vgll3*LL fish were bolder and more explorative, suggesting a genotype‐level syndrome including several behaviors. Interestingly, clear sex differences were observed in the latency to move in a new environment, with vgll3*EE males, but not females, taking longer to move than their vgll3*LL counterparts. Our results provide further empirical support for recent calls to consider more nuanced explanations than the pace of life theory for integrating behavior into life‐history theory.

Objective: The aim was to identify caries active individuals among adults by using a trajectory model of longitudinal data from the Swedish national registry (SKaPa) and comparing them with published data from the Dunedin cohort. Materials and methods: Data from two different age groups (30- and 40-year-olds) followed for 10 years were retrieved from SKaPa and were compared with published longitudinal birth-cohort data from the Dunedin study. Using the trajectory model, the subjects were divided into three different trajectories according to their caries development over time (i.e. high, 15%; moderate, 45%; low, 40%). Results: Caries experience, as measured by mean decayed, missing, and filled surfaces (DMFS) index, revealed significant differences among the three trajectories in both age groups. The patterns were similar to those observed in the Dunedin cohort. The mean increase in DMFS during the 10-year follow-up period from SKaPa was significantly higher for the high trajectories in both age groups compared with the moderate and low trajectories. Conclusions: The method using three trajectories for presentation of caries experience over time, may be a useful tool to identify subjects with different disease activities. Identification of subjects in the high caries experience trajectory may increase the possibility to explore and evaluate more effective caries prevention for this group in the future.

Abstract 2D transition metal carbides and nitrides (MXenes) suggest an uncommonly broad combination of important functionalities amongst 2D materials. Nevertheless, MXene suffers from facile oxidation and colloidal instability upon conventional water‐based processing, thus limiting applicability. By experiments and theory, It is suggested that for stability and dispersibility, it is critical to select uncommonly high permittivity solvents such as N‐methylformamide (NMF) and formamide (FA) (εr = 171, 109), unlike the classical solvents characterized by high dipole moment and polarity index. They also allow high MXene stacking order within thin films on carbon nanotube (CNT) substrates, showing very high Terahertz (THz) shielding effectiveness (SE) of 40–60 dB at 0.3–1.6 THz in spite of the film thinness

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Abstract Introduction Chronic pain associates with various sleep problems. Patients with complex regional pain syndrome (CRPS) often report impaired sleep, but objective measurements of sleep in CRPS patients are scarce. Neuromodulation with repetitive transcranial magnetic stimulation (rTMS) can alleviate pain and improve sleep. Secondary somatosensory cortex (S2) is a possible rTMS target for the treatment of chronic pain, but the effect of S2‐targeted rTMS on sleep is unknown. Methods This randomized, sham‐controlled trial assessed the effect of S2‐targeted rTMS on sleep in patients with CRPS. Patients (n = 31) received either S2‐targeted rTMS (10 Hz) or sham stimulation for 3 weeks. The effect of treatment on sleep was assessed with validated questionnaires, with a sleep and pain diary, and with a consumer‐grade sleep tracker, the Oura ring. In addition to an ordinary univariate analysis of the results, we conducted multivariate testing of the Oura data using linear discriminant analysis (LDA). Results S2‐targeted rTMS decreased sleep restlessness that significantly differed between the rTMS and sham stimulation patient groups (p = .028). In the multivariate analysis of the Oura data, LDA classification accuracy to separate the rTMS and sham groups exceeded 95% confidence level in four out of the seven tested models. In the subjective evaluation of sleep, the effect of rTMS and sham did not differ. Conclusion S2‐targeted rTMS influenced sleep in patients with CRPS. Improved sleep may enhance CRPS symptom alleviation and be of clinical importance. A univariate analysis could separate the rTMS and sham treatments. The multivariate analysis revealed that including multiple sleep‐related parameters can be beneficial when analyzing rTMS effects on sleep. As sleep is related both to pain and quality of life, and sleep rTMS can be directly affected by rTMS, objective monitoring of sleep in various future rTMS trials could be fruitful.

New biomarkers are urgently needed for many brain disorders; for example, the diagnosis of mild traumatic brain injury (mTBI) is challenging as the clinical symptoms are diverse and nonspecific. EEG and MEG studies have demonstrated several population-level indicators of mTBI that could serve as objective markers of brain injury. However, deriving clinically useful biomarkers for mTBI and other brain disorders from EEG/MEG signals is hampered by the large inter-individual variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as deviations with respect to the normal variation. To this end, a normative dataset comprising 621 healthy participants was used to determine the variation in power spectra across the cortex. In addition, we constructed normative datasets based on age-matched subsets of the full normative data. To discriminate patients from healthy control subjects, we trained support-vector-machine classifiers on the quantitative deviation maps for 25 mTBI patients and 20 controls not included in the normative dataset. The best performing classifier made use of the full normative data across the entire age and frequency ranges. This classifier was able to distinguish patients from controls with an accuracy of 79%. Inspection of the trained model revealed that low-frequency activity in the theta frequency band (4-8 Hz) is a significant indicator of mTBI, consistent with earlier studies. The results demonstrate the feasibility of using normative modeling of MEG data combined with machine learning to advance diagnosis of mTBI and identify patients that would benefit from treatment and rehabilitation. The current approach could be applied to a wide range of brain disorders, thus providing a basis for deriving MEG/EEG-based biomarkers.

Abstract Background Alphaviral replicon-based vectors induce potent immune responses both when given as viral particles (VREP) or as DNA (DREP). It has been suggested that the strong immune stimulatory effect induced by these types of vectors is mediated by induction of danger signals and activation of innate signalling pathways due to the replicase activity. To investigate the innate signalling pathways involved, mice deficient in either toll-like receptors or downstream innate signalling molecules were immunized with DREP or VREP. Results We show that the induction of a CD8+ T cell response did not require functional TLR3 or MyD88 signalling. However, IRF3, converging several innate signalling pathways and important for generation of pro-inflammatory cytokines and type I IFNs, was needed for obtaining a robust primary immune response. Interestingly, type I interferon (IFN), induced by most innate signalling pathways, had a suppressing effect on both the primary and memory T cell responses after DREP and VREP immunization. Conclusions We show that alphaviral replicon-based vectors activate multiple innate signalling pathways, which both activate and restrict the induced immune response. These results further show that there is a delicate balance in the strength of innate signalling and induction of adaptive immune responses that should be taken into consideration when innate signalling molecules, such as type I IFNs, are used as vaccine adjuvant.

BackgroundIn response to hypoxia, tumor cells undergo transcriptional reprogramming including upregulation of carbonic anhydrase (CA) IX, a metalloenzyme that maintains acid-base balance. CAIX overexpression has been shown to correlate with poor prognosis in various cancers, but the role of this CA isoform in hepatoblastoma (HB) has not been examined.MethodsWe surveyed the expression of CAIX in HB specimens and assessed the impact of SLC-0111, a CAIX inhibitor, on cultured HB cells in normoxic and hypoxic conditions.ResultsCAIX immunoreactivity was detected in 15 out of 21 archival pathology HB specimens. The CAIX-positive cells clustered in the middle of viable tumor tissue or next to necrotic areas. Tissue expression of CAIX mRNA was associated with metastasis and poor clinical outcome of HB. Hypoxia induced a striking upregulation of CAIX mRNA and protein in three HB cell models: the immortalized human HB cell line HUH6 and patient xenograft-derived lines HB-295 and HB-303. Administration of SLC-0111 abrogated the hypoxia-induced upregulation of CAIX and decreased HB cell viability, both in monolayer and spheroid cultures. In addition, SLC-0111 reduced HB cell motility in a wound healing assay. Transcriptomic changes triggered by SLC-0111 administration differed under normoxic vs. hypoxic conditions, although SLC-0111 elicited upregulation of several tumor suppressor genes under both conditions.ConclusionHypoxia induces CAIX expression in HB cells, and the CAIX inhibitor SLC-0111 has in vitro activity against these malignant cells.

Background: There is substantial evidence that midlife hypertension is a risk factor for late life dementia. Our aim was to investigate if even high blood pressure at a single timepoint in midlife can predict an increased risk for all-cause dementia, Alzheimer’s disease (AD), or vascular dementia (VaD) later in life. Methods: The community-based study population comprised 30,102 dementia-free individuals from the Westmannia Cardiovascular Risk Factors Study. The participants were aged 40 or 50 years when the health examination took place in 1990–2000. Diagnose registers from both hospitals and primary healthcare centers were used to identify individuals who after inclusion to the study developed dementia. The association between midlife high blood pressure (defined as systolic blood pressure >140 and/or diastolic blood pressure >90 mmHg) at a single timepoint and dementia was adjusted for age, gender, body mass index (BMI), fasting blood glucose, education, smoking, and physical activity level. Multivariate binary cox regression analyses were used. Results: After a mean follow-up time of 24 years resulting in 662,244 person/years, 761 (2.5%) individuals had been diagnosed with dementia. Midlife high blood pressure at a single timepoint predicted all-cause dementia (hazard ratio [HR]: 1.22, 95% confidence interval [CI]: 1.02–1.45) and VaD (HR: 2.10, 95% CI: 1.47–3.00) but not AD (HR: 1.06, 95% CI: 0.81–1.38). Conclusion: This study suggests that even midlife high blood pressure at a single timepoint predicts all-cause dementia and more than doubles the risk for VaD later in life independently of established confounders. Even though there was no such association with AD, this strengthens the importance of midlife health examinations in order to identify individuals with hypertension and initiate treatment.

This socioculturally informed study aims to apply learning by collaborative designing (LCD) as an instructional model for the creation and studying of new kinds of connected learning systems in teacher education. A case study was organized at the University of Eastern Finland in the context of an information and communication technology (ICT) course aimed at craft student teachers' (N = 13). A qualitative content analysis was used to describe the kind of learning systems that emerged when the students collaboratively designed an extended network of people, objects, and tools for their own learning and teaching. The results reveal that the student teams were active in designing and self-organising the learning environment in the pursuit of shared objects, and in using diverse tools and technologies for thinking and for collecting, organising and sharing information. Implications for designing connected learning and teaching across spaces and communities are also discussed.

Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.

In this study, Marlin et al. provide insights into the potential use of subunit vaccines that induce a high level of protection against SARS-CoV-2 in animal models.

Abstract The outbreak of the SARS-CoV-2 virus and its rapid spread into a global pandemic made the urgent development of scalable vaccines to prevent coronavirus disease (COVID-19) a global health and economic imperative. Here, we characterized and compared the immunogenicity of two alphavirus-based DNA-launched self-replicating (DREP) vaccine candidates encoding either SARS-CoV-2 spike glycoprotein (DREP-S) or a spike ectodomain trimer stabilized in prefusion conformation (DREP-Secto). We observed that the two DREP constructs were immunogenic in mice inducing both binding and neutralizing antibodies as well as T cell responses. Interestingly, the DREP coding for the unmodified spike turned out to be more potent vaccine candidate, eliciting high titers of SARS-CoV-2 specific IgG antibodies that were able to efficiently neutralize pseudotyped virus after a single immunization. In addition, both DREP constructs were able to efficiently prime responses that could be boosted with a heterologous spike protein immunization. These data provide important novel insights into SARS-CoV-2 vaccine design using a rapid response DNA vaccine platform. Moreover, they encourage the use of mixed vaccine modalities as a strategy to combat SARS-CoV-2.

Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a delayed picture naming paradigm, source-localized evoked activity and modulations of cortical oscillations that show high test–retest reliability across measurement days in healthy individuals, demonstrating their applicability in clinical settings. For patients with a language disorder picture naming can be a challenging task. Therefore, we also determined whether a semantic judgment task (‘Is this item living?’) with a spoken response (“yes”/“no”) would suffice to induce comparably consistent activity within brain regions related to language production. The MEG data was collected from 19 healthy participants on two separate days. In picture naming, evoked activity was consistent across measurement days (intraclass correlation coefficient (ICC)>0.4) in the left frontal (400–800 ms after image onset), sensorimotor (200–800 ms), parietal (200–600 ms), temporal (200–800 ms), occipital (400–800 ms) and cingulate (600–800 ms) regions, as well as the right temporal (600–800 ms) region. In the semantic judgment task, consistent evoked activity was spatially more limited, occurring in the left temporal (200–800 ms), sensorimotor (400–800 ms), occipital (400–600 ms) and subparietal (600–800 ms) regions, and the right supramarginal cortex (600–800 ms). The delayed naming task showed typical beta oscillatory suppression in premotor and sensorimotor regions (800–1200 ms) but other consistent modulations of oscillatory activity were mostly observed in posterior cortical regions that have not typically been associated with language processing. The high test–retest consistency of MEG evoked activity in the picture naming task testifies to its applicability in clinical evaluations of language function, as well as in longitudinal MEG studies of language production in clinical and healthy populations.

Modulation of the ~20-Hz brain rhythm has been used to evaluate the functional state of the sensorimotor cortex both in healthy subjects and patients, such as stroke patients. The ~20-Hz brain rhythm can be detected by both magnetoencephalography (MEG) and electroencephalography (EEG), but the comparability of these methods has not been evaluated. Here, we compare these two methods in the evaluating of ~20-Hz activity modulation to somatosensory stimuli.Rhythmic ~20-Hz activity during separate tactile and proprioceptive stimulation of the right and left index finger was recorded simultaneously with MEG and EEG in twenty-four healthy participants.Both tactile and proprioceptive stimulus produced a clear suppression at 300–350 ​ms followed by a subsequent rebound at 700–900 ​ms after stimulus onset, detected at similar latencies both with MEG and EEG. The relative amplitudes of suppression and rebound correlated strongly between MEG and EEG recordings. However, the relative strength of suppression and rebound in the contralateral hemisphere (with respect to the stimulated hand) was significantly stronger in MEG than in EEG recordings.Our results indicate that MEG recordings produced signals with higher signal-to-noise ratio than EEG, favoring MEG as an optimal tool for studies evaluating sensorimotor cortical functions. However, the strong correlation between MEG and EEG results encourages the use of EEG when translating studies to clinical practice. The clear advantage of EEG is the availability of the method in hospitals and bed-side measurements at the acute phase.

Cancer immunotherapy is ineffective in a subset of patients. Here the authors show that, in a mouse model of melanoma, resistance to immune checkpoint inhibitors relies on loss of tumor-specific T cells through FasL-mediated apoptosis triggered by polymorphonuclear-myeloid-derived suppressor cells.

Colloidal self-assembly is a unique method to produce three-dimensional materials with well-defined hierarchical structures and functionalities. Liljeström et al. show controlled preparation of macroscopic chiral wires with helical plasmonic superlattice structure composed of metal nanoparticles and viruses.

Refresher training in cardiopulmonary resuscitation and recognition of patients with failure in vital functions are often completed on separate occasions. In this study, 63 healthcare professionals participated in a pilot course and self-assessed their abilities before and after the course. Combining training scenarios with different diagnoses of patient cases provided a real-life learning environment. The training strengthened the perceived ability of healthcare professionals to respond to an acute situation of a patient with failure of vital functions. [ABSTRACT FROM AUTHOR]

This paper presents a case study in which multi-age students (aged 6-12, N?=?32) in small groups made autonomous inquiries about the phenomenon of winter fishing within the framework of design-oriented pedagogy. The research analyzed storytelling videos that the students produced as learning objects. These videos revealed a picture of the developed learning process and influence afforded and emerged learning ecosystem on it. The research question was as follow: What do the digital stories reveal about the emerged learning ecosystem and learning processes? The qualitative content analysis of the videos' structure, students' commentary, and descriptions of the implemented activities revealed that each small group constructed its own niche in the learning ecosystem. By self-organizing and utilizing the afforded community, technology, and information resources, the students constructed their own interpretations of their chosen research tasks and related inquiries. Based on the findings, we argue that such inquiry-driven learning tasks and afforded learning resources guide students to search for strategic types of knowledge to understand the given phenomena and communicate their complex study processes. The study confirms that students' agency in design-oriented pedagogy will resemble that expected of 21st-century learners. Moreover, young children (from the age of 6) can participate and become active community members in the co-developed learning process and in the creation of local knowledge in the form of storytelling.

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

Synthetic macromolecules that can bind and co-assemble with proteins are important for the future development of biohybrid materials. Active systems are further required to create materials that can respond and change their behavior in response to external stimuli. Here we report that stimuli-responsive linear-branched diblock copolymers consisting of a cationic multivalent dendron with a linear thermoresponsive polymer tail at the focal point, can bind and complex Pyrococcus furiosus ferritin protein cages into crystalline arrays. The multivalent dendron structure utilizes cationic spermine units to bind electrostatically on the surface of the negatively charged ferritin cage and the in situ polymerized poly(di(ethylene glycol) methyl ether methacrylate) linear block enables control with temperature. Cloud point of the final product was determined with dynamic light scattering (DLS), and it was shown to be approximately 31 °C at a concentration of 150 mg/L. Complexation of the polymer binder and apoferritin was studied with DLS, small-angle X-ray scattering, and transmission electron microscopy, which showed the presence of crystalline arrays of ferritin cages with a face-centered cubic (fcc, \( Fm\overline{3}m \)) Bravais lattice where lattice parameter a = 18.6 nm. The complexation process was not temperature dependent but the final complexes had thermoresponsive characteristics with negative thermal expansion.

Vaccination with DNA is an attractive strategy for induction of pathogen-specific T cells and antibodies. Studies in humans have shown that DNA vaccines are safe, but their immunogenicity needs further improvement. As a step towards this goal, we have previously demonstrated that immunogenicity is increased with the use of an alphavirus DNA-launched replicon (DREP) vector compared to conventional DNA vaccines. In this study, we investigated the effect of varying the dose and number of administrations of DREP when given as a prime prior to a heterologous boost with poxvirus vector (MVA) and/or HIV gp140 protein formulated in glucopyranosyl lipid A (GLA-AF) adjuvant. The DREP and MVA vaccine constructs encoded Env and a Gag-Pol-Nef fusion protein from HIV clade C. One to three administrations of 0.2 μg DREP induced lower HIV-specific T cell and IgG responses than the equivalent number of immunizations with 10 μg DREP. However, the two doses were equally efficient as a priming component in a heterologous prime-boost regimen. The magnitude of immune responses depended on the number of priming immunizations rather than the dose. A single low dose of DREP prior to a heterologous boost resulted in greatly increased immune responses compared to MVA or protein antigen alone, demonstrating that a mere 0.2 μg DREP was sufficient for priming immune responses. Following a DREP prime, T cell responses were expanded greatly by an MVA boost, and IgG responses were also expanded when boosted with protein antigen. When MVA and protein were administered simultaneously following multiple DREP primes, responses were slightly compromised compared to administering them sequentially. In conclusion, we have demonstrated efficient priming of HIV-specific T cell and IgG responses with a low dose of DREP, and shown that the priming effect depends on number of primes administered rather than dose.