A Serious Game (MyDiabetic) to Support Children's Education in Type 1 Diabetes Mellitus: Iterative Participatory Co-Design and Feasibility Study.

BACKGROUND: Serious games, which are gaming applications used for purposes beyond entertainment to educate users on, and address, specific issues, may present a timely approach to promote healthy diabetes management behaviors among children with type 1 diabetes mellitus (T1DM). The lasting benefits associated with these serious games encompass improved patient education; enhanced glycemic control; the reinforcement of bonds within the community of people with diabetes; the facilitation of meaningful dialogues with caregivers, especially within the familial setting; and a significant reduction in the economic burdens associated with subsequent complications. OBJECTIVE: This paper primarily aims to provide a detailed overview of the iterative design process and the associated evaluation methods used in the development of the educational game. Furthermore, this study aims to enhance motivation for sustained and extended engagement with the game over time. The MyDiabetic game design aims to educate children on various aspects, including the connections among food, insulin, and physical activity. Furthermore, it seeks to impart knowledge related to the operation of a glucometer and an insulin pen, as well as more advanced technologies such as administering glucagon, measuring ketoacidosis, and continuous glucose monitoring. METHODS: The co-design methodology was applied, involving interviews, design workshops, and prototype feedback sessions. A combination of several approaches, such as tailoring, observational learning, social and family support, decision-making practice, and reward systems, was used to support children's compliance. Moreover, incorporating the literature, guidelines, and current practices into the design ensured that the game was aligned with established health care pathways and included relevant information and best practices for diabetes management. RESULTS: The game was tested on 32 children in 3 iterations. Positive responses were received from children who tested the game as well as their parents. The game was also presented to 5 schoolmates of children with T1DM who appreciated a better understanding of the disease and the opportunity to support their friends more efficiently in T1DM compensation. The involvement of children and clinicians in participatory co-design contributed to to the game's high acceptance. With regard to the game's impact on education, 1 week of testing revealed an enhancement in educational outcomes. CONCLUSIONS: The game is especially suitable for children newly diagnosed with T1DM because it acquaints them in a fun way with new terminology; for example, they can try to measure glycemia levels in an interactive way. The game also caters to children who still need to develop reading skills by including an audio guide. The guide ensures that children of all literacy levels can benefit from the game's educational content and interactive experiences. The game is available for download on Google Play and the Apple App Store.

Correction: Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

[This corrects the article DOI: 10.1371/journal.pone.0298320.].

Statistical segmentation model for accurate electrode positioning in Parkinson's deep brain stimulation based on clinical low-resolution image data and electrophysiology.

BACKGROUND: Deep Brain Stimulation (DBS), applying chronic electrical stimulation of subcortical structures, is a clinical intervention applied in major neurologic disorders. In order to achieve a good clinical effect, accurate electrode placement is necessary. The primary localisation is typically based on presurgical MRI imaging, often followed by intra-operative electrophysiology recording to increase the accuracy and to compensate for brain shift, especially in cases where the surgical target is small, and there is low contrast: e.g., in Parkinson's disease (PD) and in its common target, the subthalamic nucleus (STN). METHODS: We propose a novel, fully automatic method for intra-operative surgical navigation. First, the surgical target is segmented in presurgical MRI images using a statistical shape-intensity model. Next, automated alignment with intra-operatively recorded microelectrode recordings is performed using a probabilistic model of STN electrophysiology. We apply the method to a dataset of 120 PD patients with clinical T2 1.5T images, of which 48 also had available microelectrode recordings (MER). RESULTS: The proposed segmentation method achieved STN segmentation accuracy around dice = 0.60 compared to manual segmentation. This is comparable to the state-of-the-art on low-resolution clinical MRI data. When combined with electrophysiology-based alignment, we achieved an accuracy of 0.85 for correctly including recording sites of STN-labelled MERs in the final STN volume. CONCLUSION: The proposed method combines image-based segmentation of the subthalamic nucleus with microelectrode recordings to estimate their mutual location during the surgery in a fully automated process. Apart from its potential use in clinical targeting, the method can be used to map electrophysiological properties to specific parts of the basal ganglia structures and their vicinity.

The epidemiology of firework-related injuries in the US, 2012-2022.

BACKGROUND: This study aimed to examine the epidemiology of firework-related injuries within a national population between 2012 and 2022, including the severity of injury by year, patient demographics, body region injured, firework type, and diagnosis category of injury. METHODS: Data were collected from the Consumer Product Safety Commission's National Electronic Injury Surveillance System, which is a representative nationwide database that collects data on consumer product-related injuries occurring in the US. Injury rates were calculated based on patient age, sex, body region injured, firework type, and diagnosis category. RESULTS: A total of 3219 injuries, representing an estimated 122,912 firework-related injuries, were treated in emergency departments within the US from 2012 to 2022. The overall incidence rate of firework-related injuries in the study rose by over 17% from 2012 [2.61 cases per 100,000 people (95% CI 2.03-3.20)] to 2022 and [3.05 cases per 100,000 people (95% CI 2.29-3.80)]. The rate of injuries was highest in adolescents and young adults (age 20-24; 7.13 cases per 100,000 people). Men experienced firework injuries at more than double the rate of women (4.90 versus 2.25 cases per 100,000 people). The upper extremities (41.62%), head/neck (36.40%), and lower extremities (13.78%) were the most commonly injured regions. Over 20% of cases in patients older than 20 were significant injuries requiring hospitalization. Aerial devices (32.11%) and illegal fireworks (21.05%) caused the highest rates of significant injury of any firework type. CONCLUSIONS: The incidence of firework-related injuries has risen over the past decade. Injuries remain the most common among adolescents and young adults. In addition, significant injuries requiring hospitalization occur most often during aerial and illegal firework use. Further targeted sale restrictions, distribution, and manufacturing regulations for high-risk fireworks are required to reduce the incidence of significant injury.

Classifying and Scoring Major Depressive Disorders by Residual Neural Networks on Specific Frequencies and Brain Regions.

Major Depressive Disorder (MDD) - can be evaluated by advanced neurocomputing and traditional machine learning techniques. This study aims to develop an automatic system based on a Brain-Computer Interface (BCI) to classify and score depressive patients by specific frequency bands and electrodes. In this study, two Residual Neural Networks (ResNets) based on electroencephalogram (EEG) monitoring are presented for classifying depression (classifier) and for scoring depressive severity (regression). Significant frequency bands and specific brain regions are selected to improve the performance of the ResNets. The algorithm, which is estimated by 10-fold cross-validation, attained an average accuracy rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error (RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific EEG channels, we obtained the best-classifying accuracy at 0.871 and the smallest RMSE at 2.80. It was discovered that signals extracted from the beta band are more distinctive in depression classification, and these selected channels tend to perform better on scoring depressive severity. Our study also uncovered the different brain architectural connections by relying on phase coherence analysis. Increased delta deactivation accompanied by strong beta activation is the main feature of depression when the depression symptom is becoming more severe. We can therefore conclude that the model developed here is acceptable for classifying depression and for scoring depressive severity. Our model can offer physicians a model that consists of topological dependency, quantified semantic depressive symptoms and clinical features by using EEG signals. These selected brain regions and significant beta frequency bands can improve the performance of the BCI system for detecting depression and scoring depressive severity.

Circulating microRNAs in young individuals with long-duration type 1 diabetes in comparison with healthy controls.

MicroRNAs (miRNAs) are short non-coding RNAs that are involved in post-transcriptional control of gene expression and might be used as biomarkers for diabetes-related complications. The aim of this case-control study was to explore potential differences in circulating miRNAs in young individuals with long-duration type 1 diabetes (T1D) compared to healthy controls, and how identified miRNAs are expressed across different tissues. Twelve adolescents, age 15.0-17.9 years, with T1D duration of more than 8 years (mean 11.1 years), were enrolled from the Swedish diabetes quality registry. An age-matched control group was recruited. Circulating miRNAs (n = 187) were analyzed by quantitative PCR. We observed that 27 miRNAs were upregulated and one was downregulated in T1D. Six of these miRNAs were tissue-enriched (blood cells, gastrointestinal, nerve, and thyroid tissues). Six miRNAs with the largest difference in plasma, five up-regulated (hsa-miR-101-3p, hsa-miR-135a-5p, hsa-miR-143-3p, hsa-miR-223-3p and hsa-miR-410-3p (novel for T1D)) and one down-regulated (hsa-miR-495-3p), with P-values below 0.01, were selected for further in-silico analyses. AKT1, VEGFA and IGF-1 were identified as common targets. In conclusion, 28 of the investigated miRNAs were differently regulated in long-duration T1D in comparison with controls. Several associations with cancer were found for the six miRNAs with the largest difference in plasma.

Secretogranin II influences the assembly and function of MHC class I in melanoma.

Melanoma is the deadliest form of skin cancer showing rising incidence over the past years. New insights into the mechanisms of melanoma progression contributed to the development of novel treatment options, such as immunotherapies. However, acquiring resistance to treatment poses a big problem to therapy success. Therefore, understanding the mechanisms underlying resistance could improve therapy efficacy. Correlating expression levels in tissue samples of primary melanoma and metastases revealed that secretogranin 2 (SCG2) is highly expressed in advanced melanoma patients with poor overall survival (OS) rates. By conducting transcriptional analysis between SCG2-overexpressing (OE) and control melanoma cells, we detected a downregulation of components of the antigen presenting machinery (APM), which is important for the assembly of the MHC class I complex. Flow cytometry analysis revealed a downregulation of surface MHC class I expression on melanoma cells that showed resistance towards the cytotoxic activity of melanoma-specific T cells. IFNγ treatment partially reversed these effects. Based on our findings, we suggest that SCG2 might stimulate mechanisms of immune evasion and therefore be associated with resistance to checkpoint blockade and adoptive immunotherapy.

Advocacy for Health Justice: An Innovative Pilot Course for MD and Master of Public Policy Students.

Problem: U.S. medical schools are searching for ways to address issues of health justice in undergraduate medical education. Physicians have not typically received training in how to be effective advocates for systemic change and individuals in policy fields are not usually equipped to understand the complex issues of health science and their intersection with the health system and society. To address this gap, medical school faculty partnered with school of public policy faculty on a collaborative learning model that engaged MD and Master of Public Policy students together to strengthen their collective knowledge of the healthcare landscape, and to build skills to work for health justice. Intervention: We hypothesized that pairing medical students with public policy students to learn about the intersections of health justice and advocacy could enhance the efficacy of each group and provide a new model of collaboration between medical and policy professionals. The students collaborated on a health justice advocacy project through which they provided consultation to an established community organization. Context: The 8-week course took place in the spring of 2021 in Los Angeles, California. Due to Covid-19 the course was taught online and included asynchronous learning modules and live Zoom sessions. The project also served as a pilot for the post-clerkship phase of a new longitudinal health justice curriculum for MD students that launched in August 2021. Impact: Analysis of student work products, course evaluations, partner interviews, and student focus groups showed that students valued learning through their interdisciplinary collaborative work which gave them new perspectives on health justice issues. The community partners indicated that the students consultative work products were useful for their initiatives, and that they found working with MD and MPP students to be a valuable way to think about how to build stronger and more inclusive coalitions to advocate for health justice. This project has the potential for national impact as it aligns with the Association of American Medical Colleges' renewed focus on the responsibility of academic medicine to partner with communities for health justice. The project also contributed to the national conversation on how to align health systems science education with the aims of health justice through our participation in the American Medical Association Accelerating Change in Medical Education Consortium. Lessons Learned: Leveraging faculty relationships with community partners was crucial for developing meaningful projects for students. Cultivating and expanding community partner networks is necessary to sustain and scale up this type of intervention. Centering the needs of communities and supporting their on-going work for health justice is essential for becoming an effective advocate. Learning communities that bring interdisciplinary students, healthcare providers, policy professionals, and community partners together to learn from one another can create key opportunities for ameliorating health inequities.

HSP90α induces immunosuppressive myeloid cells in melanoma via TLR4 signaling.

BACKGROUND: Tumor cells modulate host immunity by secreting extracellular vesicles (EV) and soluble factors. Their interactions with myeloid cells lead to the generation of myeloid-derived suppressor cells (MDSC), which inhibit the antitumor function of T and NK cells. We demonstrated previously that EV derived from mouse and human melanoma cells induced immunosuppressive activity via increased expression of programmed cell death ligand 1 (PD-L1) on myeloid cells that was dependent on the heat-shock protein 90α (HSP90α) in EV. Here, we investigated whether soluble HSP90α could convert monocytes into MDSC. METHODS: CD14 monocytes were isolated from the peripheral blood of healthy donors, incubated with human recombinant HSP90α (rHSP90α) alone or in the presence of inhibitors of TLR4 signaling and analyzed by flow cytometry. Inhibition of T cell proliferation assay was applied to assess the immunosuppressive function of rHSP90α-treated monocytes. HSP90α levels were measured by ELISA in plasma of patients with advanced melanoma and correlated with clinical outcome. RESULTS: We found that the incubation of monocytes with rHSP90α resulted in a strong upregulation of PD-L1 expression, whereas reactive oxygen species (ROS) and nitric oxide (NO) production as well as the expression of arginase-1, ectoenzymes CD39 and CD73 remained unchanged. The PD-L1 upregulation was blocked by anti-TLR4 antibodies and a nuclear factor-κB inhibitor. rHSP90α-treated monocytes displayed the downregulation of HLA-DR expression and acquired the resistance to apoptosis. Moreover, these monocytes were converted into MDSC as indicated by their capacity to inhibit T cell proliferation, which was mediated by TLR4 signaling as well as PD-L1 and indoleamine 2,3-dioxygenase (IDO) 1 expression. Higher levels of HSP90α in plasma of patients with melanoma correlated with augmented PD-L1 expression on circulating monocytic (M)-MDSC. Patients with melanoma with high levels of HSP90α displayed shorter progression-free survival (PFS) on the treatment with immune checkpoint inhibitors (ICIs). CONCLUSION: Our findings demonstrated that soluble rHSP90α increased the resistance of normal human monocytes to apoptosis and converted them into immunosuppressive MDSC via TLR4 signaling that stimulated PD-L1 and IDO-1 expression. Furthermore, patients with melanoma with high concentrations of HSP90α displayed increased PD-L1 expression on M-MDSC and reduced PFS after ICI therapy, suggesting HSP90α as a promising therapeutic target for overcoming immunosuppression in melanoma.

Melanoma Cellular Plasticity.

Despite the advances of modern medicine and the development of innovative and promising new therapeutic strategies for the treatment of the numerous types of cancer, far too many patients still lose the battle against these devastating diseases [...].

Managing Diabetes Using Mobiab: Long-Term Case Study of the Impact of a Mobile App on Self-management.

BACKGROUND: This paper describes the development of a mobile app for diabetes mellitus (DM) control and self-management and presents the results of long-term usage of this system in the Czech Republic. DM is a chronic disease affecting large numbers of people worldwide, and this number is continuously increasing. There is massive potential to increase adherence to self-management of DM with the use of smartphones and digital therapeutics interventions. OBJECTIVE: This study aims to describe the process of development of a mobile app, called Mobiab, for DM management and to investigate how individual features are used and how the whole system benefits its long-term users. Using at least 1 year of daily records from users, we analyzed the impact of the app on self-management of DM. METHODS: We have developed a mobile app that serves as an alternative form to the classic paper-based protocol or diary. The development was based on cooperation with both clinicians and people with DM. The app consists of independent individual modules. Therefore, the user has the possibility to use only selected features that they find useful. Mobiab was available free of charge on Google Play Store from mid-2014 until 2019. No targeted recruitment was performed to attract users. RESULTS: More than 500 users from the Czech Republic downloaded and signed up for the mobile app. Approximately 80% of the users used Mobiab for less than 1 week. The rest of the users used it for a longer time and 8 of the users produced data that were suitable for long-term analysis. Additionally, one of the 8 users provided their medical records, which were compared with the gathered data, and the improvements in their glucose levels and overall metabolic stability were consistent with the way in which the mobile app was used. CONCLUSIONS: The results of this study showed that the usability of a DM-centered self-management smartphone mobile app and server-based systems could be satisfactory and promising. Nonetheless, some better ways of motivating people with diabetes toward participation in self-management are needed. Further studies involving a larger number of participants are warranted to assess the effect on long-term diabetes management.

Comparing Reminders Sent via SMS Text Messaging and Email for Improving Adherence to an Electronic Health Program: Randomized Controlled Trial.

BACKGROUND: eHealth interventions can help people change behavior (eg, quit smoking). Reminders sent via SMS text messaging or email may improve the adherence to web-based programs and increase the probability of successful behavior change; however, it is unclear whether their efficiency is affected by the modality of the communication channel. OBJECTIVE: A 2-armed randomized control trial was conducted to compare the effect of providing reminders via SMS text messaging versus email on the adherence to an eHealth program for smoking cessation and on the probability to initiate a quit attempt. METHODS: Smokers were recruited via an internet-based advertisement. A total of 591 participants who diverted from intended use of the program (ie, failed to log on to a session) were automatically randomized to the experimental (SMS text messaging reminder, n=304) or the active comparator (email reminder, n=287) group. RESULTS: Unexpectedly, we found that the mode of reminder delivery did not significantly affect either the adherence, namely the number of completed program sessions, with the SMS text messaging reminder group showing a mean of 4.30 (SD 3.24) and the email reminder group showing a mean of 4.36 (SD 3.27) (t586=0.197, P=.84, and Cohen d=0.016), or the outcome, namely the quit smoking attempt rate (34.2% in the SMS text messaging group vs 31.7% in the email group; χ21=0.4, P=.52). Secondary analyses showed that age, gender, and education had significant effects on program adherence and education on the outcome. Moreover, we found a significant interaction effect between the mode of reminder delivery and gender on program adherence, suggesting that the effectiveness of SMS text message reminders might be different for females and males. However, this particular finding should be treated with care as it was based on post hoc subgroup analysis. CONCLUSIONS: This study indicates that the modality of user reminders to log on increased neither the program adherence nor the probability of quitting smoking. This suggests that program developers may save costs using emails instead of SMS text messaging reminders. TRIAL REGISTRATION: ClinicalTrials.gov NCT03276767; https://clinicaltrials.gov/ct2/show/ NCT03276767.

ADCK2 Knockdown Affects the Migration of Melanoma Cells via MYL6.

BACKGROUND: ADCK2 is a member of the AarF domain-containing kinase family, which consists of five members, and has been shown to play a role in CoQ metabolism. However, ADCKs have also been connected to cancer cell survival, proliferation and motility. In this study, we investigated the role of ADCK2 in melanoma. METHODS: The effect of ADCK2 on melanoma cell motility was evaluated by a scratch assay and a transwell invasion assay upon siRNA-mediated knockdown or stable overexpression of ADCK2. RESULTS: We found that high levels of intratumoral ADCK2 and MYL6 are associated with a higher survival rate in melanoma patients. Knocking down ADCK2 resulted in enhanced cell migration of melanoma cells. Moreover, ADCK2-knockdown cells adopted a more dedifferentiated phenotype. A gene expression array revealed that the expression of ADCK2 correlated with the expressions of MYL6 and RAB2A. Knocking down MYL6 in ADCK2-overexpressing cells could abrogate the effect of ADCK2 overexpression and thus confirm the functional connection between ADCK2 and MYL6. CONCLUSION: ADCK2 affects melanoma cell motility, most probably via MYL6. Our results allow the conclusion that ADCK2 could act as a tumor suppressor in melanoma.

Motor activity patterns can distinguish between interepisode bipolar disorder patients and healthy controls.

BACKGROUND: Bipolar disorder (BD) is linked to circadian rhythm disruptions resulting in aberrant motor activity patterns. We aimed to explore whether motor activity alone, as assessed by longitudinal actigraphy, can be used to classify accurately BD patients and healthy controls (HCs) into their respective groups. METHODS: Ninety-day actigraphy records from 25 interepisode BD patients (ie, Montgomery-Asberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) < 15) and 25 sex- and age-matched HCs were used in order to identify latent actigraphic biomarkers capable of discriminating between BD patients and HCs. Mean values and time variations of a set of standard actigraphy features were analyzed and further validated using the random forest classifier. RESULTS: Using all actigraphy features, this method correctly assigned 88% (sensitivity = 85%, specificity = 91%) of BD patients and HCs to their respective group. The classification success may be confounded by differences in employment between BD patients and HCs. When motor activity features resistant to the employment status were used (the strongest feature being time variation of intradaily variability, Cohen's d = 1.33), 79% of the subjects (sensitivity = 76%, specificity = 81%) were correctly classified. CONCLUSION: A machine-learning actigraphy-based model was capable of distinguishing between interepisode BD patients and HCs solely on the basis of motor activity. The classification remained valid even when features influenced by employment status were omitted. The findings suggest that temporal variability of actigraphic parameters may provide discriminative power for differentiating between BD patients and HCs while being less affected by employment status.

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study.

BACKGROUND: Self-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care. OBJECTIVE: In this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire-a 10-item, mobile app-based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]). METHODS: The content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model. RESULTS: A total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844). CONCLUSIONS: The ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

A Novel Saliva Collection Method among Children and Infants: A Comparison Study between Oral Swab and Pacifier-based Saliva Collection.

AIM: This study aims to test the feasibility and effectiveness of a novel pacifier-based saliva collection method on children and infants in comparison to an oral swab-based saliva collection method. MATERIALS AND METHODS: This study was performed during spring 2018 in a clinical non-sponsored setting at Queen Silvia Children's Hospital pediatric emergency ward. Saliva collection was performed by comparing oral swab (Salimetrics® SalivaBio's Children's Swab) with a pacifier-based saliva collection method (Salivac®). All participating children used both saliva collection systems. The amount of saliva collected in 2 minutes was measured. The amount of time needed for the healthcare professional was recorded. Parental preference was evaluated by a questionnaire. RESULTS: No statistically significant difference was observed in collected saliva (174 µL for pacifier-based saliva collection and 158 µL for oral swab). The healthcare professional spent significantly less (p < 0.001) mean time with the pacifier-based saliva collection method than with the oral swab (31 vs 150 sec). A total of 48 out of the 52 caretakers preferred the pacifier-based saliva collection method compared to the oral swab. CONCLUSION: The novel pacifier-based saliva collection method proved to be a feasible, appreciated, and effective way of collecting saliva that simplifies the saliva collection method among children and infants. CLINICAL SIGNIFICANCE: The pacifier-based saliva collection method simplifies saliva testing. The closed vacutainer system minimizes the risk of saliva contamination and opens up for novel home testing strategies.

FOXD1 promotes dedifferentiation and targeted therapy resistance in melanoma by regulating the expression of connective tissue growth factor.

Metastatic melanoma is an aggressive skin cancer and associated with a poor prognosis. In clinical terms, targeted therapy is one of the most important treatments for patients with BRAFV600E -mutated advanced melanoma. However, the development of resistance to this treatment compromises its therapeutic success. We previously demonstrated that forkhead box D1 (FOXD1) regulates melanoma migration and invasion. Here, we found that FOXD1 was highly expressed in melanoma cells and was associated with a poor survival of patients with metastatic melanoma. Upregulation of FOXD1 expression enhanced melanoma cells' resistance to vemurafenib (BRAF inhibitor [BRAFi]) or vemurafenib and cobimetinib (MEK inhibitor) combination treatment whereas loss of FOXD1 increased the sensitivity to treatment. By comparing gene expression levels between FOXD1 knockdown (KD) and overexpressing (OE) cells, we identified the connective tissue growth factor (CTGF) as a downstream factor of FOXD1. Chromatin immunoprecipitation and luciferase assay demonstrated the direct binding of FOXD1 to the CTGF promoter. Similar to FOXD1, knockdown of CTGF increased the sensitivity of BRAFi-resistant cells to vemurafenib. FOXD1 KD cells treated with recombinant CTGF protein were less sensitive towards vemurafenib compared to untreated FOXD1 KD cells. Based on these findings, we conclude that FOXD1 might be a promising new diagnostic marker and a therapeutic target for the treatment of targeted therapy resistant melanoma.

New Biomarkers in Cancers.

In this Special Issue of Cancers, the latest insights on biomarkers in cancers are presented in 33 up-to-the-minute research papers and reviews summing up the tremendous progress in this interesting and important field of research. The recent development of new therapeutic approaches has provided clinicians with more efficient tools than ever before for the treatment of cancerous diseases. However, choosing the right option requires to [...].