BACKGROUND: Frequent consumption of Energy Drinks (EDs) is associated with numerous health problems, including overweight and obesity, particularly among children and adolescents. The extensive promotion, wide accessibility, and relatively low cost of EDs have significantly increased their popularity among this age group. This paper examines policies/programs that, directly and indirectly, contribute to reducing ED consumption in children and adolescents and shares global experiences to help policymakers adopt evidence-based policies.
METHODS: A systematic search was performed using PubMed, Scopus, and Web of Science databases from January 2000 to June 2024, along with reputable international organization websites, to find literature on policies aimed at reducing ED consumption among children and adolescents. All sources meeting the inclusion criteria were included without restrictions. Titles and abstracts were initially screened, followed by a full-text review. After evaluating the quality of the selected studies, data were extracted and, along with information from the selected documents, compiled into a table, detailing the country, policy type, and the effectiveness and weaknesses of each policy.
RESULTS: Out of 12166 reviewed studies and documents, 84 studies and 70 documents met the inclusion criteria. 73 countries and territories have implemented policies like taxation, sales bans, school bans, labeling, and marketing restrictions on EDs. Most employ fiscal measures, reducing consumption despite enforcement challenges. Labeling, access restrictions, and marketing bans are common but face issues like black markets.
CONCLUSIONS: This scoping review outlines diverse strategies adopted by countries to reduce ED consumption among children and teenagers, such as taxation, school bans, sales restrictions, and labeling requirements. While heightened awareness of ED harms has reinforced policy efforts, many Asian and African nations lack such measures, some policies remain outdated for over a decade, and existing policies face several challenges. These challenges encompass industry resistance, governmental disagreements, public opposition, economic considerations, and the intricacies of policy design. Considering this, countries should tailor policies to their cultural and social contexts, taking into account each policy\'s strengths and weaknesses to avoid loopholes. Inter-sectoral cooperation, ongoing policy monitoring, updates, and public education campaigns are essential to raise awareness and ensure effective implementation.

Bottled water has become a popular beverage choice worldwide, with consumers increasingly seeking healthier options. However, label elements can significantly influence consumer perception and purchasing decisions. The research aimed to assess how label elements affect the liking, purchase intention, preference and concept of healthy bottled water. Two stages involved 180 and 100 participants aged between 18 and 40, provided sociodemographic information. The first stage used a hedonic scale and ranking test to perception of nine labels with different elements. The second stage selected a consensus label from prior tests. Four labels were designed, differing in brand color and nutritional information placement. In this last stage, the acceptability, preference ranking and concept of healthy were re-evaluated and eye tracking via the Pupil Lab program. Findings showed varied responses in acceptability and purchase intention among consumers. However, significant differences were observed in preferences and healthiness perceptions based on label characteristics. The label with the highest preference and perceived healthiness featured a sky-blue design with nutritional information on the right side. Combining sensory testing and eye tracking offers valuable insights for designing labels that positively impact consumer perception. The results provide important implications for bottled water manufacturers and marketers in developing effective labeling strategies to meet consumer preferences and promote healthier choices.

UNASSIGNED: There is a lack of consensus in the addiction field as to how to refer to alumni of residential treatment who no longer use substances or who reduce their use. In the literature, this label and broader identity are typically discussed in technical (amount and frequency of use) or social terms (environment and social network changes).
UNASSIGNED: The current paper seeks to simplify the discussion by focusing on personal labels without complex technical or social considerations. Alumni of an inpatient addiction treatment facility were asked how they refer to themselves regarding their sobriety status post-discharge.
UNASSIGNED: Forty-nine patients were contacted 3 months post-discharge from a residential inpatient addiction treatment (men = 67%; Mage = 47.75 years). The patients completed a post-discharge assessment that was conducted by a trained research assistant over a 20-minute video call. The current study focused on a \"sobriety label\" measure in which patients indicated what they want to be called. Patients also explained why they chose their answer in an open-ended question.
UNASSIGNED: Most patients identified as in recovery (n = 29; 59.18%) followed by a sober person (n = 7; 14.29%) and four other responses. No alum selected the in remission option, which is notably a common way to refer to patients who no longer use substances.
UNASSIGNED: The current study adds a critical patient/alumni perspective to the existing body of literature and serves as a call to action for researchers to add a similar \"sobriety label\" measure to future assessments, studies, and batteries in effort to bring consistency to the labels, definitions, and identities that are published. This methodology of understanding how this population identifies will create uniformity in future literature and decrease the stigma surrounding addiction.
There is a history of inconsistent use of labels, definitions, and identities in the addiction treatment field. Few past studies have directly asked patients how they self-label, and it is important to ask those who use substances or who have reduced their use what they preferred to be called. This study asked a simple question to alumni of an inpatient treatment facility what they want to be called. We then asked them to explain why they chose that answer. Most alumni identified as “in recovery” or “a sober person”. This simple tool can be utilized by other facilities and also highlights that many research studies are referring to individuals by terms they do not prefer (eg, “in remission”).

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality.
OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data.
METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips.
RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts\' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance.
CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

The claustrum has a unique thin sheet-like structure that makes it hard to identify in typical anatomical MRI scans. Attempts have been made to identify the claustrum in anatomical images with either automatic segmentation techniques or using atlas-based approaches. However, the resulting labels fail to include the ventral claustrum portion, which consists of fragmented grey matter referred to as \"puddles\". The current dataset is a high-resolution label of the whole claustrum manually defined using an ultra-high resolution postmortem MRI image of one individual. Manual labelling was performed by four independent research trainees. Two trainees labelled the left claustrum and another two trainees labelled the right claustrum. For every hemisphere we created a union of the two labels and assessed the label correspondence using dice coefficients. We provide size measurements of the labels in MNI space by calculating the oriented bounding box size. These data are the first manual claustrum segmentation labels that include both the dorsal and ventral claustrum regions at such a high resolution in standard space. The label can be used to approximate the claustrum location in typical in vivo MRI scans of healthy individuals.

Fluorescent labels have strongly contributed to many advancements in bioanalysis, molecular biology, molecular imaging, and medical diagnostics. Despite a large toolbox of molecular and nanoscale fluorophores to choose from, there is still a need for brighter labels, e.g., for flow cytometry and fluorescence microscopy, that are preferably of molecular nature. This requires versatile concepts for fluorophore multimerization, which involves the shielding of dyes from other chromophores and possible quenchers in their neighborhood. In addition, to increase the number of readout parameters for fluorescence microscopy and eventually also flow cytometry, control and tuning of the labels\' fluorescence lifetimes is desired. Searching for bright multi-chromophoric or multimeric labels, we developed PEGylated dyes bearing functional groups for their bioconjugation and explored their spectroscopic properties and photostability in comparison to those of the respective monomeric dyes for two exemplarily chosen fluorophores excitable at 488 nm. Subsequently, these dyes were conjugated with anti-CD4 and anti-CD8 immunoglobulins to obtain fluorescent conjugates suitable for the labeling of cells and beads. Finally, the suitability of these novel labels for fluorescence lifetime imaging and target discrimination based upon lifetime measurements was assessed. Based upon the results of our spectroscopic studies including measurements of fluorescence quantum yields (QY) and fluorescence decay kinetics we could demonstrate the absence of significant dye-dye interactions and self-quenching in these multimeric labels. Moreover, in a first fluorescence lifetime imaging (FLIM) study, we could show the future potential of this multimerization concept for lifetime discrimination and multiplexing.

In an article (Asaria in J Eat Disord 11:107, 2023) recently published by the Journal of Eating Disorders, I expressed my lived experience views on the concept of \'terminal anorexia nervosa\' (AN), and why I believe that this is a harmful new term. The article was not a response to the original paper in which Gaudiani et al. (J Eat Disord 10:23, 2022) proposed criteria for the label. However, as a result of feedback that my article did not appreciate their criteria, I have written this follow-up paper to build on and reinforce what I previously wrote. This article outlines problems with each criterion in turn, again from my lived experience perspective. It then addresses dangerous ambiguities around how the criteria can be applied safely, and their confusing purpose in the real world. Finally, I discuss the impact of labelling AN sufferers with terms that may suggest their wholehearted allegiance to the illness, in both life and death (or \'till death do us part\').

Increasing demand for camel\'s milk worldwide occurred in the context of the development of the organic sector in agriculture. The implementation of an organic label for camel milk has never been established. However, the creation of such a label faces to important challenges that are investigated in the present paper. Indeed, although camel milk conveys the image of a \"natural product\" issued from remote places, the risk of being produced in contaminated areas (mining activities, oil extraction) cannot be neglected for grazing animals. Moreover, the management of veterinary drugs for prevention or curative treatment can lead to the presence of residues in milk, especially in camel species with different pharmacokinetics, although similar instructions than for cow milk are used. Moreover, the lack of international standards regarding both composition and hygienic rules, the risks of adulteration, and the necessity to use specific indicators or analytical procedures adapted to the behavior of camel milk, have to be taken in account in the establishment of the specifications for the camel milk producers through the world.

Food labeling serves as a crucial medium for industries to communicate product qualities to consumers. Sanitary inspection and artisanal seals are significant markers for traditional cheeses, yet current information on this topic is limited. Therefore, this study aims to evaluate the impact of sanitary inspection and the ARTE seal on the acceptance of artisanal cheese. To achieve this objective, four hypothetical cheese labels featuring all combinations of sanitary inspection and ARTE seals were presented to 404 consumers. These consumers rated their acceptance of each label, a conjoint analysis was conducted, and the relative importance of each seal was calculated. Subsequently, consumers were segmented using hierarchical cluster analysis. Their socio-demographic profiles were statistically correlated to the clusters through a chi-squared method. The results revealed the existence of three distinct consumer groups: those who strongly prefer cheeses with a sanitary seal (assigning a relative importance of 80.2% to the seal), those who favor cheeses with an artisanal seal (assigning a relative importance of 52.5% to the seal), and those for whom the presence of either seal did not significantly affect acceptance. Consumers residing in metropolitan areas generally placed less value on both seals, whereas frequent purchasers of artisanal foods and residents of rural areas showed a preference for the artisanal seal. Other socio-demographic variables did not statistically correlate with cluster membership. In conclusion, consumer segmentation based on preferences for sanitary inspection and artisanal seals in food labeling is vital for developing effective marketing strategies and food safety education policies.

BACKGROUND: Prostate segmentation is an essential step in computer-aided detection and diagnosis systems for prostate cancer. Deep learning (DL)-based methods provide good performance for prostate gland and zones segmentation, but little is known about the impact of manual segmentation (that is, label) selection on their performance. In this work, we investigated these effects by obtaining two different expert label-sets for the PROSTATEx I challenge training dataset (n = 198) and using them, in addition to an in-house dataset (n = 233), to assess the effect on segmentation performance. The automatic segmentation method we used was nnU-Net.
RESULTS: The selection of training/testing label-set had a significant (p < 0.001) impact on model performance. Furthermore, it was found that model performance was significantly (p < 0.001) higher when the model was trained and tested with the same label-set. Moreover, the results showed that agreement between automatic segmentations was significantly (p < 0.0001) higher than agreement between manual segmentations and that the models were able to outperform the human label-sets used to train them.
CONCLUSIONS: We investigated the impact of label-set selection on the performance of a DL-based prostate segmentation model. We found that the use of different sets of manual prostate gland and zone segmentations has a measurable impact on model performance. Nevertheless, DL-based segmentation appeared to have a greater inter-reader agreement than manual segmentation. More thought should be given to the label-set, with a focus on multicenter manual segmentation and agreement on common procedures.
UNASSIGNED: Label-set selection significantly impacts the performance of a deep learning-based prostate segmentation model. Models using different label-set showed higher agreement than manual segmentations.
CONCLUSIONS: • Label-set selection has a significant impact on the performance of automatic segmentation models. • Deep learning-based models demonstrated true learning rather than simply mimicking the label-set. • Automatic segmentation appears to have a greater inter-reader agreement than manual segmentation.