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Smart cities and communities (SCC) constitute a new paradigm in urban development. SCC ideate a data-centered society aimed at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with Internet of Things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, smart traffic control and driver modeling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, availability of data from different stakeholders is necessary. Further, though AI technologies provide accurate predictions and classifications, there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability: models can have difficulty explaining how they came to certain conclusions, so it is difficult for humans to trust them.

Background The use of large language models (LLMs) in healthcare has expanded significantly with advances in natural language processing. Models, such as ChatGPT and Google Gemini, are increasingly used to generate human-like responses to questions, including those posed by patients and their families. With the rise in the incidence of type 1 diabetes (T1D) among children, families frequently seek reliable answers regarding the disease. Previous research has focused on type 2 diabetes, but studies on T1D in a pediatric population remain limited. This study aimed to evaluate and compare the performance and effectiveness of different LLMs when answering common questions about T1D. Methods This cross-sectional, comparative study used questions frequently asked by children with T1D and their parents. Twenty questions were selected from inquiries made to pediatric endocrinologists via social media. The performance of ChatGPT-3.5 ChatGPT-4 ChatGPT-4o was assessed using a standard prompt for each model. The responses were evaluated by five pediatric endocrinologists interested in diabetes using the General Quality Scale (GQS), a 5-point Likert scale, assessing factors such as accuracy, language simplicity, and empathy. Results All five LLMs responded to the 20 selected questions, with their performance evaluated by GQS scores. ChatGPT-4o had the highest mean score (3.78 ± 1.09), while Gemini had the lowest (3.40 ± 1.24). Despite these differences, no significant variation was observed between the models ( p  = 0.103). However, ChatGPT-4o, ChatGPT-4, and Gemini Advanced produced the highest-quality answers compared to ChatGPT-3.5 and Gemini, scoring consistently between 3 and 4 points. ChatGPT-3.5 had the smallest variation in response quality, indicating consistency but not reaching the higher performance levels of other models. Conclusions This study demonstrated that all evaluated LLMs performed similarly in answering common questions about T1D. LLMs such as ChatGPT-4o and Gemini Advanced can provide above-average, accurate, and patient-friendly answers to common questions about T1D. Although no significant differences were observed, the latest versions of LLMs show promise for integration into healthcare, provided they continue to be evaluated and improved. Further research should focus on developing specialized LLMs tailored for pediatric diabetes care.

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above 94% IoU for raindrop detection.

Recombinant growth hormone (GH) is administered as daily subcutaneous injections. Daily treatment can be challenging for children/adolescents, as well as for parents and/or caregivers, such as legal representatives or guardians of children in institutional care. Challenges associated with daily treatment may result in missing several doses but non-adherence with treatment leads to inadequate growth response. As an inadequate growth response does not meet criteria for continuing treatment, payers (commercial or public) may decide to end reimbursement. Novel long-acting GH (LAGH) formulations with extended half-life may be administered less frequently and aim to improve patient convenience and consequently to improve adherence and responses to treatment. LAGH formulations can restore growth velocity and body composition as effectively as daily treatment, without unexpected adverse effects, as reported in randomized clinical trials.

Kenny-Caffey syndrome (KCS) is a rare autosomal recessive (AR)/dominant disease characterized by hypoparathyroidism, skeletal dysplasia, dwarfism, and dysmorphism. or TBCE gene mutations are responsible for this syndrome. Osteocraniostenosis (OCS) is a lethal syndrome with similar features to KCS, and it can be a severe form of KCS type 2 which results from the gene mutation. The mutation is generally characterized by the autosomal dominant transition. We present a male case having compound heterozygous variants (c.976T>A and c.1714_1716del) in the gene with an AR inheritance pattern. Hypocalcemia developed on the second day of life. The patient and his older sister had a dysmorphic face, skeletal dysplasia, and they were diagnosed with hypoparathyroidism. Both siblings died due to septicemia. He is the first reported patient with the mutation in Turkey. The phenotype of the patient is compatible with OCS, and the detected variants may explain the disease genetically.

AimThis study aimed to investigate the relationship between adenoid hypertrophy, the most common cause of obstructive sleep apnea (OSA) in children, with the systemic immune inflammation index (SII) and the pan-immune inflammation value (PIV), and to evaluate the clinical utility of SII and PIV in prognostic and predictive aspects.Materials and MethodsThe retrospective data from 29 patients presenting to the otorhinolaryngology clinic with dyspnea and undergoing adenoidectomy for OSA between June, 2022 and June, 2023 were reviewed. Thirty age- and sex-matched healthy subjects were included as the control group. The preoperative and postoperative 6-month SII and PIV values of both groups were compared.ResultsThere was no significant difference between the groups in terms of age and gender (p>0.05). Platelet SII and PIV were statistically significantly higher in patients in the preoperative period compared to the control group (p<0.05). No significant differences were found in the preoperative neutrophil, lymphocyte, and monocyte counts between the patients and the control subjects (p>0.05). Postoperative neutrophil, platelet, and monocyte counts, as well as the SII and PIV values of the patients, were significantly higher than of those in the control group (p<0.05).ConclusionOur study highlights the potential utility of SII and PIV in assessing systemic inflammation in adenoid hypertrophy-related OSA. However, the unexpected increase in postoperative SII and PIV values underscores the need for further research into their clinical implications.

Neonatal diabetes mellitus (NDM) may be transient or permanent, and the majority is caused by genetic mutations. Early diagnosis is essential to select the patients who will respond to oral treatment. In this investigation, we aimed to present the phenotype and genotype of our patients with NDM and share our experience in a single tertiary center A total of 16 NDM patients from 12 unrelated families are included in the study. The clinical presentation, age at diagnosis, perinatal and family history, consanguinity, gender, hemoglobin A1c, C-peptide, insulin, insulin autoantibodies, genetic mutations, and response to treatment are retrospectively evaluated. The median age at diagnosis of diabetes was five months (4 days-18 months) although six patients with a confirmed genetic diagnosis were diagnosed >6 months. Three patients had mutations, six had mutations, three had mutations, and one had a mutation. All the permanent NDM patients with and mutations were started on sulfonylurea treatment resulting in a significant increase in C-peptide level, better glycemic control, and discontinuation of insulin. Although NDM is defined as diabetes diagnosed during the first six months of life, and a diagnosis of type 1 diabetes is more common between the ages of 6 and 24 months, in rare cases NDM may present as late as 12 or even 24 months of age. Molecular diagnosis in NDM is important for planning treatment and predicting prognosis. Therefore, genetic testing is essential in these patients.

Persistent hypoglycemia in infants with congenital hyperinsulinism (CHI) can be challenging in approximately half of these cases, even after undergoing a near-total pancreatectomy. While maltodextrin has been recommended in the nutritional management of CHI cases younger than six months, information regarding its efficacy in managing hypoglycemia are not yet clear. Here, we present a male infant with CHI who experienced persistent hypoglycemia even after undergoing a near-total pancreatectomy and despite multiple medical treatments. The infant’s hypoglycemic episodes were successfully controlled by adding maltodextrin to his diet.

Non syndromic monogenic obesity is a rare cause of early onset severe obesity in the childhood period. This form may not be distinguishable from other forms of severe obesity without genetic analysis, particularly if patients do not exibit any physical abnormalities or developmental delay. The aim of this study was to screen 41 different obesity-related genes in children with non-syndromic early onset severe obesity. Children with severe (body mass index-standard deviation score >3) and early onset (<7 years) obesity were screened by next-generation sequencing based, targeted DNA custom panel for 41 known-obesity-related genes and the results were confirmed by Sanger technique. Six novel variants were identified in five candidate genes in seven out of 105 children with severe obesity; two in (p.W306C and p.Q36X), one in (p.Y160H), one in (p.W130G fs Ter8), two in (p.D126E) and one in (p.Q4H). Additionally, two previously known variations in were identified in four patients (p.R165W in three, and p.V166I in one). We identified six novel and four previously described variants in six obesity-related genes in 11 out of 105 childrens with early onset severe obesity. The prevalence of monogenic obesity was 10.4% in our cohort.