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Over recent years, video‐surveillance systems have seen extensive adoption, largely driven by security imperatives, with radar‐based speed detection being a common feature in traffic monitoring. Despite its prevalence, broader anomaly detection in traffic patterns has not received equivalent focus. This research develops a sophisticated deep learning framework, drawing architectural inspiration from MobileNet, ResNet50, and VGG19, to not only detect and track vehicles but also analyze trajectory data to identify nonstandard behaviors. Specifically, our model detects four distinct anomalies: overspeeding, lingering in no‐stopping zones, insufficient spacing between vehicles, and violations of traffic light signals. To support this, we constructed a unique dataset comprising over 60,000 video frames. The YOLOv3 algorithm facilitated initial object recognition, which was complemented by data augmentation techniques to mitigate issues related to class imbalance and the limited availability of annotated datasets in this domain. Our enhanced model achieved an overall accuracy of 95%, with a detailed performance breakdown for each detected anomaly. [ABSTRACT FROM AUTHOR]

Background: Breast neuroendocrine neoplasms represent a rare subtype of breast cancer which have not been well studied or characterised, particularly in the metastatic setting. Aim: To present clinicopathological characteristics, treatment and outcomes of a series of patients with metastatic neuroendocrine carcinoma of the breast and review the current literature. Methods: We performed a retrospective review to identify and describe patients with metastatic neuroendocrine carcinoma of the breast at our centre between 2011 and 2021. Medical records, pathology and imaging results were examined to evaluate the clinical and histopathological features as well as the treatment pathways and prognosis of these patients. Results: We present a series of seven female patients with metastatic neuroendocrine carcinoma of the breast, as defined by the World Health Organization classification, over a period of 10 years (2011–2021) from a single centre. Median age at diagnosis was 48 years (range 39–63). Six of seven tissue samples expressed synaptophysin and chromogranin and were also oestrogen and progesterone receptor positive; median Ki‐67 index was 50% (range 20–90%). All seven patients had demonstrated avidity on 18F‐FDG PET imaging, and the six who underwent 68Ga‐DOTATATE PET all had significant avidity. Treatment modalities and sequencing varied, but all patients received chemotherapy during their disease course. Six patients received three or more lines of treatment. Median overall survival was 31.8 months (range 3.7–108.6). Median progression‐free survival (PFS) with first‐line therapy for metastatic disease was 5.8 months (range 1.8–37.8). Conclusions: This series shows the use of multiple modalities in treating this disease, with different sequencing in different patients. Despite multiple modalities used in the first‐line setting, first‐line PFS remains short. Larger series and further molecular characterisation are required to aid clinicians in managing this condition and to guide optimal treatment sequencing to improve outcomes in this rare patient group. [ABSTRACT FROM AUTHOR]

The lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. The outstanding results achieved by deep learning techniques in developing such applications have improved the diagnostic accuracy. Nevertheless, the performance of these models is heavily dependent on the volume of labelled data used for training, which is unfortunately not available. To address this problem, traditional data augmentation is usually adopted. Recently, the emergence of a generative adversarial network (GAN) seems a more plausible solution, where synthetic images are generated. In this work, we have developed a deep generative adversarial network (DGAN) multi-class classifier, which can generate skin problem images by learning the true data distribution from the available images. Unlike the usual two-class classifier, we have developed a multi-class solution, and to address the class-imbalanced dataset, we have taken images from different datasets available online. One main challenge faced during our development is mainly to improve the stability of the DGAN model during the training phase. To analyse the performance of GAN, we have developed two CNN models in parallel based on the architecture of ResNet50 and VGG16 by augmenting the training datasets using the traditional rotation, flipping, and scaling methods. We have used both labelled and unlabelled data for testing to test the models. DGAN has outperformed the conventional data augmentation by achieving a performance of 91.1% for the unlabelled dataset and 92.3% for the labelled dataset. On the contrary, CNN models with data augmentation have achieved a performance of up to 70.8% for the unlabelled dataset. The outcome of our DGAN confirms the ability of the model to learn from unlabelled datasets and yet produce a good diagnosis result. [ABSTRACT FROM AUTHOR]

The use of cannabidiol in electronic liquids (e‐liquids) is becoming increasingly widespread, and the current regulations enforced onto nicotine‐containing e‐liquids are not applicable to cannabidiol‐based products. This has led to concerns about the quality of cannabidiol vapes. Articles investigating the reliability of product labelling were reviewed using systematic review criteria. Of 70 e‐liquids, 77.1% of the e‐liquids tested in the articles were found to have underestimated or overestimated the cannabidiol quantities stated in the product labelling. Statistical analysis confirmed that there was a significant difference between the labelled and analysed cannabidiol concentrations (p < 0.05, Mann‐Whitney U and Wilcoxon Signed Rank). Inaccuracies in received cannabidiol dosages could lead to an increased risk of adverse reactions or limit the therapeutic effect received, highlighting the benefit of enforcing specific regulations on cannabidiol‐based e‐liquids to protect consumer safety and guarantee product efficacy. [ABSTRACT FROM AUTHOR]

Electronic cigarettes (ECs) are thought to be less harmful than traditional combustible cigarettes and were originally intended to help smokers quit. Over the past two decades, they have especially gained popularity with the younger generation. To date, there are over 7000 unique e‐liquid flavours available and over 400 different e‐cigarette brands. The accuracy of nicotine strength labelling in e‐liquids was assessed in this work. Twenty‐three studies from around the world were chosen to assess the level and frequency of nicotine mislabelling in 545 e‐liquid products. Nicotine strengths were most commonly mislabelled by between 5% and 20%, with the majority testing lower than what the label indicated. Fifteen European e‐liquids that were assessed were labelled as 20 mg/ml or less, yet when tested, they contained more than 20 mg/ml of nicotine. One e‐liquid that was supposed to contain no nicotine in fact contained 23.91 mg/ml of nicotine. Furthermore, the difference between the medians of the available labelled and experimental nicotine concentrations was significant (p < 0.001, Wilcoxon signed rank test). Preliminary studies show that high nicotine levels delivered via aerosol increase the risk for nicotine poisoning and cause airway inflammation. Other EC ingredients, such as flavourings, contribute to EVALI and 'popcorn lung'. There is evidence that certain flavourings, such as menthol, reinforce the effects of nicotine and modify drug absorption and metabolism. There is a global need for better quality control in EC products in order to make these safe for consumers. [ABSTRACT FROM AUTHOR]

A review of the literature surrounding the use, analysis, and detection of pesticide material for cannabis cultivation is presented. The use of pesticides in crop cultivation is not new, and cannabis crops are no exception. Studies have found that the use of these are common and that high levels of the pesticides are transferred into the cannabis smoke. The most common pesticide classes associated with cannabis are insecticides, acaricides, and fungicides. Over 350 different pesticide products may be used on cannabis materials and of these, 16 pesticides and three plant growth regulators (PGR) are considered to be the main candidates. Many of the pesticides found in cannabis samples destined for consumption are classed as moderately hazardous by the World Health Organization. Analytical methods for pesticide detection on cannabis are being developed with a view to implementing quality control of cannabis, where it is legal, before being sold. However, no standardized protocol exists. The pesticide levels found in the cannabis samples tested were generally low (less than μg/g), these results do not, however, provide information on chronic low‐dose adverse effects of pesticides in relation to cannabis consumption. Currently no research exists on the toxicity of pyrolyzed pesticides in humans from smoking cannabis. More studies are needed to further understand this potentially harmful health threat. [ABSTRACT FROM AUTHOR]

A graph G has a Ck‐decomposition if its edge set can be partitioned into cycles of length k. We show that if δ(G)≥2∣G∣∕3−1, then G has a C4‐decomposition, and if δ(G)≥∣G∣∕2, then G has a C2k‐decomposition, where k∈N and k≥4 (we assume G is large and satisfies necessary divisibility conditions). These minimum degree bounds are best possible and provide exact versions of asymptotic results obtained by Barber, Kühn, Lo and Osthus. In the process, we obtain asymptotic versions of these results when G is bipartite or satisfies certain expansion properties. [ABSTRACT FROM AUTHOR]

Background: Mass spectrometry‐based metabolomics analyses were performed to examine metabolic changes under diet‐induced obesity in Alzheimer's Disease (AD) and assess whether these changes are reversible with diet modification. Specifically, amino acid metabolism was investigated because amino acid levels are related to obesity/diabetes and elevated levels have been shown to induce many of the pathophysiological hallmarks of AD. Method: Targeted hydrophilic interaction liquid chromatography‐triple quadrupole mass spectrometry (HILIC‐MS/MS) and untargeted reversed‐phase liquid chromatography‐high resolution tandem mass spectrometry (RPLC‐HRMS/MS) assays were developed to analyze the metabolic changes that occur in AD and obesity. Frozen liver samples were obtained from a previously defined study, in which APPSwe/PS1ΔE9 (APP/PSEN1) transgenic mice (to represent familial or early‐onset AD) and wild‐type litter mater controls were fed either a high‐fat diet (HFD, 60% kcal from lard), low‐fat diet (LFD, 10% kcal from lard), or reversal diet (REV, high‐fat for 7.5 months followed by low‐fat for 2.5 months). Liver samples were collected after sacrifice and were prepared through homogenization and an established protein precipitation protocol. Result: Multiple amino acids (including alanine, glutamic acid, leucine, isoleucine, and phenylalanine), carnitines, and members of the fatty acid oxidation pathway were significantly increased in APP/PSEN1 mice on HFD compared to LFD. More substantial effects and changes were observed in the APP/PSEN1 mice than WT mice, suggesting that they were more sensitive to a HFD. These dysregulated peripheral pathways include numerous amino acid pathways and fatty acid beta oxidation and suggest that obesity combined with AD further enhances cognitive impairment. These dysregulated peripheral pathways include pathways directly linked to the TCA cycle and mitochondrial dysfunction, which suggest that the HFD may contribute to AD pathogenesis by further contributing to this mitochondrial dysfunction. Furthermore, partial reversibility of many altered pathways was observed, which highlights that diet change can mitigate metabolic effects of AD. The same trends in individual amino acids were observed in both strategies, highlighting the biological validity of the results. Conclusion: Our targeted and untargeted metabolomics results suggest that numerous peripheral pathways, specifically amino acid metabolism and fatty acid metabolism, were significantly affected in a combinatorial fashion by AD genotype and diet. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Autistic children can experience mental health, social and emotional difficulties. Carol Gray's Social Stories™ are a highly personalised intervention that provide social information in a short individually tailored story.A multi‐site pragmatic cluster randomised controlled trial to evaluate the clinical and cost‐effectiveness of Social Stories™ alongside care as usual in autistic children aged 4–11 years. The primary outcome was the Social Responsiveness Scale‐2 completed by teachers 6 months post‐randomisation, analysed on an intention‐to‐treat basis. Trial Registration: ISRCTN11634810.Eighty‐seven schools, including 249 children, were randomised (intervention 44 schools with 129 children, and usual care 43 schools with 120 children). After 6 months, a reduction of 1.61 points was found on the Social Responsiveness Scale‐2 in the intervention group (95% CI −4.18 to 0.96, p = .220) and for those who attended at least six sessions a reduction of 3.37 points (CACE 95% CI −6.65 to −0.10, p = .043). Children in the intervention group met their individual socio‐emotional goal more frequently than children receiving usual care alone and this was statistically significant. No statistically significant differences were found in other secondary outcomes including anxiety, depression, general health or parental stress.Social Stories™ represent a low‐cost, low‐burden intervention. Benefits are seen in individual socio‐emotional goals but without clinically evident impact on social responsiveness, anxiety, depression, parental stress or general health. [ABSTRACT FROM AUTHOR]