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Cyberbullying has become more widespread as a result of the common use of social media, particularly among teenagers and young people. A lack of studies on the types of advice and support available to victims of bullying has a negative impact on individuals and society. This work proposes a hybrid model based on transformer models in conjunction with a support vector machine (SVM) to classify our own data set images. First, seven different convolutional neural network architectures are employed to decide which is best in terms of results. Second, feature extraction is performed using four top models, namely, ResNet50, EfficientNetB0, MobileNet and Xception architectures. In addition, each architecture extracts the same number of features as the number of images in the data set, and these features are concatenated. Finally, the features are optimized and then provided as input to the SVM classifier. The accuracy rate of the proposed merged models with the SVM classifier achieved 96.05%. Furthermore, the classification precision of the proposed merged model is 99% in the bullying class and 93% in the non-bullying class. According to these results, bullying has a negative impact on students’ academic performance. The results help stakeholders to take necessary measures against bullies and increase the community’s awareness of this phenomenon.

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Background/objectives: The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal., Methods: This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier., Results: The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45b%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions., Conclusions: The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model's high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support.

The intrinsic nature of tic disorders, characterized by symptom variability and fluctuation, poses challenges in clinical evaluations. Currently, tic assessments predominantly rely on subjective questionnaires administered periodically during clinical visits, thus lacking continuous quantitative evaluation. This study aims to establish an automatic objective measure of tic expression in natural behavioral settings. A custom-developed smartphone application was used to record selfie-videos of children and adolescents with tic disorders exhibiting facial motor tics. Facial landmarks were utilized to extract tic-related features from video segments labeled as either "tic" or "non-tic". These features were then passed through a tandem of custom deep neural networks to learn spatial and temporal properties for tic classification of these segments according to their labels. The model achieved a mean accuracy of 95% when trained on data across all subjects, and consistently exceeded 90% accuracy in leave-one-session-out and leave-one-subject-out cross validation training schemes. This automatic tic identification measure may provide a valuable tool for clinicians in facilitating diagnosis, patient follow-up, and treatment efficacy evaluation. Combining this measure with standard smartphone technology has the potential to revolutionize large-scale clinical studies, thereby expediting the development and testing of novel interventions.

Behavior exhibits a complex spatiotemporal structure consisting of discrete sub-behaviors, or motifs. Continuous behavior data requires segmentation and clustering to reveal these embedded motifs. The popularity of automatic behavior quantification is growing, but existing solutions are often tailored to specific needs and are not designed for the time scale and precision required in many experimental and clinical settings. Here we propose a generalized framework with an iterative approach to refine both segmentation and clustering. Temporally aligned segmentation and clustering (TASC) uses temporal linear alignment to compute distances between and align the recurring behavior motifs in a multidimensional time series, enabling precise segmentation and clustering. We introduce an alternating-step process: evaluation of temporal neighbors against current cluster centroids using linear alignment, alternating with selecting the best non-overlapping segments and their subsequent re-clustering. The framework is evaluated on semi-synthetic and real-world experimental and clinical data, demonstrating enhanced segmentation and clustering, offering a better foundation for consequent research. The framework may be used to extend existing tools in the field of behavior research and may be applied to other domains requiring high precision of time series segmentation., (© 2024. The Author(s).)

Lung cancer is a major public health problem and a leading cause of cancer-related deaths worldwide. Despite advances in treatment options, the five-year survival rate for lung cancer patients remains low, emphasizing the urgent need for innovative diagnostic and therapeutic strategies. MicroRNAs (miRNAs) have emerged as potential biomarkers and therapeutic targets for lung cancer due to their crucial roles in regulating cell proliferation, differentiation, and apoptosis. For example, miR-34a and miR-150, once delivered to lung cancer via liposomes or nanoparticles, can inhibit tumor growth by downregulating critical cancer promoting genes. Conversely, miR-21 and miR-155, frequently overexpressed in lung cancer, are associated with increased cell proliferation, invasion, and chemotherapy resistance. In this review, we summarize the current knowledge of the roles of miRNAs in lung carcinogenesis, especially those induced by exposure to environmental pollutants, namely, arsenic and benzopyrene, which account for up to 1/10 of lung cancer cases. We then discuss the recent advances in miRNA-based cancer therapeutics and diagnostics. Such information will provide new insights into lung cancer pathogenesis and innovative diagnostic and therapeutic modalities based on miRNAs.

Background: Per- and polyfluoroalkyl substances (PFAS) have been associated with higher cholesterol and liver function markers in some studies, but the evidence for specific cardiometabolic conditions has been inconclusive., Objectives: We quantified the associations of single and combined PFAS with cardiometabolic markers and conditions in a cross-sectional study of three Australian communities with PFAS-contaminated water from the historical use of aqueous film-forming foam in firefighting activities, and three comparison communities., Methods: Participants gave blood samples for measurement of nine PFAS, four lipids, six liver function markers, and completed a survey on sociodemographic characteristics and eight cardiometabolic conditions. We estimated differences in mean biomarker concentrations per doubling in single PFAS concentrations (linear regression) and per interquartile range increase in the PFAS mixture (Bayesian kernel machine regression). We estimated prevalence ratios of biomarker concentrations outside reference limits and self-reported cardiometabolic conditions (Poisson regression)., Results: We recruited 881 adults in exposed communities and 801 in comparison communities. We observed higher mean total cholesterol with higher single and mixture PFAS concentrations in blood serum (e.g., 0.18 mmol/L, 95% credible interval -0.06 to 0.42, higher total cholesterol concentrations with an interquartile range increase in all PFAS concentrations in Williamtown, New South Wales), with varying certainty across communities and PFAS. There was less consistency in direction of associations for liver function markers. Serum perfluorooctanoic acid (PFOA) concentrations were positively associated with the prevalence of self-reported hypercholesterolemia in one of three communities, but PFAS concentrations were not associated with self-reported type II diabetes, liver disease, or cardiovascular disease., Discussion: Our study is one of few that has simultaneously quantified the associations of blood PFAS concentrations with multiple biomarkers and cardiometabolic conditions in multiple communities. Our findings for total cholesterol were consistent with previous studies; however, substantial uncertainty in our estimates and the cross-sectional design limit causal inference., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Professor Martyn Kirk worked part-time for the Australian Government Department of Health between 2020 and 2022 on the Australian national COVID-19 response., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Environmental chemical contamination is a recognised risk factor for psychological distress, but has been seldom studied in the context of per- and polyfluoroalkyl substances (PFAS) contamination. We examined psychological distress in a cross-sectional study of three Australian communities exposed to PFAS from the historical use of aqueous film-forming foam in firefighting activities, and three comparison communities without environmental contamination., Methods: Participation was voluntary following recruitment from a PFAS blood-testing program (exposed) or random selection (comparison). Participants provided blood samples and completed a survey on their exposure history, sociodemographic characteristics, and four measures of psychological distress (Kessler-6, Distress Questionnaire-5, Patient Health Questionnaire-15, and Generalised Anxiety Disorder-7). We estimated prevalence ratios (PR) of clinically-significant psychological distress scores, and differences in mean scores: (1) between exposed and comparison communities; (2) per doubling in PFAS serum concentrations in exposed communities; (3) for factors that affect the perceived risk of living in a community exposed to PFAS; and (4) in relation to self-reported health concerns., Results: We recruited 881 adults in exposed communities and 801 in comparison communities. We observed higher levels of self-reported psychological distress in exposed communities than in comparison communities (e.g., Katherine compared to Alice Springs, Northern Territory: clinically-significant anxiety scores, adjusted PR = 2.82, 95 % CI 1.16-6.89). We found little evidence to suggest that psychological distress was associated with PFAS serum concentrations (e.g., Katherine, PFOS and anxiety, adjusted PR = 0.85, 95 % CI 0.65-1.10). Psychological distress was higher among exposed participants who were occupationally exposed to firefighting foam, used bore water on their properties, or were concerned about their health., Conclusion: Psychological distress was substantially more prevalent in exposed communities than in comparison communities. Our findings suggest that the perception of risks to health, rather than PFAS exposure, contribute to psychological distress in communities with PFAS contamination., Competing Interests: Declaration of competing interest MDK worked part-time for the Australian Government Department of Health between 2020 and 2022 on the Australian national COVID-19 response team. The other authors declare that they have no competing interests., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

One of the promising opportunities of digital health is its potential to lead to more holistic understandings of diseases by interacting with the daily life of patients and through the collection of large amounts of real-world data. Validating and benchmarking indicators of disease severity in the home setting is difficult, however, given the large number of confounders present in the real world and the challenges in collecting ground truth data in the home. Here we leverage two datasets collected from patients with Parkinson's disease, which couples continuous wrist-worn accelerometer data with frequent symptom reports in the home setting, to develop digital biomarkers of symptom severity. Using these data, we performed a public benchmarking challenge in which participants were asked to build measures of severity across 3 symptoms (on/off medication, dyskinesia, and tremor). 42 teams participated and performance was improved over baseline models for each subchallenge. Additional ensemble modeling across submissions further improved performance, and the top models validated in a subset of patients whose symptoms were observed and rated by trained clinicians., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YG serves as scientific advisor for Eli Lilly and Company and Merck & Co.; serves as scientific advisor and receives grants from Merck KGaA. YH has grant funding through BBJ from Sanofi S.A. and UCB for unrelated projects. BBJ has grant funding from Sanofi S.A. and UCB for unrelated projects. AJ is funded by MJ Fox Foundation for data curation. All other authors report no competing interests., (Copyright: © 2023 Sieberts et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Morbidity and mortality in rheumatoid arthritis (RA) is partly mitigated by maintaining immune and hematologic homeostasis. Identification of those at risk is challenging. Red cell distribution width (RDW) and absolute lymphocyte count (ALC) associate with cardiovascular disease (CVD) and mortality in the general population, and with disease activity in RA. How these variables relate to inflammation and mortality in RA was investigated., Methods: In a retrospective single Veterans Affairs (VA) Rheumatology Clinic cohort of 327 patients with RA treated with methotrexate (MTX)+/- a tumor necrosis factor (TNF) inhibitor (TNFi), we evaluated RDW and ALC before and during therapy and in relation to subsequent mortality. Findings were validated in a national VA cohort (n = 13,914). In a subset of patients and controls, we evaluated inflammatory markers., Results: In the local cohort, high RDW and low ALC prior to MTX treatment was associated with subsequent mortality over 10 years (both P < 0.001). The highest mortality was observed in those with both high RDW and low ALC. This remained after adjusting for age and comorbidities and was validated in the national RA cohort. In the immunology cohort, soluble and cellular inflammatory markers were higher in patients with RA than in controls. ALC correlated with age, plasma TNF receptor II, natural killer HLA-DR mean fluorescence intensity, and CD4CM/CD8CM HLA-DR/CD38%, whereas RDW associated with age and ALC. MTX initiation was followed by an increase in RDW and a decrease in ALC. TNFi therapy added to MTX resulted in an increase in ALC., Conclusion: RDW and ALC before disease-modifying antirheumatic drug therapy are associated with biomarkers of monocyte/macrophage inflammation and subsequent mortality. The mechanistic linkage between TNF signaling and lymphopenia found here warrants further investigation., (Copyright © 2023 by the Journal of Rheumatology.)

According to many studies, addressing the religious and spiritual (R/S) needs of patient's increase patient satisfaction. One area of interest is how patient self-perceived level of religiosity and spirituality (R/S) influences hospital needs. In this cross-sectional study, 195 inpatients at a non-faith-based academic hospital in Toledo, OH, USA completed surveys examining self-perceived R/S levels, as well as how those R/S levels impacted preferred services, conversations, and experiences in the hospital. Patients with no religious identity (self-identified as atheist, agnostic, or no religion) were less likely to report discussions about R/S needs than religious respondents (16.7% vs. 47.3%, p = 0.039). Nevertheless, such patients were just as likely to want a R/S conversation started by their healthcare provider (75% vs. 56%, p = 0.241). Those with no R/S identity were more likely to report presumed negative assumptions by hospital staff (25% vs. 0%, p < 0.001). Our data suggests that even for a nonreligious population, it is important to consider R/S needs., (© 2020. Springer Science+Business Media, LLC, part of Springer Nature.)

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded more than 210 countries around the world and as of February 18 th , 2021, just after a year has passed, a total of 110,533,973 confirmed cases of COVID-19 were reported and its death toll reached about 2,443,091. COVID-19 is a new member of the family of corona viruses, its nature, behaviour, transmission, spread, prevention, and treatment are to be investigated. Generally, a huge amount of data is accumulating regarding the COVID-19 pandemic, which makes hot research topics for machine learning researchers. However, the panicked world's population is asking when the COVID-19 will be over? This study considered machine learning approaches to predict the spread of the COVID-19 in many countries. The experimental results of the proposed model showed that the overall R2 is 0.99 from the perspective of confirmed cases. A machine learning model has been developed to predict the estimation of the spread of the COVID-19 infection in many countries and the expected period after which the virus can be stopped. Globally, our results forecasted that the COVID-19 infections will greatly decline during the first week of September 2021 when it will be going to an end shortly afterward., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Motor tics are sudden, rapid, recurrent movements that are the key symptoms of Tourette syndrome and other tic disorders. The pathophysiology of tic generation is associated with abnormal inhibition of the basal ganglia, particularly its primary input structure, the striatum. In animal models of both rodents and non-human primates, local application of GABAA antagonists, such as bicuculline and picrotoxin, into the motor parts of the striatum induces local disinhibition resulting in the expression of motor tics. Here, we present acute and chronic models of motor tics in rats. In the acute model, bicuculline microinjections through a cannula implanted in the dorsal striatum elicit the expression of tics lasting for short time periods of up to an hour. The chronic model is an alternative enabling the extension of tic expression to periods of several days or even weeks, utilizing continuous infusion of bicuculline via a sub-cutaneous mini-osmotic pump. The models enable the study of the behavioral and neural mechanisms of tic generation throughout the cortico-basal ganglia pathway. The models support the implantation of additional recording and stimulation devices in addition to the injection cannulas, thus allowing for a wide variety of usages such as electrical and optical stimulation and electrophysiological recordings. Each method has different advantages and shortcomings: the acute model enables the comparison of the kinematic properties of movement and the corresponding electrophysiological changes before, during and after tic expression and the effects of short-term modulators on tic expression. This acute model is simple to establish; however, it is limited to a short period of time. The chronic model, while more complex, makes feasible the study of tic dynamics and behavioral effects on tic expression over prolonged periods. Thus, the type of empirical query drives the choice between these two complementary models of tic expression.

Motor tics, the hallmark of Tourette syndrome (TS), are modulated by different behavioral and environmental factors. A major modulating factor is the sleep-wake cycle in which tics are attenuated to a large extent during sleep. This study demonstrates a similar reduction in tic expression during sleep in an animal model of chronic tic disorders and investigates the underlying neural mechanism. We recorded the neuronal activity during spontaneous sleep-wake cycles throughout continuous GABA A antagonist infusion into the striatum. Analysis of video streams and concurrent kinematic assessments indicated tic reduction during sleep in both frequency and intensity. Extracellular recordings in the striatum revealed a state-dependent dissociation between motor tic expression and their macro-level neural correlates ("LFP spikes") during the sleep-wake cycle. Local field potential (LFP) spikes, which are highly correlated with tic expression during wakefulness, persisted during tic-free sleep and did not change their properties despite the reduced behavioral expression. Local, micro-level, activity near the infusion site was time-locked to the LFP spikes during wakefulness, but this locking decreased significantly during sleep. These results suggest that whereas LFP spikes encode motor tic generation and feasibility, the behavioral expression of tics requires local striatal neural activity entrained to the LFP spikes, leading to the propagation of the activity to downstream targets and consequently their motor expression. These findings point to a possible mechanism for the modulation of tic expression in patients with TS during sleep and potentially during other behavioral states., Competing Interests: The authors declare no competing financial interests., (© 2021 The Author(s).)

The entopeduncular nucleus is one of the basal ganglia's output nuclei, thereby controlling basal ganglia information processing. Entopeduncular nucleus neurons integrate GABAergic inputs from the Striatum and the globus pallidus, together with glutamatergic inputs from the subthalamic nucleus. We show that endocannabinoids and dopamine interact to modulate the long-term plasticity of all these primary afferents to the entopeduncular nucleus. Our results suggest that the interplay between dopamine and endocannabinoids determines the balance between direct pathway (striatum) and indirect pathway (globus pallidus) in entopeduncular nucleus output. Furthermore, we demonstrate that, despite the lack of axon collaterals, information is transferred between neighboring neurons in the entopeduncular nucleus via endocannabinoid diffusion. These results transform the prevailing view of the entopeduncular nucleus as a feedforward "relay" nucleus to an intricate control unit, which may play a vital role in the process of action selection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Gorodetski, Loewenstern, Faynveitz, Bar-Gad, Blackwell and Korngreen.)

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public's concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and  Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios., Competing Interests: Conflict of interestThe authors declare that there is no conflict of interest., (© Springer-Verlag London Ltd., part of Springer Nature 2020.)

Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases., Competing Interests: The authors declare that there is no conflict of interest, (© 2020 Elsevier Ltd. All rights reserved.)

Tourette syndrome (TS) and attention deficit hyperactivity disorder (ADHD) are two neurodevelopmental hyper-behavioral disorders that are highly comorbid. The source of this comorbidity and the neuronal mechanisms underlying these disorders are still unclear. We examined the neuronal activity of freely behaving rats before and after striatal disinhibition, to reveal the similar and distinct neuronal components underlying the mechanisms of TS-like and ADHD-like symptom expression. Focal disinhibition induced motor tics, locomotor hyperactivity or a comorbid effect depending on the location of the injection within the different functional domains of the striatum. While injections within the motor domain induced motor tics, injections into the limbic domain induced mainly locomotor hyperactivity. Disinhibition, regardless of its striatal location, led to qualitatively similar macro-scale and micro-scale neuronal changes. These changes were localized to the domain of the manipulation and remained partly segregated, indicating that hyperactivity is induced as a result of changes in the limbic domain without directly activating the motor domain. Despite the general similarity of induced neuronal changes, these changes were associated with different behavioral effects and were more stereotypic and pronounced following motor-domain disinhibition in comparison to limbic-domain disinhibition. Our recordings revealed a disparity in the neuronal input-output transformation of the two models of the disorders. The results suggest that tic expression and hyperactivity states share similar local neuronal activity changes which manifest in different neuronal and behavioral outcomes. These results expose an intriguing link between tics and their comorbid symptoms and hint at striatal disinhibition, resulting from GABAergic alterations, as a potential common mechanism underlying distinct symptoms expressed by hyper-behavioral patients., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Computational models are crucial to studying the encoding of individual neurons. Static models are composed of a fixed set of parameters, thus resulting in static encoding properties that do not change under different inputs. Here, we challenge this basic concept which underlies these models. Using generalized linear models, we quantify the encoding and information processing properties of basal ganglia neurons recorded in-vitro. These properties are highly sensitive to the internal state of the neuron due to factors such as dependency on the baseline firing rate. Verification of these experimental results with simulations provides insights into the mechanisms underlying this input-dependent encoding. Thus, static models, which are not context dependent, represent only part of the neuronal encoding capabilities, and are not sufficient to represent the dynamics of a neuron over varying inputs. Input-dependent encoding is crucial for expanding our understanding of neuronal behavior in health and disease and underscores the need for a new generation of dynamic neuronal models.