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This toxicogenomic study was conducted to predict (post)menopausal human health effects of commercial soy supplementation using ovariectomized rats as a model. Different target tissues (i.e. breast, uterus and sternum) and non-target tissues (i.e. peripheral blood mononuclear cells (PBMC), adipose and liver) of ovariectomized F344 rats exposed to a commercially available soy supplement for eight weeks, were investigated. Changes in gene expression in these tissues were analysed using whole-genome microarray analysis. No correlation in changes in gene expression were observed among different tissues, indicating tissue specific effects of soy isoflavone supplementation. Out of 87 well-established estrogen responsive genes (ERGs), only 19 were found to be significantly regulated (p

Skin cancer, a prevalent malignancy worldwide, poses significant health concerns owing to its increasing incidence. Autophagy, a natural cellular process, is a pivotal event in skin cancer and has advantageous and detrimental effects. This duality has prompted extensive investigations into medical interventions targeting autophagy modulation for their substantial therapeutic potential. This systematic review aimed to investigate the relationship between skin cancer and autophagy and the contribution and mechanism of autophagy modulators in skin cancer. We outlined the effectiveness and safety of targeting autophagy as a promising therapeutic strategy for the treatment of skin cancer. This comprehensive review identified a diverse array of autophagy modulators with promising potential for the treatment of skin cancer. Each of these compounds demonstrates efficacy through distinct physiological mechanisms that have been elucidated in detail. Interestingly, findings from a literature search indicated that none of the natural, synthetic, or semisynthetic compounds exhibited notable adverse effects in either human or animal models. Consequently, this review offers novel mechanistic and therapeutic perspectives on the targeted modulation of autophagy in skin cancer.

Muir-Torre syndrome (MTS) is a rare genodermatosis, diagnosed by the presence of sebaceous neoplasms along with an internal malignancy, most commonly colorectal carcinomas. MTS is most commonly caused by microsatellite instabilities of the hMLH1 and hMSH2 mismatch repair genes, and is rarely caused by mutations of the hMSH6 gene. We describe the case of a 56-year-old male who presented with an enlarging mass on his back as well as hematochezia. The back mass was excised, and pathology confirmed microsatellite instability in MSH2 and MSH6. Abdominal CT and colonoscopy confirmed the presence of synchronous masses in the cecum, ascending colon, and the transverse colon. He refused any further workup or treatment, only to return 8 months later complaining of hematochezia and discomfort due to an enlarging mass protruding from the rectum. After consenting to surgical intervention, he agreed to outpatient chemotherapy treatment. The presence of sebaceous neoplasms should raise suspicion for the possibility of an associated internal malignancy.

The health effects of soy supplementation in (post)menopausal women are still a controversial issue. The aim of the present study was to establish the effect of the soy isoflavones (SIF) present in a commercially available supplement on ovariectomized rats and to investigate whether these rats would provide an adequate model to predict effects of SIF in (post)menopausal women. Two dose levels (i.e. 2 and 20 mg/kg b.w.) were used to characterize plasma bioavailability, urinary and fecal concentrations of SIF and changes in gene expression in peripheral blood mononuclear cells (PBMC). Animals were dosed at 0 and 48 h and sacrificed 4 h after the last dose. A clear dose dependent increase of SIF concentrations in plasma, urine and feces was observed, together with a strong correlation in changes in gene expression between the two dose groups. All estrogen responsive genes and related biological pathways (BPs) that were affected by the SIF treatment were regulated in both dose groups in the same direction and indicate beneficial effects. However, in general no correlation was found between the changes in gene expression in rat PBMC with those in PBMC of (post)menopausal women exposed to a comparable dose of the same supplement. The outcome of this short-term study in rats indicates that the rat might not be a suitable model to predict effects of SIF in humans. Although the relative exposure period in this rat study is comparable with that of the human study, longer repetitive administration of rats to SIF may be required to draw a final conclusion on the suitability of the rat a model to predict effects of SIF in humans.

Abstract Background The ongoing global crisis of Higher Education (HE) institutions during the post-COVID-19 pandemic period has increased the likelihood of enduring psychological stressors for staff. This study aimed to identify factors associated with job insecurity, burnout, psychological distress and coping amongst staff working at HE institutions globally. Methods An anonymous cross-sectional study was conducted in 2023 with staff at HE institutions across 16 countries. Job insecurity was measured using the Job Insecurity Scale (JIS), burnout using the Perceived Burnout measure question, psychological distress using the Kessler Psychological Distress Scale (K10), and coping using the Brief Resilient Coping Scale. Multivariable logistic regression with a stepwise variable selection method was used to identify associations. Results A total of 2,353 staff participated; the mean age (± SD) was 43(± 10) years and 61% were females. Most staff (85%) did not feel job insecurity, one-third (29%) perceived burnout in their jobs, more than two-thirds (73%) experienced moderate to very high levels of psychological distress, and more than half (58%) exhibited medium to high resilient coping. Perceived job insecurity was associated with staff working part-time [Adjusted Odds Ratio 1.53 (95% Confidence Intervals 1.15–2.02)], having an academic appointment [2.45 (1.78–3.27)], having multiple co-morbidities [1.86 (1.41–2.48)], perceived burnout [1.99 (1.54–2.56)] and moderate to very high level of psychological distress [1.68 (1.18–2.39)]. Perceived burnout was associated with being female [1.35 (1.12–1.63)], having multiple co-morbidities [1.53 (1.20–1.97)], perceived job insecurity [1.99 (1.55–2.57)], and moderate to very high levels of psychological distress [3.23 (2.42–4.30)]. Staff with multiple co-morbidities [1.46 (1.11–1.92)], mental health issues [2.73 (1.79–4.15)], perceived job insecurity [1.61 (1.13–2.30)], and perceived burnout [3.22 (2.41–4.31)] were associated with moderate to very high levels of psychological distress. Staff who perceived their mental health as good to excellent [3.36 (2.69–4.19)] were more likely to have medium to high resilient coping. Conclusions Factors identified in this study should be considered in reviewing and updating current support strategies for staff at HE institutions across all countries to reduce stress and burnout and improve wellbeing.

Objective Brain tumors are a leading global cause of mortality, often leading to reduced life expectancy and challenging recovery. Early detection significantly improves survival rates. This paper introduces an efficient deep learning model to expedite brain tumor detection through timely and accurate identification using magnetic resonance imaging images. Methods Our approach leverages deep transfer learning with six transfer learning algorithms: VGG16, ResNet50, MobileNetV2, DenseNet201, EfficientNetB3, and InceptionV3. We optimize data preprocessing, upsample data through augmentation, and train the models using two optimizers: Adam and AdaMax. We perform three experiments with binary and multi-class datasets, fine-tuning parameters to reduce overfitting. Model effectiveness is analyzed using various performance scores with and without cross-validation. Results With smaller datasets, the models achieve 100% accuracy in both training and testing without cross-validation. After applying cross-validation, the framework records an outstanding accuracy of 99.96% with a receiver operating characteristic of 100% on average across five tests. For larger datasets, accuracy ranges from 96.34% to 98.20% across different models. The methodology also demonstrates a small computation time, contributing to its reliability and speed. Conclusion The study establishes a new standard for brain tumor classification, surpassing existing methods in accuracy and efficiency. Our deep learning approach, incorporating advanced transfer learning algorithms and optimized data processing, provides a robust and rapid solution for brain tumor detection.

This study quantified hazardous heavy metals (Cu, Cr, and Pb) in soil and vegetables (potato, tomato, pepper, cauliflower, and cabbage) across six upazilas (Kahaloo, Bogura Sadar, Shajahanpur, Shibganj, Nandigram, and Dupchanchia) in Bogura district, Bangladesh, assessing their health and environmental impacts. The detection method was validated for its accuracy and precision with QC samples. Results indicated that Cu levels in all samples were within safe limits set by BFSA and FAO/WHO, whereas Cr and Pb in vegetables exceeded permissible levels, though soil concentrations remained within limits. Pb contamination was particularly severe in vegetables (CF > 6), and all vegetables showed significant contamination degrees (CD), highlighting extensive heavy metal pollution. The Pollution Load Index (PLI) identified Kahaloo and Bogura Sadar as the most polluted, whereas Nandigram and Dupchanchia were the least. Bioaccumulation factors (BF) for all metals were 10 −4 , HI > 1) across all upazilas. The findings underscore the urgent need for measures to mitigate heavy metal pollution in these areas to safeguard environmental and public health.

ABSTRACT Background Squamous cell carcinoma (SCC) is a rare malignancy of invasive epithelium with keratinocyte differentiation, and it is the most common form of eyelid malignant neoplasm, comprising 5%–10% of malignancies. While SCC rarely affects the orbit, it may be involved through local invasion from a cutaneous primary site or extension by perineural invasion. Only 12 cases of primary orbital SCC have been reported until now. Here, we present a case of primary carcinoma of the right orbit with coexisting Carney's syndrome, a rare genetic disorder associated with multiple endocrine neoplasias (MEN) syndromes. Case A 62‐year‐old South Asian male presented with a painful swelling in the lateral aspect of the right eyebrow and protrusion of the eyeball in August 2020. He had a history of excision of Right atrial Myxoma in March 2020. Orbital computerized tomography (CT) and positron emission tomography (PET‐CT) scans revealed an enhancing soft tissue lesion in the right orbit with the involvement of frontal and ethmoid sinuses. Biopsy confirmed HPV‐related poorly differentiated SCC, positive for HPV‐related markers. The patient received concurrent chemo irradiation with Cisplatin. Follow‐up PET‐CT done 3 months later showed a new lesion appeared in the right orbital region and right lobe of thyroid. Later had surgical excision and total thyroidectomy, and histopathological examination (HPE) from orbit was reported as invasive SCC and from the thyroid was reported as synchronous papillary thyroid cancer. The patient's proptosis resolved, and subsequent PET‐CT and magnetic resonance imaging (MRI) scans did not show any residual or recurrent disease. Conclusion Primary SCC of the orbit is an extremely rare disease, and this case report presents the 13th reported case and the first one associated with Carney's syndrome. As there is no standard treatment regimen for primary SCC of the orbit, this case highlights the use of multimodality treatment, including surgical excision and chemo irradiation. The findings emphasize the importance of early detection and management of this uncommon and life‐threatening condition, providing hope for patients and aiding in the prevention of recurrence.

The underground water pipeline system is a crucial infrastructure that largely remains out of sight. However, it is the source of a clean and uninterrupted flow of water for our everyday lives. Various factors, including corrosion, material degradation, ground movement, and improper maintenance, cause pipe leaks, a silent crisis that causes an estimated 39 billion dollars of loss every year. Prompt leakage detection and localization can help reduce the loss. This research investigates the potential of two machine learning models as supporting tools for surveying extensive areas to identify and pinpoint the location of underground leaks. The presented combined approach ensures the speed and accuracy of the leakage survey. The first machine learning model is a hybrid ML model that employs thermal imaging to identify subterranean water leakage. It relies on detecting thermal anomalies and distinctive signatures associated with water leakage to identify and locate underground water leakage. The developed model can detect up to 750 mm underground leakage with 95.20 % accuracy. The second model uses binaural audio from geophones to localize the leakage position. The model utilizes interaural time difference and interaural phase difference for localization purposes, and the 1D-CNN network delivers an angle in twenty-degree increments with an accuracy of 88.19 %. Large-scale implementation of the proposed model could be a powerful catalyst to reduce water loss in the water supply system.

The complete mitochondrial D-loop region was sequenced for a total of 18 individuals of Bangladeshi native chickens (BNC); full feathered (nana) (n＝7), naked neck (Nana) (n＝8) and Red junglefowl (RJF) (n＝3). The alignment of mitochondrial D-loop sequence of these chicken populations with 39 reference sequences from DNA databank; White Leghorn, G. g. murghi, G. g. bankiva, G. g. spadiceus, G. g. gallus and other Asiatic chickens was done to identify the phylogenetic position of BNC for the conservation and improvement of chicken genetic resource. The nucleotide variation of sequence among haplotypes for within and between populations of BNC supported the phenotypic variation of individual of the populations. Phylogenetic analysis showed that 57 individuals were grouped into 6 clades. Of the BNC populations, 6 nana, 7 Nana and 2 RJF individuals (83.3%) were closely related with each other and only 1 nana and 1 Nana (11%), and 1 RJF (5.5%) individuals were divergent from them. Therefore, the phylogenetic tree showed low genetic distance and close relationship within and between the chicken populations of Bangladesh, which were closely related with G. g. murghi of Indian origin, and also related with G. g. bankiva, G. g. gallus implying the origin of gene flow to Bangladesh. The genetic information from this study may serve as an initial step to make future plans to assess more molecular information on genetic diversity for the characterization, conservation and improvement of valuable chicken genetic resource of Bangladesh.

The aim of the present review is to assess the potential usefulness of crossbred chickens in tropical environment. Poultry is a promising and emerging sector for poverty alleviation as well as an animal protein source in Bangladesh. Poultry production is yet lies in rural scavenging poultry in tropical country. Crossbreds are reared in scavenging, semi-intensive or intensive systems resulting in birds with good adaptability to tropical climate, highly resistant to disease and performing even better than pure exotic or indigenous chickens. Better growth performances are determined in indigenous naked neck (D. Nana) with Rhode Island Red (RIR), White Leghorn (WLH) or Fayoumi crossbreds in comparison with pure exotic, indigenous or other crossbreds. With respect to egg production, WLH×Fayoumi, RIR×Fayoumi, RIR×WLH and Fayoumi×WLH appear to be suitable combinations. However, crossbreds of D. Nana with RIR or Fayoumi produce more eggs than that of RIR or Fayoumi under scavenging conditions in a tropical climate. The best quality egg is found in WLH chickens but the highest egg shell thickness is found in D. Nana which affects hatchability of eggs. Of the above crosses, RIR×Fayoumi and their reciprocal crosses are found to be the best for fertility and hatchability. Accordingly D. Nana crossed with RIR or WLH or D. nana (indigenous full feathered chicken) results in improved fertility and hatchability of eggs. Although D. Nana crossed with an exotic broiler strain performs the best for meat yield traits, crossbred of D. Nana with RIR, WLH or Fayoumi improve meat yield traits. Therefore, the present review reveals that crossbreds of RIR×Fayoumi or D. Nana cockerels with RIR, WLH and Fayoumi hens may be considered for poultry production in tropical climate. This review emphasizes the use of D. Nana and its crosses with RIR, WLH or Fayoumi for their suitability in tropical regions.

This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our model capitalized on CNN's proficiency in feature extraction and LSTM's prowess in classifying seizure. The key strength of the NeuroWave-Net lies in its ability to combine these distinct architectures, synergizing their capabilities for enhanced accuracy in identifying seizure conditions within EEG data. Our proposed model exhibited outstanding performance, achieving a classification accuracy of 99.48%. This study contributed to the advancement of seizure classification models, providing a robust and streamlined approach for accurate categorization within EEG datasets. NeuroWave-Net stands as a testament to the potential of hybrid neural network architectures in neurological diagnostics.

The objective of the study is to examine the relationship between knowledge, religiosity and experience, and the behavior of employee provident fund (EPF) investors investing in Islamic and conventional unit trusts. The variables are extracted from the literature to achieve the objective. Additionally, 125 valid responses were composed through surveys of Malaysian government and private employees. The Statistical Package for Social Sciences has been applied to analyze the collected data, using correlation analysis techniques. This research found that knowledge about Islamic unit trusts and religious beliefs have a positive significant relationship with the EPF investors’ behavior of investing in Islamic unit trusts. Moreover, the experience of investing in Islamic and conventional unit trusts is also positively and significantly associated with the EPF investors’ behavior of investing in Islamic and conventional unit trusts, respectively. As research in this area is insufficient, this study will play a vital role for the unit trust industry in exploiting the financial strategies that are highly and positively observed by Malaysian EPF investors.

Abstract Background The lack of appropriate prognostic biomarkers remains a significant obstacle in the early detection of Head and Neck Squamous Cell Carcinoma (HNSCC), a cancer type with a high mortality rate. Despite considerable advancements in treatment, the success in diagnosing HNSCC at an early stage still needs to be improved. Nuclear factor erythroid 2-related factor 2 (Nrf2) and Sonic Hedgehog (Shh) are overexpressed in various cancers, including HNSCC, and have recently been proposed as possible therapeutic targets for HNSCC. Circulating Tumor Cell (CTC) is a novel concept used for the early detection of cancers, and studies have suggested that a higher CTC count is associated with the aggressiveness of HNSCC and poor survival rates. Therefore, we aimed to establish molecular markers for the early diagnosis of HNSCC considering Shh/Nrf2 overexpression in the background. In addition, the relation between Shh/Nrf2 and CTCs is still unexplored in HNSCC patients. Methods In the present study, we selected a cohort of 151 HNSCC patients and categorized them as CTC positive or negative based on the presence or absence of CTCs in their peripheral blood. Data on demographic and clinicopathological features with the survival of the patients were analyzed to select the patient cohort to study Shh/Nrf2 expression. Shh and Nrf2 expression was measured by qRT-PCR. Results Considering significant demographic [smoking, betel leaf (p-value

Abstract Aims Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non‐invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non‐invasive biomarker to classify healthy adults and those with heart failure. Methods and results We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre‐processing, we performed time‐domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five‐fold cross‐validation using four machine learning classification algorithms: support vector machine, k‐nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. Conclusions The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non‐invasive HRV, to screen congestive heart failure individuals in primary care.

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL.

Objective Theoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults. Methods The COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration. Result A fluid analogy of the COM-B system is presented. Conclusions The proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change interventions.

Background: The coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has detrimental effects on physical and mental health. Patients with severe mental illness are at higher risk of contracting the virus due to social determinants of health. Vulnerable populations include the elderly, people with pre-existing conditions, and those exposed to SARS-CoV-2. Unfortunately, only a few countries have updated vaccination strategies to prioritize patients with mental illnesses. Therefore, we aimed to explore whether individuals with mental disorders are prioritized in vaccine allocation strategies in different world regions. They are often neglected in policymaking but are highly vulnerable to the threatening complications of COVID-19. Methods: A questionnaire was developed to record details regarding COVID-19 vaccination and prioritizations for groups of persons with non-communicable diseases (NCDs), mental disorders, and substance use disorders (SUDs). NCDs were defined according to the WHO as chronic diseases that are the result of a combination of genetic, physiological, environmental, and behavioral factors such as cardiovascular diseases, cancer, respiratory diseases, and diabetes. Results: Most countries surveyed (80%) reported healthcare delivery via a nationalized health service. It was found that 82% of the countries had set up advisory groups, but only 26% included a mental health professional. Most frequently, malignancy (68%) was prioritized followed by diabetes type 2 (62%) and type 1 (59%). Only nine countries (26%) prioritized mental health conditions. Conclusion: The spread of the coronavirus has exposed both the strengths and flaws of our healthcare systems. The most vulnerable groups suffered the most and were hit first and faced most challenges. These findings raise awareness that patients with mental illnesses have been overlooked in immunization campaigns. The range of their mortality, morbidity, and quality of life could have widened due to this delay.

Abstract Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person’s life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model’s transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

The neuron is sometimes referred to as the “head” or “central” cell of the nervous system since it has the ability to communicate with other neurons or cells via electrical impulses. The hardware realization and simulation of these neurons are critical in neuromorphic engineering. In this paper, we made a device that generates 4 different spiking patterns of the nervous system as a Spike Generator (SG) using a hybrid approximation of the target model called the Piece-Wised Power-2 Based Izhikevich Model (PWP2BIM). This proposed model works in a low-cost state to achieve a correct digital implementation of the Izhikevich model, one of the main neuron models (i.e. decreasing hardware resources and enhancing speed and accuracy). The proposed model successfully reproduces the behavioral traits of the initial neuron model. To verify the results of the mathematical simulation, the proposed model was synthesized and implemented on the Zynq XC7Z010 (3CLG400) reconfigurable board (FPGA). The findings of hardware synthesis and applications of the suggested paradigm demonstrate that certain biological behaviors may be duplicated more effectively and at a significantly lower cost. The suggested model’s frequency can be increased using this technique (implemented on the Zynq board) at least by 3.6 times compared to the original model, and power consumption can be decreased by 28%. High-frequency design of neuronal models with low-cost attributes is required for application-based types of equipment in case of high-speed operations of these components. Thus, using our approach, the desired goals of application-based features are to be fulfilled. In addition, because the suggested model uses fewer hardware resources than the original model, it is feasible to construct a significantly higher number of neurons (approximately 5 times) on a single Zynq board.

This study examined research interest in copper materials for caries management. We conducted an exhaustive literature search of English publications on copper materials for caries management. We removed duplicate publications and screened the titles and abstracts to identify relevant publications. Then, we analyzed the bibliometric data of the publications using the Bibliometrix and VOSviewer programs. This study included 75 laboratory studies, six clinical trials, and 17 reviews. Most of the original research studied copper or copper oxide nanoparticles (45/81, 56%). The materials could be doped into topical agents, restorative fillers, dental adhesives, dental implants, and orthodontic appliances. Since the first paper was published in 1980, publication counts gradually increased and surged in 2019. Among publications on copper materials for caries management, the publication counts and citations from 2019 to 2024 accounted for 65% (64/98) and 74% (1677/2255) over the last 45 years. Cocitation analysis revealed that the two main keywords were nanoparticles and antibacterial activity, and their burst strengths (period) were 3.84 (2021–2024) and 2.21 (2020–2021). The topics of the top two publications with the highest citation burst strength (period) are the antimicrobial effect of copper oxide nanoparticles (3.14, 2021–2022) and the dental application of copper nanoparticles (2.84, 2022–2024). In conclusion, this study revealed a growing interest in copper materials for caries management.

Objectives: This study developed a sol–gel tricalcium silicate/graphene oxide (TCS-GO) composite and examined its physicochemical properties, antimicrobial activity, and osteo/odontogenic effect on dental pulp stem cells. Methods: Tricalcium silicate was synthesized and combined with graphene oxide at three different concentrations, namely 0.02%, 0.04%, and 0.08% w/w, while tricalcium silicate and mineral trioxide aggregate served as controls. The setting time, compressive strength, pH, and calcium ion release of the composites were evaluated, as well as antimicrobial properties against Streptococcus mutans and Lactobacillus acidophilus. Additionally, the viability of dental pulp stem cells; apatite forming ability; and the gene expression of Alkaline phosphatase, Dentin sialophosphoprotein, and Runt-related transcription factor 2 were assessed. Results: TCS-GO (0.08%) showed a significantly shorter setting time and higher compressive strength when compared to MTA (p < 0.05). Additionally, tricalcium silicate and TCS-GO groups showed a higher release of Ca ions than MTA, with no significant difference in pH values among the different groups. TCS-GO (0.08%) also demonstrated a significantly stronger antimicrobial effect against Lactobacillus acidophilus compared to MTA (p < 0.05). ALP expression was higher in TCS-GO (0.08%) than MTA on days 3 and 7, while DSPP expression was higher in TCS-GO (0.08%) than MTA on day 3 but reversed on day 7. There was no significant difference in RUNX2 expression between TCS-GO (0.08%) and MTA on days 3 and 7. Conclusions: The TCS-GO (0.08%) composite demonstrated superior physicochemical characteristics and antimicrobial properties compared to MTA. Moreover, the early upregulation of ALP and DSPP markers in TCS-GO (0.08%) indicates that it has the potential to promote and enhance the osteo/odontogenic differentiation of DPSCs.

Deep learning, a subset of machine learning that utilizes neural networks, has seen significant advancements in recent years. These advancements have led to breakthroughs in a wide range of fields, from natural language processing to computer vision, and have the potential to revolutionize many industries or organizations. They have also demonstrated exceptional performance in the identification and mapping of seagrass images. However, these deep learning models, particularly the popular Convolutional Neural Networks (CNNs) require architectural engineering and hyperparameter tuning. This paper proposes a Deep Neuroevolutionary (DNE) model that can automate the architectural engineering and hyperparameter tuning of CNNs models by developing and using a novel metaheuristic algorithm, named 'Boosted Atomic Orbital Search (BAOS)'. The proposed BAOS is an improved version of the recently proposed Atomic Orbital Search (AOS) algorithm which is based on the principle of atomic model and quantum mechanics. The proposed algorithm leverages the power of the Lévy flight technique to boost the performance of the AOS algorithm. The proposed DNE algorithm (BAOS-CNN) is trained, evaluated and compared with six popular optimisation algorithms on a patch-based multi-species seagrass dataset. This proposed BAOS-CNN model achieves the highest overall accuracy (97.48%) among the seven evolutionary-based CNN models. The proposed model also achieves the state-of-the-art overall accuracy of 92.30% and 93.5% on the publicly available four classes and five classes version of the 'DeepSeagrass' dataset, respectively. This multi-species seagrass dataset is available at: https://ro.ecu.edu.au/datasets/141/.

The expansive soil swells significantly in the presence of moisture, which often leads to the failure of superstructures. Conventional stabilization techniques are applied in many instances, although environmental issues are of significant concern for such stabilization. Keeping this in mind, an attempt is made to apply a new approach for stabilizing different types of expansive soils, treated with a nonconventional binder geopolymer that utilizes fly ash as the main ingredient. A series of laboratory experiments are run to determine the engineering properties of treated soils with varying percentages of geopolymer from 0% to 30%. The experimental investigation involved tests such as unconfined compressive strength, compaction, Atterberg limits, and swelling pressure. Significant strength development occurs with increasing percentages of geopolymer, and their swelling pressures decrease considerably. Additionally, a series of California Bearing Ratio (CBR) tests were undertaken to assess the suitability for road construction. The optimum dosage of the stabilizing agent is found to be 20%, as justified by studies in the literature. Furthermore, scanning electronic microscope (SEM) images of the treated samples revealed microstructural changes in the soil matrix, which strongly correlate with the improvement of strength and swelling behavior. Hence, based on our experimental results, 20% geopolymer content is sufficient for enhancing the engineering properties of expansive soils, and the treated soils can directly be used as subgrade or sub-base material.

Purpose: Several therapeutic combinations are available for the treatment of advanced gastric cancer (AGC). It is unclear which combinations are most beneficial to the patients. The purpose of this study was to compare the efficacy and safety of Tegafur/ gimeracil/ oteracil (S-1) plus oxaliplatin (S-1OX) with capecitabine plus oxaliplatin (CAPOX) in patients with AGC. Materials and Methods: Relevant randomized controlled trials were searched in MEDLINE, EMBASE, The Cochrane Library (CENTRAL), two major Chinese biomedical databases (CBM, CNKI), and registry centers until July 22, 2019, with no language restrictions. Data were extracted for overall response rate (ORR), time to progression (TTP), overall survival time (OST), and toxicity. The systematic review was performed according to the recommendations of the Cochrane collaboration. RevMan 5.3.1 was used for statistical analysis. Results: A total of 6 randomized controlled trials involving 911 patients were included. The quality of the trials was less than 3 points. All the trials demonstrated a significantly improved toxicity (hand-foot syndrome and neuropathy) in the S-1OX trials (p < 0.05). There was no statistically significant difference (p > 0.05) between S-1OX versus CAPOX in terms of ORR, OST, TTP. Any of the subgroup analyses did not exhibit heterogeneity, so the fixed-effects model be used to execute the subgroup meta-analysis. Conclusions: Both S-1OX and CAPOX showed similar efficacy for treatment of AGC. However, S1-OX appeared to present less toxicity in terms of hand-foot syndrome and neuropathy as compared to CAPOX.

AimsIn a high-income country, Australia, it is unclear how raised systolic blood pressure (SBP) ranks among other risk factors regarding the overall and cardiovascular disease (CVD) burden, and whether the situation has changed over time.MethodsWe analysed the 2019 Global Burden of Disease (GBD) data, with focus on Australia. We assessed ten leading risk factors for all-cause and CVD deaths and disability-adjusted life-years (DALYs) and compared findings with the Australian Burden of Diseases Study.ResultsFrom 1990 to 2019, raised SBP remained the leading risk factor for attributable all-cause deaths (followed by dietary risks and tobacco use), accounting for 29,056/75,235 (95% Uncertainty Interval (UI) [24,863 to 32,915]) deaths in 1990; 21,845/76,893 [17,678 to 26,044] in 2010; and 25,498/90,393 [20,152 to 30,851] in 2019. Contributions of raised SBP to cardiovascular deaths for both sexes were 54.0% [45.8 to 61.5] in 1990, 44.0% [36.7 to 51.3] in 2010 and 43.7% [36.2 to 51.6] in 2019, respectively. The contribution of raised SBP to cardiovascular deaths declined between 1990 and 2010 but exhibited an increase in males from 2010 onwards, with figures of 52.6% [44.7 to 60.0] in 1990, 43.1% [36.0 to 50.5] in 2010 and 43.5% [35.7 to 51.4] in 2019. The contribution of raised SBP to stroke deaths and DALYs in males aged 25-49 years were higher than other age groups, in excess of 60% and increasing steeply between 2010 and 2019.ConclusionRaised SBP continues to be the leading risk factor for all-cause and cardiovascular deaths in Australia. We urge cross-disciplinary stakeholder engagement to implement effective strategies to detect, treat and control raised blood pressure as a central priority to mitigate the CVD burden.

BackgroundThis study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants. ObjectiveThe primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL. MethodsA total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement. ResultsOverall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=–0.3% to 0.9%), 1.19% for standing (LoA=–1.5% to 3.42%), and –4.71% for walking (LoA=–9.26% to –0.16%). The mean biases between SORD and activPAL were –3.45% for sitting and reclining (LoA=–11.59% to 4.68%), 7.45% for standing (LoA=–5.04% to 19.95%), and –5.40% for walking (LoA=–11.44% to 0.64%). ConclusionsResults suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Unhealthy diet is associated with increased risk of cardiovascular diseases (CVD). However, there are no studies reporting the impact and trends of dietary risk factors on CVD in Australia. This study aimed to determine the burden of CVDs attributable to dietary risk factors in Australia between 1990 and 2019. We used data from the Global Burden of Diseases (GBD) study and quantified the rate (per 100,000) of deaths, disability-adjusted life years (DALYs), years lived with a disability (YLDs), and years of life lost (YLLs) for 21 CVDs attributable to 13 dietary risk factors (eight food groups and five nutrients) in Australia by sex and age groups (≥25 years and over). In 2019, the age-standardised rates of deaths, YLDs, YLLs, and DALYs attributable to dietary risk factors attributable to CVDs in the Australian population were 26.5, 60.8, 349.9, and 410.8 per 100,000 in women and 46.1, 62.6, 807.0, and 869.6 in men. Between 1990 and 2019, YLLs consistently contributed more towards the rates of DALYs than YLDs. Over the 30-year period, CVD deaths, YLLs, and DALYs attributable to dietary risk factors declined in both women and men. The leading dietary risk factors for CVD deaths and DALYs were a diet high in red meat (6.1 deaths per 100,000 [3.6, 8.7] and 115.6 DALYs per 100,000 [79.7, 151.6]) in women and a diet low in wholegrains (11.3 deaths [4.4, 15.1] and 220.3 DALYs [86.4, 291.8]) in men. Sex differences were observed in the contribution of dietary risk factors to CVD over time such that the lowest rate of decrease in deaths and DALYs occurred with diets high in sodium in women and diets high in processed meat in men. Although the burden of diet-related CVD has decreased significantly in the Australian population over the past 30 years, diets low in wholegrains and high in red meat continue to contribute significantly to the overall CVD burden. Future nutrition programs and policies should target these dietary risk factors.