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This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

This paper intends to introduce a novel sugarcane plant disease prediction. There are three stages to the projected method, namely: (i) Preprocessing, (ii) Feature extraction, and (iii) Disease detection phase. Initially, the input image is given to the pre-processing stage in which the grey transformation and bilinear interpolation are carried out. The grey transformation is undergone to improve the clarity of the picture and the bilinear interpolation is used as a re-sampling technique. The feature extraction step follows the pre-processed picture, where both texture and colour features are retrieved. More specifically, the texture features like GLCM and suggested Distance-based LBP (D-LBP) features will be extracted. Subsequently, the extracted features are subjected to the disease detection phase process, where a hybrid classifier is introduced. This hybrid classifier blends the concepts of 'Neural Network (NN) and Support Vector Machine (SVM)' for better prediction results. In order to verify the effectiveness of the work that is suggested, a comparison analysis is conducted comparing the suggested and current approaches. The proposed method achieves highest accuracy in 80% of learning which is 13.97%, 16.12%, 11.82%, and 12.9% better than the other methods such as SVM, DT, RF, and NN, respectively. [ABSTRACT FROM AUTHOR]

Attention is a cognitive process that is essential for human performance. Human development frequently depends on attention; however, this topic still requires additional research. Recently, EEG brain waves have been utilised to identify a person's attention states. This work aims to review numerous machine learning algorithms to analyse the Electroencephalographic (EEG) data to recognise human attention. Various machine learning approaches for the analysis of emotional states with EEG data were reviewed. Moreover, the analysis includes the performance achieved in various works; their benefits and disadvantages are reviewed. An EEG data processing pipeline and a review of human attention recognition are developed to evaluate the performance of various machine learning techniques. According to the analysis, the neural network framework achieves 99.81% accuracy. The results serve as a design framework for future systems using EEG data on brain activity to monitor people's health. [ABSTRACT FROM AUTHOR]

This paper has aimed to develop the Breast Cancer Detection (BCD) technique along with the mammography reports of the patients. For this proposed framework, the required images are collected from publicly available datasets. Further, the collected images are given into the phase of feature extraction, where the deep features based on texture features, ResNet, morphological features and density features are extracted and fused together. In addition, the weighted fused feature selection is performed with the fused features, in which the weight is optimised with the Modified Hybrid based Leader Optimization (MHLBO) obtained from Hybrid Leader-Based Optimization(HLBO) to elevate the feature performance on classification. Then, breast cancer can be classified with the developed Adaptive Cascaded Deep Network (ACDN) by combing the classification results of, Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Here, the parameter tuning has occurred in all three classifiers using the same enhanced HLBO. Throughout the experimentation, the designed model shows 97% and 96% regarding accuracy and F1-score, respectively. The tentative outcomes show the efficacy of the designed BCD method is compared with other baseline approaches. [ABSTRACT FROM AUTHOR]

Corneal Ulcers are defined as inflammation or even infection. They are one of the most frequent diseases that affect eye health. The proper measurement of corneal ulcer lesions enables the physician to evaluate the treatment effectiveness and decision making. This paper presents a segmentation method that aims to assist doctors in monitoring the treatment of corneal ulcer lesions. We applied a stepwise fine-tuning in U-Net Convolutional Neural Network architecture to train a model with 358 Point-flaky corneal ulcer images. The result from the model using U-Net architecture is then submitted to post-processing operations. Based on experiments performed with 91 Flaky corneal ulcer images, our approach achieved 0.823 of the Dice Coefficient on average, 88.9% of Recall, 99.4% of Specificity, and True Dice Coefficient of 0.835. The results are promising; we then have evidence that the proposed method could generalise the data from the training phase to segment the test data. [ABSTRACT FROM AUTHOR]

The Multimodal Medical Image Fusion (MMIF) is affected by poor image quality, which leads to the extraction of inefficient features. The main intent of this work is to fuse various planes in the PET and MRI medical images efficiently using the MMIF approach. Initially, the sample images containing the axial plane of PET and MRI images are aggregated from standard datasets. Then, the collected images are employed for the decomposition process, which is accomplished via Optimal Non-Subsampled Contourlet Transform (ONSCT). The parameters in the NSCT are optimized using the Modified Water Strider Algorithm (MWSA. Once the images are decomposed, it is segmented into two sub-bands as high frequency and low-frequency sub-bands. Consequently, the high-frequency sub-bands of both PET and MRI images are fused by using the optimal weighted average fusion, in which the weight factor is obtained optimally by the MWSA algorithm. Similarly, the low-frequency sub-bands of both medical images are combined by sparse fusion technique. Finally, both the resultant fused images are subjected to Inverse Non-Subsampled Contourlet Transform (INSCT) to get desired fused images. The experimental findings suggest that the proposed model has effectively fused the images, and it also enhances the similarity score with axial planes. [ABSTRACT FROM AUTHOR]

Some of the viruses may cause lung parenchyma and airway involvement. Usually, viral pneumonia causes ground-glass opacities, bilateral peripheral distribution, consolidation, vascular thickening, and reticular opacity. These features are common in COVID-19 rather than Non-Covid-19 viral pneumonia. However, in advanced cases, COVID-19 viral pneumonia may cause organising pneumonia and fibrosis of the lung. Atypical findings of Non-Covid-19 pneumonia have included central peripheral distribution, pleural effusion, lymphadenopathy, nodules, tree-in-bud opacities, and pneumothorax. Therefore, differentiating Non-Covid-19 pneumonia from COVID-19 pneumonia at chest computed tomography (CT) is necessary. In that case, CT scans of the thorax are one of the essential tools for early identification and future prognosis of viral pneumonia. We have proposed a Computer-Aided Diagnostic (CAD) system that can detect features of chest CT using a Deep Neural Network (DNN) with Long Short-Term Memory (LSTM). Transfer learning using pre-trained DNN models (ResNet50, VGG19, InceptionV3, Xception, DenseNet121, and VGG16) is applied to retain both high-level and low-level features effectively. The deep features are passed to the LSTM layer. The LSTM is utilised as a classifier and detects long short-term dependencies. The proposed method employs a hybrid DNN-LSTM network for automatic detection to take advantage of the uniqueness of the two models. The proposed models are trained with common and different features present in the chest CT of COVID-19 and Non-Covid-19 viral pneumonia. The 5-fold cross-validation (CV) method validated and tested the proposed model. The proposed DNN model's performance is quite improved with LSTM and CV. As a result, the proposed LM-DNN (VGG16+LSTM+CV) model has achieved the classification test accuracy of 91.58% and specificity of 93.86%, which offers superior performance with state-of-the-art. Also, the DenseNet121+LSTM+CV model has reached the classification test accuracy of 90.1% and sensitivity of 92%. [ABSTRACT FROM AUTHOR]

Upper-limb prostheses are either too expensive for many consumers or have a greatly simplified choice of actions. This research aims to enable an improvement to the quality of life for recipients of these devices by providing an understanding of how we use our hands in modern everyday life. To achieve this a taxonomy of grasps used in activities of daily living has been created. A portable motion capture system was developed to collect data. Thirteen participants' hand movements were recorded (totalling 62 hours and 10 minutes of data). From these data, 38 and 22 grasps were observed from the left and right hands, respectively. The portable system effectively captured natural hand motions, which formed an updated taxonomy of grasps. [ABSTRACT FROM AUTHOR]

Skin melanoma is a potentially fatal form of cancer. If left untreated or allowed to spread, it can lead to death or serious disability. Therefore, early diagnosis is crucial for improving patient prognosis and outcomes. Recent advances in machine learning (ML) and deep learning (DL) have greatly contributed to the categorisation and identification of melanoma. The goal of this survey is to evaluate 60 publications that have been submitted in order to create an overview of the melanoma detection process. It examines several feature extraction techniques.The evaluation concentrates on different melanoma detection methods, including 'deep learning (DL)' and 'machine learning (ML)' models. The analysis includes performance metrics and a review of the results obtained from the PH2 dataset, which achieved a high accuracy of 96.5%. Finally, the survey addresses research gaps to facilitate future investigations into melanoma identification strategies. [ABSTRACT FROM AUTHOR]

A virus known as the Coronavirus 2019 (COVID-19) can have serious effects on a variety of body parts. Preventing this spread of the virus requires early detection and proper treatment. Many methods are followed for early detection of this disease based on symptoms and classification for proper treatment. In this paper, classification of COVID-19 using Computed Tomography (CT) scan images with hybrid optimisation-enabled deep learning is researched. Here, a hybrid optimisation algorithm called War Snake Optimisation Algorithm (WSOA) is proposed to train Deep Q-Network (Deep Q-Net). Moreover, the proposed WSOA is formed by integrating War strategy optimisation (WSO) and Snake Optimiser (SO). In this paper, pre-processing is carried out using a median filter, followed by lung lobe segmentation that is done by Generative Adversarial Network (GAN). Furthermore, image augmentation is done by translation, rotation, flipping and scaling. Then, feature extraction is followed after image augmentation, where needed features get extracted to end up with the classification of COVID-19. This classification brings out the patient's health condition of normal and abnormal situations. Moreover, this research work is analysed with three performance metrics, such as accuracy, sensitivity and specificity with values of 93.7%, 94.8% and 89.5%. [ABSTRACT FROM AUTHOR]

A method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard's colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously affected normal life as well as public safety. It is extremely transmissible and has now infected millions of people worldwide. To obtain more image features of the lungs, Computed Tomography (CT) scans are widely used. However, manual examination of CT images for abnormal areas of COVID-19 disease can be time-consuming, and it is highly subjective to determine whether they are infected. To rapidly screen patients, Machine Learning (ML) can be used to determine whether patients have the disease. In this paper, a hybrid extraction technique is used to extract feature vectors from CT images, which is a mixture of a histogram of orientation gradients (HOG) extraction technique and a local binary pattern (LBP) extraction technique. In this experiment, 960 NON-COVID-19 and 960 COVID-19 were adopted to train the model, and 240 NON-COVID-19 and 240 COVID-19 were used to test the model. And the CT images were scaled to a uniform size. After obtaining the feature vectors using HOG and LBP feature extraction methods, The CT images were classified using a Support Vector Machine (SVM) classifier optimised by Particle Swarm Optimisation (PSO). In the performance evaluation of the presented classification model, the combination of the HOG feature extraction technique and the LBP feature extraction technique resulted in a substantial improvement in the classification effectiveness of the SVM. HOG_LBP PSO SVM improved Accuracy to 97.5%, Precision to 97.75%, Recall to 97.27%, Specificity to 97.25%, F1_score to 97.50%, and Mcc to 95.01%. [ABSTRACT FROM AUTHOR]

The liver is the largest solid organ in the body that can be anatomically divided into segments. We present in this work a web-based subject-specific human liver atlas based on the Couinaud segments simulated from portal venous (PV) perfusion zones, hepatic arterial (HA) and hepatic venous (HV) trees, as well as biliary drainage. The purpose of the atlas is to provide the modelling community with freely accessible 3D hepatic structures for in silico simulations, which are of tremendous value in yielding novel insights in hepatic circulation, drug transport and clearance. [ABSTRACT FROM AUTHOR]

This study utilised a prototype system which consisted of a person-specific 3D printed jaw tracking harness interfacing with the maxillary and mandibular teeth and custom jaw tracking software implemented on a smartphone. The prototype achieved acceptable results. The prototype demonstrated a static position accuracy of less than 1 mm and 5°. It successfully tracked 30 cycles of a protrusive excursion, left lateral excursion, and 40 mm of jaw opening on a semi-adjustable articulator. The standard error of the tracking accuracy was reported as 0.1377 mm, 0.0449 mm, and 0.9196 mm, with corresponding ${r^2}$ r 2 values of 0.98, 1.00, and 1.00, respectively. Finally, occlusal contacts of left, right, and protrusive excursions were tracked with the prototype system and their trajectories were used to demonstrate kinematic modelling (no occlusal forces) with a biomechanical simulation tool. [ABSTRACT FROM AUTHOR]

Machine learning methods have been extensively employed to predict COVID-19 using chest X-ray images in numerous studies. However, a machine learning model must exhibit robustness and provide reliable predictions for diverse populations, beyond those used in its training data, to be truly valuable. Unfortunately, the assessment of model generalisability is frequently overlooked in current literature. In this study, we investigate the generalisability of three classification models – ResNet50v2, MobileNetv2, and Swin Transformer – for predicting COVID-19 using chest X-ray images. We adopt three concurrent approaches for evaluation: the internal-and-external validation procedure, lung region cropping, and image enhancement. The results show that the combined approaches allow deep models to achieve similar internal and external generalisation capability. [ABSTRACT FROM AUTHOR]

Brain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes' territory localisation. All models' architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications. [ABSTRACT FROM AUTHOR]

Background: Auditory electrophysiological tests of the cortex, which are processed in or close to the auditory cortex, are brain reactions to sound. A variation in a continuous stimulus causes the acoustic change complex potential (ACC), which is a wave following the P1-N1-P2 response. Objective: To measure the amplitude and latency of different components of ACC in normal subjects and across individuals with mild and moderate degrees of sensorineural hearing loss (SNHL). Patients and methods: The study includes 100 individuals with the age ranged from 10 to 50 years with different degrees of SNHL. The ACC was evoked by a change of second formant in the middle of ongoing steady-state synthetic, 3 formant vowels (ooee). The total duration was 500 ms. Changing occurred at 250 ms. Results: The SNHL subgroups showed statistically significantly longer P1 and N1 latencies. N1 and P2 amplitudes of ACC onset response were larger with a statistical significance as compared to controls. Post hoc analysis revealed no statistically significant difference between mild and moderate SNHL on ACC parameters. Age showed a significant negative correlation with ACC N1 and P2 latency, ACC P1 and N1 amplitude, and onset P2 latency. Onset response P1 latency was significantly higher in children than adults. Median ACC P1 amplitude significantly increased in children than adults. Conclusion: ACC is a reliable tool for testing the auditory cortex function of detecting difference in sounds presented that can be recorded readily in patients with mild and moderate SNHL. [ABSTRACT FROM AUTHOR]

Ecological restoration and environmental risk control on bauxite residue disposal areas have received persistent attention worldwide as it concerns the sustainable development of global alumina enterprises. Currently, microbially driven ecological restoration has been regarded as a promising way in consideration of its cost effectiveness, lower risks, and durability. However, the extremely degraded environment (e.g., high alkalinity, poor physical structure, and low organic carbon content) poses significant challenges for the activity and effectiveness of functional microorganisms involved in bioremediation. Moreover, the specific mechanisms of functional microorganisms, including alkaline regulation, stable aggregate formation, organic carbon accumulation, and plant growth promotion, in the soil-formation and revegetation process are not well understood, hindering the widespread application of microbially driven ecological restoration strategies in large-scale bauxite residue disposal. To date, these strategies have not been demonstrated in a large scale. To address these gaps, this review focuses on the microbial properties of bauxite residue disposal areas and the limiting factors of microbial functions. Furthermore, considering the differences between bauxite residue and natural soils, the roles of microorganisms in alkaline stabilization, aggregate formation, and organic carbon accumulation in bauxite residue during the soil-formation process are explored. Meanwhile, the microbially driven ecological restoration on bauxite residue disposal areas, including plant growth capacity and restoration process regulation, are discussed. Lastly, future research directions for understanding microbially driven soil-formation processes and ecological restoration on bauxite residue disposal areas are envisaged. This review provides new insights into the driving mechanisms of microorganisms on the soil formation of bauxite residue, offering a theoretical foundation and practical guidance for ecological restoration on bauxite residue disposal areas. [ABSTRACT FROM AUTHOR]

Di(2-ethylhexyl) phthalate (DEHP) is a plasticizer that is used worldwide and raises concerns because of its prevalence in the environment and potential toxicity. Herein, the capability of Fusarium culmorum to degrade a high concentration (3 g/L) of DEHP as the sole carbon and energy source in solid-state fermentation (SSF) was studied. Cultures grown on glucose were used as controls. The biodegradation of DEHP by F. culmorum reached 96.9% within 312 h. This fungus produced a 3-fold higher esterase activity in DEHP-supplemented cultures than in control cultures (1288.9 and 443.2 U/L, respectively). In DEHP-supplemented cultures, nine bands with esterase activity (24.6, 31.2, 34.2, 39.5, 42.8, 62.1, 74.5, 134.5, and 214.5 kDa) were observed by zymography, which were different from those in control cultures and from those previously reported for cultures grown in submerged fermentation. This is the first study to report the DEHP biodegradation pathway by a microorganism grown in SSF. The study findings uncovered a novel biodegradation strategy by which high concentrations of DEHP could be biodegraded using two alternative pathways simultaneously. F. culmorum has an outstanding capability to efficiently degrade DEHP by inducing esterase production, representing an ecologically promising alternative for the development of environmental biotechnologies, which might help mitigate the negative impacts of environmental contamination by this phthalate. KEY POINTS: • F. culmorum has potential to tolerate and remove di(2-ethylhexyl) phthalate (DEHP) • Solid-state fermentation is an efficient system for DEHP degradation by F. culmorum • High concentrations of DEHP induce high levels of esterase production by F. culmorum., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Trichoderma longibrachiatum UN32 is known for its efficient production of dendrobine-type total alkaloids (DTTAs). This study aimed to determine the optimal medium composition for the UN32 strain using response surface methodology. Key factors, including glucose, beef extract, and CoCl 2 , were selected through the Plackett-Burman design. Subsequently, a factorial optimization approach was employed using the steepest ascent design, and 17 trial sets were completed via the Box-Behnken design. The optimal medium composition was found to consist of 29.4 g/L of glucose, 17.3 g/L of beef extract, and 0.28 mmol/L of CoCl 2 . This optimized medium resulted in an impressive 80.8% increase in mycelial dry weight (reaching 12.303 g/L) and a substantial 76.4% boost in DTTA production (reaching 541.63 ± 46.95 μg). Furthermore, the fermentation process was scaled up to a 5-L bioreactor, leading to a DTTA production approximately 1.95 times than the control. Transcriptome analysis of strain UN32 in response to CoCl 2 supplementation revealed significant changes in the expression of critical genes associated with the TCA cycle and L-valine, L-leucine, and L-isoleucine biosynthesis changed. These alterations resulted in a heightened influx of acetyl-CoA into DTTA production. Additionally, the expression of genes related to antioxidant enzymes was modified to maintain homeostasis of reactive oxygen species (ROS). A potential mechanism for the accumulation of DTTAs based on ROS as a signal transduction was proposed. These findings provide valuable insights into the regulatory mechanisms of DTTA biosynthesis, potentially offering a method to enhance the production of secondary metabolites in the UN32 strain. KEY POINTS: • After the RSM optimization, there is a substantial increase of 80.8% in biomass production and a significant 76.4% rise in DTTA production. • Transcriptome analysis revealed that the inclusion of CoCl 2 supplements resulted in an enhanced influx of acetyl-CoA. • Proposed a mechanism for the accumulation of DTTAs for the role of ROS as a signal transduction pathway., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The discharge of industrial water requires the removal of its pollutants, where biological wastewater treatment plants (WWTPs) are the most used systems. Biological WWTPs make use of activated sludge (AS), where bacteria are responsible for the removal of pollutants. However, our knowledge of the microbial communities of industrial plants is limited. Understanding the microbial population is essential to provide solutions to industrial problems such as bulking. The aim of this study was to identify at a high taxonomic resolution the bacterial population of 29 industrial WWTPs using 16S rRNA amplicon sequencing. Our results revealed that the main functional groups were dominated by Thauera and Zoogloea within denitrifiers, Dechloromonas in phosphate-accumulating organisms, and Defluviicoccus in glycogen-accumulating organisms. The activated sludge characterization indicated that 59% of the industrial plants suffered from bulking sludge, with DSVI values of up to 448 mL g -1 . From the bulking cases, 72% corresponded to filamentous bulking with Thiothrix as the most abundant filament; meanwhile, the other 28% corresponded to viscous bulking sludge in which Zoogloea was the most abundant genus. Furthermore, the bacterial population did not share a core of taxa across all industrial plants. However, 20 genera were present in at least 50% of the plants comprising the general core, including Thauera, Ca. Competibacter, and several undescribed microorganisms. Moreover, statistical analysis revealed that wastewater salinity strongly affected the microbial richness of the industrial plants. The bacterial population across industrial plants differed considerably from each other, resulting in unique microbial communities that are attributed to the specificity of their wastewaters. KEY POINTS: • The general core taxa of industrial plants were mostly made up of undescribed bacterial genera. • Filamentous bacteria constituted on average 4.1% read abundance of the industrial WWTPs. • Viscous bulking remains a significant type of bulking within industrial WWTPs., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Hexabromocyclododecane (HBCD) is a widely used brominated flame retardant; however, it is a persistent organic pollutant as well as affects the human thyroid hormones and causes cancer. However, the degradation of HBCD has received little attention from researchers. Due to its bioaccumulative and hazardous properties, an appropriate strategy for its remediation is required. In this study, we investigated the biodegradation of HBCD using Shewanella oneidensis MR-1 under optimized conditions. The Box-Behnken design (BBD) was implemented for the optimization of the physical degradation parameters of HBCD. S. oneidensis MR-1 showed the best degradation performance at a temperature of 30 °C, pH 7, and agitation speed of 115 rpm, with an HBCD concentration of 1125 μg/L in mineral salt medium (MSM). The strain tolerated up to 2000 μg/L HBCD. Gas chromatography-mass spectrometry analysis identified three intermediates, including 2-bromo dodecane, 2,7,10-trimethyldodecane, and 4-methyl-1-decene. The results provide an insightful understanding of the biodegradation of HBCD by S. oneidensis MR-1 under optimized conditions and could pave the way for further eco-friendly applications. KEY POINTS: • HBCD biodegradation by Shewanella oneidensis • Optimization of HBCD biodegradation by the Box-Behnken analysis • Identification of useful metabolites from HBCD degradation., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The COVID-19 pandemic challenged our lives during the years 2020-2022. Impacts could be seen in everyday life, both locally and nationally, through economic, mental and social elements. However, these effects varied depending on the life situation of individuals. This paper aims to gather information from the representatives and operators working in two Finnish municipalities, Inari and Utsjoki, to understand and learn about their experiences during the COVID-19 pandemic. The data (20 interviews) were collected between December 2021 and February 2022 and analysed following the principles of the qualitative content analysis. The results suggest that the effects of COVID-19 emerged through issues related to the national border between Finland and Norway, economic challenges, and the pressure that people experienced. However, despite challenges, people were supported by everyday life and a connection to nature, communality and close co-operation. Additionally, local needs were highlighted among participants. The results provide a deeper understanding about the public health impacts in these Northernmost municipalities and can therefore be utilised in future development work. They also provide relevant information on the experiences of Sámi people, and specific views related to Sámi people can be recognised.

Many indigenous cultures conceptualize health wholistically, whereby physical, mental, spiritual and relational dimensions of health are interconnected. Yet, quantitative approaches to studying Indigenous health remain anchored in western perspectives, that separate the dimensions of health. This paper aims to operationalize a wholistic indicator of health based on the IQI model of Inuit health. Variables from the 2017 Nunavik Health Survey ( N  = 1196) were selected based on their representativeness of IQI model. Exploratory Latent Class Analysis (LCA) was used to identify wholistic health profiles. Once participants assigned to their health profile, sociodemographic characteristics were compared across profiles, and multinomial regression models were used to examine the relationship between community-level social determinants of health and the profiles. The LCA revealed three health profiles, labelled as " excellent" , " good" and " fair" based on the distribution of answers to the indicators. Nunavimmiut in " excellent" and " good" health were more likely to: rate their health positively; be over 30 years old; be in a relationship; and have participated or volunteered in community events. Nunavimmiut in " fair " health tended to report lower levels of community cohesion, family relationships, and emotional support. Intergrating culturally relevant models of health can support improved health status assessments and identify opportunities for health promotion.

This paper outlines the methodological approaches to a multi-site Circumpolar case study exploring the impacts of COVID-19 on Indigenous and remote communities in 7 of 8 Arctic countries. Researchers involved with the project implemented a three-phase multi-site case study to assess the positive and negative societal outcomes associated with the COVID-19 pandemic in Arctic communities from 2020 to 2023. The goal of the multi-site case study was to identify community-driven models and evidence-based promising practices and recommendations that can help inform cohesive and coordinated public health responses and protocols related to future public health emergencies in the Arctic. Research sites included a minimum of 1 one community each from Canada (Nunavut,) United States of America (Alaska), Greenland, Iceland, Norway, Sweden, Finland. The approaches used for our multi-site case study provide a comprehensive, evidence-based account of the complex health challenges facing Arctic communities, offering insights into the effectiveness of interventions, while also privileging Indigenous local knowledge and voices. The mixed method multi-site case study approach enriched the understanding of unique regional health disparities and strengths during the pandemic. These methodological approaches serve as a valuable resource for policymakers, researchers, and healthcare professionals, informing future strategies and interventions.

In the last decade, policy strategies were adopted in response to population ageing in the Nordic countries. Governmental actions have to be evaluated in terms of their efficacy. The objective of this article is to identify and review the policies related to age-inclusive outdoor spaces in the Arctic regions of Nordic countries. Our analysis focuses on central government white papers that address the older adults in Finland, Norway, Sweden, and Iceland. A review of such policy documents provides insights into the predominant understandings of older adults and healthy ageing. Moreover, such analysis may identify "blind spots" in the national policies, especially regarding the sparsely researched northernmost and rural Arctic territories. Our results demonstrate how the older populations in the Nordic Arctic and their access to outdoor spaces are addressed in the policy documents. We found that with few exceptions, the older people of the rural Arctic is strikingly absent in the Nordic national governmental papers. Moreover, access to outdoor spaces is mentioned in general terms, and specific challenges of the rural Arctic context, such as the harsh climate, long winters and geographical distances are not addressed. The noticed omissions might be the result of "urban-rural", "south-north", "indoor-outdoor", and "generalisation" biases.

High viscosity glass ionomer cement (HVGIC) has been employed as a restorative material for Atraumatic Restorative Treatment (ART). As residual caries persist after caries removal in ART, the antibacterial activity of HVGIC gains importance. Organic and inorganic substances with antibacterial properties have been incorporated into HVGIC over the years, and their effects on the antibacterial and physical properties have been studied. The objective of this paper is to review the various alterations made to HVGIC using organic compounds, their effect on the antibacterial activity, and the physical properties of the cement. Various in vitro investigations have been conducted by adding antiseptics, antibiotics, and naturally occurring antibacterial substances. Most of these compounds render superior antibacterial properties to HVGIC, but higher concentrations affect physical properties in a dose-dependent manner. However, some naturally occurring antibacterial substances, such as chitosan, improve the physical properties of HVGIC, as they enhance cross-linking and polysalt bridging. There is potential for clinical benefits to be gained from the addition of organic antibacterial compounds to HVGIC. In-depth research is required to determine the optimum concentration at which the antibacterial effect is maximum without affecting the physical properties of the cement., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

This study presents the results of systematic reviews on adjunctive tools in screening and diagnosis of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) and to determine if the current literature supports their use as either an adjunctive tool or replacement of gold standard techniques. Systemic reviews and meta-analysis that evaluated adjunctive tools including chemiluminescence, tissue autofluorescence, tissue fluorescence spectroscopy, vital staining and cytology techniques were systematically examined using AMSTAR II. Twenty-seven systematic reviews were included. Five studies had a low quality of evidence, and nine studies had a critically low quality of evidence. This review found limited evidence to recommend chemiluminescence, tissue autofluorescence tools and vital staining as diagnostic tools, but only serve as clinical adjuncts to conventional oral examination. Cytology techniques and narrow-band imaging may be utilised as a non-invasive diagnostic adjunctive tool for the detection of OSCC and the malignant transformation of OPMD. In conclusion, this paper provides evidence on several types of adjunctive tools and provides recommendations on their use in clinical practice. These tools are considered useful as clinical adjuncts but there is insufficient evidence for their use as a diagnostic tool to replace gold standard conventional oral examination and surgical biopsy., Competing Interests: None., (© 2024 The Authors.)

The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic accuracy of AI models for caries detection and classification on bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk of bias (RoB) assessment tool was developed and applied to the 14 articles that met the inclusion criteria out of 935 references. Dataset sizes ranged from 112 to 3686 radiographs. While 86 % of the studies reported a model with an accuracy of ≥80 %, most exhibited unclear or high risk of bias. Three studies compared the model's diagnostic performance to dentists, in which the models consistently showed higher average sensitivity. Five studies were included in a bivariate diagnostic random-effects meta-analysis for overall caries detection. The diagnostic odds ratio was 55.8 (95 % CI= 28.8 - 108.3), and the summary sensitivity and specificity were 0.87 (0.76 - 0.94) and 0.89 (0.75 - 0.960), respectively. Independent meta-analyses for dentin and enamel caries detection were conducted and showed sensitivities of 0.84 (0.80 - 0.87) and 0.71 (0.66 - 0.75), respectively. Despite the promising diagnostic performance of AI models, the lack of high-quality, adequately reported, and externally validated studies highlight current challenges and future research needs., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The longevity of an extraoral prosthesis depends on its physical and mechanical properties and user maintenance. Faced with multiple outcome measures, researchers find it difficult to determine the most appropriate extraoral prosthetic material. This comprehensive review evaluates the most used extraoral prosthesis materials and qualitatively assesses their longevity and function. The study aims to identify and interpret the results of current updates on the factors that affect longevity and functionality. This comprehensive review summarizes and evaluates differences in the properties of commonly used extraoral maxillofacial prosthetic materials. The review was planned to focus on all factors related to the longevity and function of the extraoral maxillofacial prosthetics. An electronic search covered English articles in PubMed, Scopus, Google Scholar, Web of Science, and grey literature. Manual searching was also performed. Six authors participated in the screening. Search engines extracted 1107 records, and 88 studies were included for qualitative and bias assessments. Silicones are the most frequently used extraoral maxillofacial prosthetic materials. Heat-cured silicones are more color-stable than those cured at room temperature. Additional ingredients and processing techniques affect prosthesis longevity., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Objective : The multibranched off-the-shelf Zenith® t-Branch (Cook Medical, Bloomington, IN) device is commonly chosen for endovascular repair of thoracoabdominal aortic aneurysms. The aim of this study was to report early and mid-term outcomes in all patients treated with the t-Branch in Norway; Design and Methods : A retrospective multicenter study with Norwegian centers performing complex endovascular aortic repair was undertaken. T-Branch patients from 2014 to 2020 were included. All postoperative computed tomography angiography images were reviewed, and demographic, anatomical, perioperative and follow-up data were analyzed; Results: Seventy patients were treated in a single-step ( n  = 55) or staged ( n  = 15) procedure. Symptomatic presentation was seen in 20 patients, six of which had a contained rupture. Technical success was 87% ( n  = 59), with failures caused by unsuccessful bridging of target vessels ( n  = 4), target vessel bleeding ( n  = 3), persisting type 1c endoleak ( n  = 1) and t-Branch malrotation ( n  = 1). 30-day mortality was 9% ( n  = 6) and was associated with high BMI ( p  = .038). The spinal cord ischemia rate was 21% ( n  = 15) and was associated with type II aneurysms (OR 5.4, 95% CI 1.1-26.7, p  = .04), smoking (OR 6.0, 95% CI 1.3-27.6, p  = .02) and intraoperative blood loss (OR 1.1, 95% CI 1.0-1.3, p  = .01). Survival at one, two and three years was 84 ± 4%, 70 ± 6% and 67 ± 6%, respectively. Freedom from aortic-related reinterventions at one, two and three years was 80 ± 5%, 65 ± 7% and 50 ± 8%, respectively; Conclusion : The study showed low early mortality (9%) and satisfactory mid-term survival. Technical success was achieved in acceptable 87% of procedures. The rate of spinal cord ischemia was high, occurring in 21% of patients.

The World Wide Web and the advancement of computer technology in the 1960s and 1990s respectively set the ground for a substantial and simultaneous change in many facets of our life, including medicine, health care, and medical education. The traditional didactic approach has shifted towards more dynamic and interactive methods, leveraging technologies such as simulation tools, virtual reality, and online platforms. At the forefront is the remarkable evolution that has revolutionized how medical knowledge is accessed, disseminated, and integrated into pedagogical practices. The COVID-19 pandemic also led to rapid and large-scale adoption of e-learning and digital resources in medical education because of widespread lockdowns, social distancing measures, and the closure of medical schools and healthcare training programs. This review paper examines the evolution of medical education from the Flexnerian era to the modern digital age, closely examining the influence of the evolving WWW and its shift from Education 1.0 to Education 4.0. This evolution has been further accentuated by the transition from the static landscapes of Web 2D to the immersive realms of Web 3D, especially considering the growing notion of the metaverse. The application of the metaverse is an interconnected, virtual shared space that includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create a fertile ground for simulation-based training, collaborative learning, and experiential skill acquisition for competency development. This review includes the multifaceted applications of the metaverse in medical education, outlining both its benefits and challenges. Through insightful case studies and examples, it highlights the innovative potential of the metaverse as a platform for immersive learning experiences. Moreover, the review addresses the role of emerging technologies in shaping the post-pandemic future of medical education, ultimately culminating in a series of recommendations tailored for medical institutions aiming to successfully capitalize on revolutionary changes.

Background: Xenograft kidney transplantation has been receiving increasing attention. The purpose of this study is to use bibliometric analysis to identify papers in this research field and explore their current status and development trends., Methods: Using the data in the Web of Science core database from Clarivate Analytics as the object of study, we used 'TS = Kidney OR Renal AND xenotransplantation' as the search term to find all literature from 1980 to 2 November 2022., Results: In total, 1005 articles were included. The United States has the highest number of publications and has made significant contributions in this field. Harvard University was at the forefront of this study. Professor Cooper has published 114 articles in this field. Xenotransplantation has the largest number of relevant articles. Transplantation was the most cited journal. High-frequency keywords illustrated the current state of development and future trends in xenotransplantation. The use of transgenic pigs and the development of coordinated co-stimulatory blockers have greatly facilitated progress in xenotransplantation research. We found that 'co-stimulation blockade', 'xenograft survival', 'pluripotent stem cell', 'translational research', and 'genetic engineering' were likely to be the focus of attention in the coming years., Conclusions: This study screened global publications related to xenogeneic kidney transplantation; analyzed their literature metrology characteristics; identified the most cited articles in the research field; understood the current situation, hot spots, and trends of global research; and provided future development directions for researchers and practitioners.

Delayed graft function (DGF) is an early complication after kidney transplantation. The literature on DGF has experienced substantial growth. However, there is a lack of bibliometric analysis of DGF. This study aimed to analyze the scientific outputs of DGF and explore its hotspots from 2013 to 2023 by using CiteSpace and VOSviewer. The 2058 pieces of literature collected in the Web of Science Core Collection (WOSCC) from 1 January 2013 to 31 December 2023 were visually analyzed in terms of the annual number of publications, authors, countries, journals, literature co-citations, and keyword clustering by using CiteSpace and VOSviewer. We found that the number of papers published in the past ten years showed a trend of first increasing and then decreasing; 2021 was the year with the most posts. The largest number of papers was published by the University of California System, and the largest number of papers was published by the United States. The top five keyword frequency rankings are: 'delayed graft function', 'kidney transplantation', 'renal transplantation', 'survival', and 'recipients'. These emerging trends include 'brain death donors', 'blood absence re-injection injuries', 'tacrolimus', 'older donors and recipients', and 'artificial intelligence and DGF'. In summary, this study reveals the authors and institutions that could be cooperated with and discusses the research hotspots in the past ten years. It provides a reference and direction for future research and application of DGF.

Polycystic Ovarian Syndrome (PCOS) is a prevalent metabolic and hormonal disorder affecting women of reproductive age. Limited data exists on Syrian women's PCOS awareness and health behaviors. This study aimed to gauge PCOS prevalence, knowledge, awareness, and health-related practices among Syrian women. A cross-sectional online survey was conducted from 11 February to 27 October 2022, targeting Syrian women aged 18-45. Collaborators from specific medical universities distributed a questionnaire adapted from a Malaysian paper through social media platforms. Out of 1840 surveyed Syrian women, 64.2% were aged 21-29, and 69.6% held bachelor's degrees. Those with a bachelor's degree exhibited the highest mean knowledge score (12.86), and women previously diagnosed with PCOS had a higher mean knowledge score (13.74) than those without. Approximately 27.4% were confirmed PCOS cases, and 38.9% had possible cases. Women with PCOS were 3.41 times more likely to possess knowledge about the condition. The findings suggest a moderate level of PCOS knowledge and health-related practices among Syrian women, emphasizing the need for increased awareness. Consistent local PCOS screening programs, in collaboration with obstetrics and gynecology professionals, are crucial for improving understanding and clinical symptom recognition of this condition among Syrian women.

Background: Exotropia control is deteriorated by post-strabismus surgery in many cases. Improving this control is considered as an important factor for success of the strabismus surgery like ocular motor alignment., Objective: To determine the therapeutic effect of postoperative eye exercises on exodeviation eye control after the strabismus surgery., Methods: Forty patients suffering from intermittent exotropia after strabismus surgery were randomly divided into experimental group and control groups. Both groups received the usual medical care, while the experimental group received different types of eye exercises using physiotherapy modalities in 24 sessions over three months (two sessions a week). The measurement of exotropia control for near target and far target using the office-based scale was done before and after the treatment., Results: There was a significant improvement in near eye control post-treatment ( 0 . 45 ± 0 . 61 ) in the experimental group compared with control group ( 3 . 65 ± 0 . 88 ) as the mean difference was -3.20 and 95% CI of the difference between them was -3.645--2.755 with P < 0 . 01 . Also, far eye control improved post-treatment in the experimental group ( 0 . 75 ± 0 . 64 ) compared with control group ( 4 . 00 ± 0 . 86 ) with mean difference of -3.250 and 95% CI of the difference between them was -3.727--2.773 with P < 0 . 01 ., Conclusions: For patients with intermittent exotropia who had undergone strabismus surgery, the addition of physiotherapy exercises for the eye provided significant improvements in exotropia control for near target and far target., Competing Interests: No potential conflict of interest related to this paper was reported., (© Hong Kong Physiotherapy Association.)