E-scooter-related dental injuries: a two-year retrospective review.

Introduction In June 2020, the United Kingdom (UK) published guidance on electric scooter (e-scooter) use to ease transport congestion and reduce pollution. This study aims to examine dental injuries sustained during the two years following initiation of the trial.Methods The research was conducted at a UK, Level 1, supra-regional major trauma centre. All eligible patient records were analysed to identify e-scooter-related dental injuries to the following regions: teeth, periodontium, alveolus, palate, tongue, floor of mouth, frenum, buccal mucosa and lips. To assess significant associations between recorded variables, a Pearson's chi-square test was utilised.Results Of the 32 patients who experienced a total of 71 dental injuries, 46.5% (n = 33) affected teeth, predominantly upper central incisors (n = 17). 'Lacerations' (n = 32) and 'lips' (n = 30) were the most common type and site of soft tissue injuries, respectively. Unprovoked falls by riders accounted for 53.1% (n = 17) of the injuries. There was an overall increase in e-scooter-related dental injuries throughout the two-year period.Conclusion E-scooters have introduced an additional source of dental trauma. It is imperative health care professionals can also identify signs of head and non-dental injuries when managing such patients. Further studies are warranted allowing for better informed and optimised dental public health interventions.

Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model.

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.

Introduction: Paddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production. Methods: In this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered. Results: Three infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%. Discussion: The findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.

Clinical and epidemiological features of pediatric population hospitalized with COVID-19: a multicenter longitudinal study (March 2020-December 2021) from Pakistan.

Background: We aimed to explore the epidemiological, clinical, and phenotypic parameters of pediatric patients hospitalized with COVID-19 in Pakistan. Methods: This longitudinal cohort study was conducted in five tertiary care hospitals in Pakistan from March 2020 to December 2021. Data on various epidemiological and clinical variables were collected using Case Report Forms (CRFs) adapted from the WHO COVID-19 clinical data platform at baseline and at monthly follow-ups for 3 months. Findings: A total of 1090 children were included. The median age was 5 years (Interquartile range 1-10), and the majority presented due to new signs/symptoms associated with COVID-19 (57.8%; n = 631), the most common being general and respiratory symptoms. Comorbidities were present in 417 (38.3%) children. Acute COVID-19 alone was found in 932 (85.5%) children, 81 (7.4%) had multisystem inflammatory syndrome (MIS-C), 77 (7.0%) had overlapping features of acute COVID-19 and MIS-C, and severe disease was found in 775/1086 (71.4%). Steroids were given to 351 (32.2%) patients while 77 (7.1%) children received intravenous immunoglobulins. Intensive care unit (ICU) care was required in 334 (31.6%) patients, and 203 (18.3%) deaths were reported during the study period. The largest spike in cases and mortality was from July to September 2021 when the Delta variant first emerged. During the first and second follow-ups, 37 and 10 children expired respectively, and medical care after discharge was required in 204 (25.4%), 94 (16.6%), and 70 (13.7%) children respectively during each monthly follow-up. Interpretation: Our study highlights that acute COVID-19 was the major phenotype associated with high severity and mortality in children in Pakistan in contrast to what has been observed globally. Funding: The study was supported by the World Health Organization (WHO), which was involved in the study design but played no role in its analysis, writeup, or publication.

Short to midterm follow-up of multi-system inflammatory syndrome in children with special reference to cardiac involvement.

OBJECTIVES: We aim to describe the early and upto 16 months follow-up of post-coronavirus disease (COVID), multi-system inflammatory syndrome in children (MIS-C), with special reference to cardiac involvement. STUDY DESIGN: This cohort non-interventional descriptive study included patients <18 years admitted between May, 2020 and April, 2021. Based on underlying similarities, children were classified as post-COVID MIS-C with overlapping Kawasaki Disease, MIS-C with no overlapping Kawasaki Disease, and MIS-C with shock. Post-discharge, patients were followed at 1, 3, 6, 12, and 16 months. RESULTS: Forty-one patients predominantly males (73%), at median age of 7 years (range 0.2-16 years) fulfilled the World Health Organisation criteria for MIS-C. Cardiac involvement was seen in 15 (36.5%); impaired left ventricle (LV) function in 5 (12.2%), coronary artery involvement in 10 (24.4%), pericardial effusion in 6 (14.6%) patients, and no arrhythmias. There were two hospital deaths (4.9%), both in MIS-C shock subgroup (2/10, 20%). At 1 month, there was persistent LV dysfunction in 2/5, coronary artery abnormalities in 7/10, and pericardial effusion resolved completely in all patients. By 6 months, LV function returned to normal in all but coronary abnormalities persisted in two patients. At last follow-up (median 9.8 months, interquartile range 2-16 months), in 36/38 (94.7%) patients, coronary artery dilatation was persistent in 2 (20%) patients. CONCLUSIONS: Children with MIS-C have a good early outcome, though MIS-C with shock can be life-threatening subgroup in a resource-constrained country setting. On midterm follow-up, there is normalisation of LV function in all and recovery of coronary abnormalities in 80% of patients.

A robust deep learning approach for tomato plant leaf disease localization and classification.

Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato plant disease classification, however, the timely localization and identification of various tomato leaf diseases is a complex job as a consequence of the huge similarity among the healthy and affected portion of plant leaves. Furthermore, the low contrast information between the background and foreground of the suspected sample has further complicated the plant leaf disease detection process. To deal with the aforementioned challenges, we have presented a robust deep learning (DL)-based approach namely ResNet-34-based Faster-RCNN for tomato plant leaf disease classification. The proposed method includes three basic steps. Firstly, we generate the annotations of the suspected images to specify the region of interest (RoI). In the next step, we have introduced ResNet-34 along with Convolutional Block Attention Module (CBAM) as a feature extractor module of Faster-RCNN to extract the deep key points. Finally, the calculated features are utilized for the Faster-RCNN model training to locate and categorize the numerous tomato plant leaf anomalies. We tested the presented work on an accessible standard database, the PlantVillage Kaggle dataset. More specifically, we have obtained the mAP and accuracy values of 0.981, and 99.97% respectively along with the test time of 0.23 s. Both qualitative and quantitative results confirm that the presented solution is robust to the detection of plant leaf disease and can replace the manual systems. Moreover, the proposed method shows a low-cost solution to tomato leaf disease classification which is robust to several image transformations like the variations in the size, color, and orientation of the leaf diseased portion. Furthermore, the framework can locate the affected plant leaves under the occurrence of blurring, noise, chrominance, and brightness variations. We have confirmed through the reported results that our approach is robust to several tomato leaf diseases classification under the varying image capturing conditions. In the future, we plan to extend our approach to apply it to other parts of plants as well.

IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey.

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images.

Recent advancement in the field of deep learning has provided promising performance for the analysis of medical images. Every year, pneumonia is the leading cause for death of various children under the age of 5 years. Chest X-rays are the first technique that is used for the detection of pneumonia. Various deep learning and computer vision techniques can be used to determine the virus which causes pneumonia using Chest X-ray images. These days, it is possible to use Convolutional Neural Networks (CNN) for the classification and analysis of images due to the availability of a large number of datasets. In this work, a CNN model is implemented for the recognition of Chest X-ray images for the detection of Pneumonia. The model is trained on a publicly available Chest X-ray images dataset having two classes: Normal chest X-ray images and Pneumonic Chest X-ray images, where each class has 5000 Samples. 80% of the collected data is used for the purpose to train the model, and the rest for testing the model. The model is trained and validated using two optimizers: Adam and RMSprop. The maximum recognition accuracy of 98% is obtained on the validation dataset. The obtained results are further compared with the results obtained by other researchers for the recognition of biomedical images.

A novel multiple kernel fuzzy topic modeling technique for biomedical data.

BACKGROUND: Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. METHODS: In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. RESULTS: Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. CONCLUSION: We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

Berardinelli Seip Syndrome: A rare case report.

Berardinelli Seip Congenital Lipodystrophy (BSCL) or Congenital Generalized Lipodystrophy (CGL) is one of the four subgroups of lipodystrophy syndrome which is characterized by varying degrees of loss of adipose mass in the body. It is an extremely rare autosomal recessive disorder and commonly reported clinical presentations include muscular hypertrophy, gigantism, hepatomegaly, impaired glucose tolerance, acanthosis nigricans, hypertriglyceridaemia, cardiomyopathy, intellectual impairment, bone cysts and phlebomegaly. We present a case of a 4.5 years old male child born to consanguineous parents, presented with pneumonia. There was history of recurrent diarrhea and chest infection in the past. He had acromegaly like features, hirsutism, firm hepatomegaly, a well defined bone cyst in proximal right femur, pancytopenias with normal bone marrow biopsy report, hypertriglyceridemia and selective IgA deficiency. This is the first case of BSCL, reported in Pakistan with a bone cyst and IgA deficiency. Such patients need to be identified and monitored for complications like diabetes mellitus and hypertrophic cardiomyopathy.

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction.

Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds.

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud computing. The digital fingerprinting technique has been adopted as an effective solution to protect the copyright and privacy of digital properties from illegal distribution and identification of malicious traitors over the cloud. Furthermore, it is used to trace the unauthorized distribution and the user of multimedia content distributed through the cloud. In this paper, we propose a novel fingerprinting technique for the cloud environment to protect numeric attributes in relational databases for digital privacy management. The proposed solution with the novel fingerprinting scheme is robust and efficient. It can address challenges such as embedding secure data over the cloud, essential to secure relational databases. The proposed technique provides a decoding accuracy of 100%, 90%, and 40% for 10% to 30%, 40%, and 50% of deleted records.

Association of Blood Groups with the Severity and Outcome of COVID-19 Infection in Children.

The objective of this study was to find out the association of ABO blood groups with the severity and outcome of corona virus disease 2019 (COVID-19) in children. It included all laboratory-confirmed cases of COVID-19 and post-COVID multisystem inflammatory syndrome in children (MIS-C)/ Kawasaki disease (KD) like illness, admitted from March to September, 2020 to The Children's Hospital, Lahore. Out of 66 children, 45 (68.2%) were COVID-19 and 21 (31.8%) MIS-C/KD temporally associated with SARS-C0V-2. The mean age was 7.9 ± 4.2 years. Majority of children had mild to moderate illness 38 (57.6%), while 23 (34.8%) had severe or critical disease. Among all patients, 24 (36.4%) had some underlying comorbidity. Blood group A was significantly associated with severe and critical disease (p=0.030). COVID-19 in children had generally a good outcome, but children with blood group A were more susceptible to severe/critical disease. Key Words: Coronavirus disease 2019, ABO blood groups, Children, Severity, Outcome.

Detection of Diabetic Eye Disease from Retinal Images Using a Deep Learning Based CenterNet Model.

Diabetic retinopathy (DR) is an eye disease that alters the blood vessels of a person suffering from diabetes. Diabetic macular edema (DME) occurs when DR affects the macula, which causes fluid accumulation in the macula. Efficient screening systems require experts to manually analyze images to recognize diseases. However, due to the challenging nature of the screening method and lack of trained human resources, devising effective screening-oriented treatment is an expensive task. Automated systems are trying to cope with these challenges; however, these methods do not generalize well to multiple diseases and real-world scenarios. To solve the aforementioned issues, we propose a new method comprising two main steps. The first involves dataset preparation and feature extraction and the other relates to improving a custom deep learning based CenterNet model trained for eye disease classification. Initially, we generate annotations for suspected samples to locate the precise region of interest, while the other part of the proposed solution trains the Center Net model over annotated images. Specifically, we use DenseNet-100 as a feature extraction method on which the one-stage detector, CenterNet, is employed to localize and classify the disease lesions. We evaluated our method over challenging datasets, namely, APTOS-2019 and IDRiD, and attained average accuracy of 97.93% and 98.10%, respectively. We also performed cross-dataset validation with benchmark EYEPACS and Diaretdb1 datasets. Both qualitative and quantitative results demonstrate that our proposed approach outperforms state-of-the-art methods due to more effective localization power of CenterNet, as it can easily recognize small lesions and deal with over-fitted training data. Our proposed framework is proficient in correctly locating and classifying disease lesions. In comparison to existing DR and DME classification approaches, our method can extract representative key points from low-intensity and noisy images and accurately classify them. Hence our approach can play an important role in automated detection and recognition of DR and DME lesions.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images.

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Association of Blood Groups with the Severity and Outcome of COVID-19 Infection in Children.

The objective of this study was to find out the association of ABO blood groups with the severity and outcome of corona virus disease 2019 (COVID-19) in children. It included all laboratory-confirmed cases of COVID-19 and post-COVID multisystem inflammatory syndrome in children (MIS-C)/ Kawasaki disease (KD) like illness, admitted from March to September, 2020 to The Children's Hospital, Lahore. Out of 66 children, 45 (68.2%) were COVID-19 and 21 (31.8%) MIS-C/KD temporally associated with SARS-C0V-2. The mean age was 7.9 ± 4.2 years. Majority of children had mild to moderate illness 38 (57.6%), while 23 (34.8%) had severe or critical disease. Among all patients, 24 (36.4%) had some underlying comorbidity. Blood group A was significantly associated with severe and critical disease (p=0.030). COVID-19 in children had generally a good outcome, but children with blood group A were more susceptible to severe/critical disease. Key Words: Coronavirus disease 2019, ABO blood groups, Children, Severity, Outcome.

Exploring the Link Between Emotional Child Abuse and Anorexia Nervosa: A Psychopathological Correlation.

Eating disorders (ED) are well known psychiatric disorders associated with dysregulated eating behaviors and related thoughts and emotions. Common eating disorders are bulimia nervosa (BN), anorexia nervosa (AN), and binge eating disorders (BED). There is an active link between child abuse and eating disorders, emotional child abuse being the important subtype of CA and has a strong comorbid psychopathological relationship with EDs, including AN. The PubMed database was searched for the related articles about child abuse, including emotional childhood maltreatment and their psychopathology associated with EDs, especially AN. No filters were used for the date of publication and article types. Childhood abuse, including physical, sexual, and emotional maltreatment, has an active link with psychopathology associated with dysregulated eating behaviors. However, emotional childhood maltreatment including emotional abuse, neglect, and/or exposure to intimate partner violence (IPV) has been least studied, but studies have shown a strong relationship with the symptoms of anorexia nervosa such as weight concern, negative self-image, and maladaptive emotional response. Emotional dysregulation is the crucial psychopathological factor involved in mediating the effects of emotional childhood maltreatment and symptoms of anorexia nervosa and is strongly associated with long-term morbidity in patients with AN. Conducting more clinical studies in the future would help explore the temporal causation, and this association may help the practitioners to develop new diagnostic and therapeutic strategies in the management of AN.