Supervised Machine Learning-Based Prediction of Hydrogen Storage Classes Utilizing Dibenzyltoluene as an Organic Carrier.

Dibenzyltoluene (H0-DBT), a Liquid Organic Hydrogen Carrier (LOHC), presents an attractive solution for hydrogen storage due to its enhanced safety and ability to store hydrogen in a concentrated liquid form. The utilization of machine learning proves essential for accurately predicting hydrogen storage classes in H0-DBT across diverse experimental conditions. This study focuses on the classification of hydrogen storage data into three classes, low-class, medium-class and high-class, based on the hydrogen storage capacity values. We introduce Hydrogen Storage Prediction with the Support Vector Machine (HSP-SVM) model to predict the hydrogen storage classes accurately. The performance of the proposed HSP-SVM model was investigated using various techniques, which included 5-Fold Cross Validation (5-FCV), Resubstitution Validation (RV), and Holdout Validation (HV). The accuracy of the HV approach for the low, medium, and high class was 98.5%, 97%, and 98.5%, respectively. The overall accuracy of HV approach reached 97% with a miss clarification rate of 3%, whereas 5-FCV and RV possessed an overall accuracy of 93.9% with a miss clarification rate of 6.1%. The results reveal that the HV approach is optimal for predicting the hydrogen storage classes accurately.

"Smartphone as an educational tool" the perception of dental faculty members of all the dental colleges of Khyber Pakhtunkhwa - Pakistan.

BACKGROUND: With the increasing advancement in the field of information technology, it's about time we realize that our future will be shaped by this field. With more and more people using smartphones, we need to adapt them to the medical field. Already many advancements in medical field are done thanks to the advancement of computer science. But we need to implement this into our teaching and learning as well. Almost all students and faculty members use smartphones in one way or another if we can utilize the smartphone to enhance the learning opportunities for our medical students, it would greatly benefit them. But before the implementation, we need to find out if our faculty is willing to adopt this technology. The objective of this study is to find out what are the perceptions of dental faculty members about using a smartphone as a teaching tool. METHODOLOGY: A validated questionnaire was distributed among the faculty members of all the dental colleges of KPK. The questionnaire had 2 sections. First one contains information regarding the demographics. The second one had questions related to the faculty members' perception regarding using a smartphone as a teaching tool. RESULTS: The results of our study showed that the faculty (Mean 2.08) had positive perceptions regarding using a smartphone as a teaching tool. CONCLUSION: Most of the Dental Faculty members of KPK agree that smartphone can be used as a teaching tool, and it can have better outcomes if proper applications and teaching strategies are used.

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16.

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

Classification and Detection of COVID-19 and Other Chest-Related Diseases Using Transfer Learning.

COVID-19 has infected millions of people worldwide over the past few years. The main technique used for COVID-19 detection is reverse transcription, which is expensive, sensitive, and requires medical expertise. X-ray imaging is an alternative and more accessible technique. This study aimed to improve detection accuracy to create a computer-aided diagnostic tool. Combining other artificial intelligence applications techniques with radiological imaging can help detect different diseases. This study proposes a technique for the automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images of suspected patients by applying transfer learning (TL) algorithms. For this purpose, two balanced datasets, Dataset-1 and Dataset-2, were created by combining four public databases and collecting images from recently published articles. Dataset-1 consisted of 6000 chest X-ray images with 1500 for each class. Dataset-2 consisted of 7200 images with 1200 for each class. To train and test the model, TL with nine pretrained convolutional neural networks (CNNs) was used with augmentation as a preprocessing method. The network was trained to classify using five classifiers: two-class classifier (normal and COVID-19); three-class classifier (normal, COVID-19, and viral pneumonia), four-class classifier (normal, viral pneumonia, COVID-19, and tuberculosis (Tb)), five-class classifier (normal, bacterial pneumonia, COVID-19, Tb, and pneumothorax), and six-class classifier (normal, bacterial pneumonia, COVID-19, viral pneumonia, Tb, and pneumothorax). For two, three, four, five, and six classes, our model achieved a maximum accuracy of 99.83, 98.11, 97.00, 94.66, and 87.29%, respectively.

Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning.

Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology.

The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.

Towards Parallel Selective Attention Using Psychophysiological States as the Basis for Functional Cognition.

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses.

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Rainfall Prediction System Using Machine Learning Fusion for Smart Cities.

Precipitation in any form-such as rain, snow, and hail-can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naïve Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models.

A Novel Grayscale Image Encryption Scheme Based on the Block-Level Swapping of Pixels and the Chaotic System.

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

To assess the role of cataract extraction in glaucoma management by its intraocular pressure lowering effect.

OBJECTIVE: To measure the mean change of intraocular pressure in glaucoma patients with cataract after uncomplicated phacoemulsification surgery with intraocular lens implanted in capsular bag. METHODS: The quasi-experimental study was conducted at the Ophthalmology Department of Pakistan Institute of Medical Sciences Shaheed Zulfiqar Ali Bhutto Medical University, Islamabad, Pakistan, from June 11 to December 10, 2018, and comprised patients who had uncomplicated cataract extraction by phacoemulsification with intraocular lens implant in the capsular bag in glaucomatous eyes of age 30-80 years. Visual acuity, intraocular pressure, slit lamp examination, fundoscopy, visual fields, details about topical medication and relevant history were recorded not more than 5 days before cataract extraction. Intraocular pressure was recorded using Goldman's applanation tonometer one day before surgery, and post-surgery 1 month and 3 months. Data was analysed using SPSS 20. RESULTS: Of the 40 patients, 19(47.50%) were males and 21(52.50%) were females. The overall mean age was 52.23±9.44 years. Mean pre-operation intraocular pressure was 20.42±1.69mmHg, while at 1 month post-surgery it was 18.55±0.90mmHg and at 3 months it was 17.03±1.19mmHg (p=0.0001). CONCLUSIONS: There was a significant change in intraocular pressure readings in glaucoma patients with cataract after uncomplicated phacoemulsification surgery with intraocular lens implanted in capsular bag.

A novel nonsense variant in SLC24A4 causing a rare form of amelogenesis imperfecta in a Pakistani family.

BACKGROUND: Amelogenesis imperfecta (AI) is a highly heterogeneous group of hereditary developmental abnormalities which mainly affects the dental enamel during tooth development in terms of its thickness, structure, and composition. It appears both in syndromic as well as non-syndromic forms. In the affected individuals, the enamel is usually thin, soft, rough, brittle, pitted, chipped, and abraded, having reduced functional ability and aesthetics. It leads to severe complications in the patient, like early tooth loss, severe discomfort, pain, dental caries, chewing difficulties, and discoloration of teeth from yellow to yellowish-brown or creamy type. The study aimed to identify the disease-causing variant in a consanguineous family. METHODS: We recruited a consanguineous Pashtun family of Pakistani origin. Exome sequencing analysis was followed by Sanger sequencing to identify the pathogenic variant in this family. RESULTS: Clinical analysis revealed hypomaturation AI having generalized yellow-brown or creamy type of discoloration in affected members. We identified a novel nonsense sequence variant c.1192C > T (p.Gln398*) in exon-12 of SLC24A4 by using exome sequencing. Later, its co-segregation within the family was confirmed by Sanger sequencing. The human gene mutation database (HGMD, 2019) has a record of five pathogenic variants in SLC24A4, causing AI phenotype. CONCLUSION: This nonsense sequence variant c.1192C > T (p.Gln398*) is the sixth disease-causing variant in SLC24A4, which extends its mutation spectrum and confirms the role of this gene in the morphogenesis of human tooth enamel. The identified variant highlights the critical role of SLC24A4 in causing a rare AI type in humans.

Recent update on basic mechanisms of spinal cord injury.

Spinal cord injury (SCI) is a life-shattering neurological condition that affects between 250,000 and 500,000 individuals each year with an estimated two to three million people worldwide living with an SCI-related disability. The incidence in the USA and Canada is more than that in other countries with motor vehicle accidents being the most common cause, while violence being most common in the developing nations. Its incidence is two- to fivefold higher in males, with a peak in younger adults. Apart from the economic burden associated with medical care costs, SCI predominantly affects a younger adult population. Therefore, the psychological impact of adaptation of an average healthy individual as a paraplegic or quadriplegic with bladder, bowel, or sexual dysfunction in their early life can be devastating. People with SCI are two to five times more likely to die prematurely, with worse survival rates in low- and middle-income countries. This devastating disorder has a complex and multifaceted mechanism. Recently, a lot of research has been published on the restoration of locomotor activity and the therapeutic strategies. Therefore, it is imperative for the treating physicians to understand the complex underlying pathophysiological mechanisms of SCI.

High-intensity focused ultrasound: past, present, and future in neurosurgery.

Since Lynn and colleagues first described the use of focused ultrasound (FUS) waves for intracranial ablation in 1942, many strides have been made toward the treatment of several brain pathologies using this novel technology. In the modern era of minimal invasiveness, high-intensity focused ultrasound (HIFU) promises therapeutic utility for multiple neurosurgical applications, including treatment of tumors, stroke, epilepsy, and functional disorders. Although the use of HIFU as a potential therapeutic modality in the brain has been under study for several decades, relatively few neuroscientists, neurologists, or even neurosurgeons are familiar with it. In this extensive review, the authors intend to shed light on the current use of HIFU in different neurosurgical avenues and its mechanism of action, as well as provide an update on the outcome of various trials and advances expected from various preclinical studies in the near future. Although the initial technical challenges have been overcome and the technology has been improved, only very few clinical trials have thus far been carried out. The number of clinical trials related to neurological disorders is expected to increase in the coming years, as this novel therapeutic device appears to have a substantial expansive potential. There is great opportunity to expand the use of HIFU across various medical and surgical disciplines for the treatment of different pathologies. As this technology gains recognition, it will open the door for further research opportunities and innovation.

Synthesis, monomer conversion, and mechanical properties of polylysine based dental composites.

OBJECTIVE: The aim of this study was to synthesize a new bioactive and antibacterial composite by incorporating reactive calcium phosphate and antibacterial polylysine into a resin matrix and evaluate the effect of these fillers on structural analysis, degree of monomer conversion, mechanical properties, and bioactivity of these newly developed polypropylene based dental composites. METHODOLOGY: Stock monomers were prepared by mixing urethane dimethacrylate and polypropylene glycol dimethacrylate and combined with 40 wt% silica to make experimental control (E-C). The other three experimental groups contained a fixed percentage of silica (40 wt%), monocalcium phosphate monohydrate, and ß-tri calcium phosphate (5 wt% each) with varying amounts of polylysine (PL). These groups include E-CCP0 (0 wt% PL), E-CCP5 (5 wt% PL) and E-CCP10 (10 wt% PL). The commercial control used was Filtek™ Z250 3M ESPE. The degree of conversion was assessed by using Fourier transform infrared spectroscopy (FTIR). Compressive strength and Vicker's micro hardness testing were evaluated after 24 h of curing the samples. For bioactivity, prepared samples were placed in simulated body fluid for 0, 1, 7, and 28 days and were analyzed using a scanning electron microscope (SEM). SPSS 23 was used to analyze the data and one-way ANOVA and post hoc tukey's test were done, where the significant level was set ≤0.05. RESULTS: Group E-C showed better mechanical properties than other experimental and commercial control groups. Group E-C showed the highest degree of conversion (72.72 ± 1.69%) followed by E-CCP0 (72.43 ± 1.47%), Z250 (72.26 ± 1.75%), E-CCP10 (71.07 ± 0.19%), and lowest value was shown by E-CCP5 (68.85 ± 7.23%). In shear bond testing the maximum value was obtained by E-C. The order in decreasing value of bond strength is E-C (8.13 ± 3.5 MPa) > Z250 (2.15 ± 1.1 MPa) > E-CCP10 (2.08 ± 2.1 MPa) > E-CCP5 (0.94 ± 0.8 MPa) > E-CCP0 (0.66 ± 0.2 MPa). In compressive testing, the maximum strength was observed by commercial control i.e., Z250 (210.36 ± 18 MPa) and E-C (206.55 ± 23 MPa), followed by E-CCP0 (108.06 ± 19 MPa), E-CCP5 (94.16 ± 9 MPa), and E-CCP10 (80.80 ± 13 MPa). The maximum number of hardness was shown by E-C (93.04 ± 8.23) followed by E-CCP0 (38.93 ± 9.21) > E-CCP10 (35.21 ± 12.31) > E-CCP5 (34.34 ± 12.49) > Z250 (25 ± 2.61). SEM images showed that the maximum apatite layer as shown by E-CCP10 and the order followed as E-CCP10 > E-CCP5 > E-CCP0 >Z250> E-C. CONCLUSION: The experimental formulation showed an optimal degree of conversion with compromised mechanical properties when the polylysine percentage was increased. Apatite layer formation and polylysine at the interface may result in remineralization and ultimately lead to the prevention of secondary caries formation.

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence.

A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

Brachyolmia, dental anomalies and short stature (DASS): Phenotype and genotype analyses of Egyptian and Pakistani patients.

Brachyolmia is a heterogeneous group of developmental disorders characterized by a short trunk, short stature, scoliosis, and generalized platyspondyly without significant deformities in the long bones. DASS (Dental Abnormalities and Short Stature), caused by alterations in the LTBP3 gene, was previously considered as a subtype of brachyolmia. The present study investigated three unrelated consanguineous families (A, B, C) with Brachyolmia and DASS from Egypt and Pakistan. In our Egyptian patients, we also observed hearing impairment. Exome sequencing was performed to determine the genetic causes of the diverse clinical conditions in the patients. Exome sequencing identified a novel homozygous splice acceptor site variant (LTBP3:c.3629-1G > T; p. ?) responsible for DASS phenotypes and a known homozygous missense variant (CABP2: c.590T > C; p.Ile197Thr) causing hearing impairment in the Egyptian patients. In addition, two previously reported homozygous frameshift variants (LTBP3:c.132delG; p.Pro45Argfs*25) and (LTBP3:c.2216delG; p.Gly739Alafs*7) were identified in Pakistani patients. This study emphasizes the vital role of LTBP3 in the axial skeleton and tooth morphogenesis and expands the mutational spectrum of LTBP3. We are reporting LTBP3 variants in seven patients of three families, majorly causing brachyolmia with dental and cardiac anomalies. Skeletal assessment documented short webbed neck, broad chest, evidences of mild long bones involvement, short distal phalanges, pes planus and osteopenic bone texture as additional associated findings expanding the clinical phenotype of DASS. The current study reveals that the hearing impairment phenotype in Egyptian patients of family A has a separate transmission mechanism independent of LTBP3.