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An emerging hypothesis linking arsenic toxicity involves altered epigenetic mechanisms, such as DNA methylation. In this study, we examined the relationship between parents’ arsenic exposure and DNA methylation in tissues obtained from 28 infants with spina bifida from Bangladesh. We analyzed arsenic in parents’ toenails using inductively coupled plasma mass spectrometry (ICP-MS). DNA methylation was measured in infants’ dural tissue, buccal swabs, and whole blood using the Illumina Infinium MethylationEPIC BeadChip. We performed epigenome-wide association analyses (EWAS) and tested differentially methylated regions (DMRs). In EWAS, DNA methylation at cg24039697 in dural tissue was positively associated (β = 0.59, p = 7.6 × 10−9) with father’s toenail arsenic concentrations, adjusting for covariates. We did not identify any CpG sites related to father’s arsenic exposure in the other tissues, or any CpG sites related to mother’s arsenic exposure. Gene ontology analysis identified many biological pathways of interest, including the Wnt signaling pathways. We identified several DMRs across the tissues related to arsenic exposure that included probes mapping to genes that have previously been identified in studies of neural tube defects. This study emphasizes the potential impact of arsenic exposure in fathers, often understudied in epidemiological studies, on DNA methylation in a unique neurological tissue specific to spina bifida.

ABSTRACT Background Previously, most researchers explored the association between chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). This study investigates the distinct influence of COVID‐19 vaccination status on patients with both conditions. Objective We investigate the relationship between COPD and CVD in a cohort of 838 individuals who presented with both conditions. Our aim is to understand how these conditions interact and how COVID‐19 vaccination status affects patient outcomes. Methods A retrospective analysis was conducted on 838 patients with COPD and CVD treated at DHQ Hospital in Muzaffargarh, Punjab, Pakistan, from November 2022 to April 2023. We employed multiple logistic regression and the Wilcoxon signed‐rank test to assess the odds ratio and relative risk of COPD in patients with‐CVD under various conditions. Additionally, we analyzed time‐to‐death and survival using Kaplan–Meier methods. Results Findings reveal a 7.95 times higher risk of death in patients without COVID‐19 vaccination compared with those who were vaccinated (95% CI, 6.12–10.33). Conversely, COVID‐19‐vaccinated patients exhibited a 0.221 times lower risk of recovery than their nonvaccinated counterparts (95% CI, 0.08–0.60). We also observed significant differences in time‐to‐death and recovery based on the presence of COPD and CVD, with vaccinated patients generally experiencing milder disease. Conclusion Our study assessed the impact of COVID‐19 vaccination status on patient outcomes in patients with overlapping COPD and CVD. Individuals diagnosed with COPD and CVD display significant differences in terms of their probability of survival, with those who have received vaccinations.

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed ''MobVGG.” This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience.

Abstract Background Spina bifida, a developmental malformation of the spinal cord, is associated with high rates of mortality and disability. Although folic acid-based preventive strategies have been successful in reducing rates of spina bifida, some areas continue to be at higher risk because of chemical exposures. Bangladesh has high arsenic exposures through contaminated drinking water and high rates of spina bifida. This study examines the relationships between mother’s arsenic exposure, folic acid, and spina bifida risk in Bangladesh. Methods We conducted a hospital-based case-control study at the National Institute of Neurosciences & Hospital (NINS&H) in Dhaka, Bangladesh, between December 2016 and December 2022. Cases were infants under age one year with spina bifida and further classified by a neurosurgeon and imaging. Controls were drawn from children seen at NINS&H and nearby Dhaka Shishu Hospital. Mothers reported folic acid use during pregnancy, and we assessed folate status with serum assays. Arsenic exposure was estimated in drinking water using graphite furnace atomic absorption spectrophotometry (GF-AAS) and in toenails using inductively coupled plasma mass spectrometry (ICP-MS). We used logistic regression to examine the associations between arsenic and spina bifida. We used stratified models to examine the associations between folic acid and spina bifida at different levels of arsenic exposure. Results We evaluated data from 294 cases of spina bifida and 163 controls. We did not find a main effect of mother’s arsenic exposure on spina bifida risk. However, in stratified analyses, folic acid use was associated with lower odds of spina bifida (adjusted odds ratio [OR]: 0.50, 95% confidence interval [CI]: 0.25-1.00, p = 0.05) among women with toenail arsenic concentrations below the median value of 0.46 µg/g, and no association was seen among mothers with toenail arsenic concentrations higher than 0.46 µg/g (adjusted OR: 1.09, 95% CI: 0.52–2.29, p = 0.82). Conclusions Mother’s arsenic exposure modified the protective association of folic acid with spina bifida. Increased surveillance and additional preventive strategies, such as folic acid fortification and reduction of arsenic, are needed in areas of high arsenic exposure.

This research work targets to evaluate the floristic composition of Mahseer National Park, Azad Jammu and Kashmir (AJK). Field data was recorded from fifteen different sites. The Quadrat method was used for vegetation sampling while the exact location of each site, altitude, exposure and geographical coordinates were documented by using geographical positioning system (GPS). To analyze the significance of environmental variables, multivariate statistical analysis was carried out by using two-way clustering, canonical correspondence analysis (CCA) and general linear model (GLM) response curve analysis. Floristically, 109 plant species belonging to 45 families were recorded. Among families, Poaceae was most commonly distributed, accounting for 15 species in total. Two-way cluster analysis categorized the vegetation into four major plant communities. CCA was used to analyze the vegetation-environment relation. Plant species showed a significant correlation response against altitudinal gradient, total nitro-gen, electrical conductivity and calcium contents. The GLM response curve and IVI demonstrated that Cynodon dactylon was the most dominant species followed by Dalbergia sissoo and Adhatoda zeylinica. This study provided the baseline information about the eco-floristic composition. It suggested that the area is floristically rich, and needs to be analyzed in detail by future researchers.

Abstract Depression is a multifactorial disease with unknown etiology affecting globally. It’s the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies.

Companies with diverse product offerings rely on customer reviews to gauge product reception. Following a purchase, customers often share their opinions on the website. Prospective buyers, prior to deciding, typically peruse these reviews to inform their choices. Analysing such feedback, whether positive or negative, holds paramount importance for companies seeking to improve product quality. Researchers are actively exploring methods to categorize comments based on sentiment scores. Notably, customers may express their reviews in Arabic text. Despite challenges such as the structure and morphology of Arabic text, a scarcity of machine-readable Arabic dictionaries, and limited tools for handling Arabic text, minimal progress has been made in the analysis of Arabic reviews. While some attempts have been undertaken, they have achieved suboptimal accuracy. In response, the authors propose a hybrid deep learning model comprising a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with GlobalMaxPooling. Through multiple iterations, the authors fine-tuned the proposed model and applied it to the publicly available Arabic Reviews dataset, achieving a notable 95% accuracy, precision, recall, and F1 score. The results indicate that, when compared to alternative models, the proposed model exhibits superior accuracy.

Abstract Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

This review article provides a comprehensive overview of the multidisciplinary management of giant cell tumors of the bone (GCTB). GCTB is a rare, intermediate-grade, benign, locally aggressive osteolytic neoplasm that primarily affects the epiphyses of long bones in individuals aged 20–45, with a female predominance. The article discusses the epidemiology, clinical features, and various management approaches for GCTB.The article highlights the importance of GCTB, which can resemble other bone lesions, so an accurate diagnosis is essential. A biopsy is a definitive way to diagnose GCTB. While, radiography includes MRI, PET scans, and bone scans to help diagnose and stage GCTB. The Campanacci grading system, based on radiographic appearance and soft tissue involvement, is commonly used to stage GCTB.Surgical management options for GCTB include intralesional curettage, wide marginal resection, and en-bloc resection, depending on the location and extent of the tumor. In challenging anatomical locations, such as the axial skeleton, intralesional curettage may be the only viable option to preserve function and minimize surgical morbidity. However, it is associated with a high rate of local recurrence. Wide marginal resection is effective in reducing recurrence rates but may result in significant morbidity and require bone defect reconstruction. En-bloc resection is preferred for tumors with soft tissue extension or involvement of expendable bones.In cases where complete surgical resection is not feasible or carries a high risk of morbidity, alternative treatment options such as radiation therapy, arterial embolization, and systemic therapy with bisphosphonates (zoledronic acid) and denosumab are valuable tools in achieving disease control and improving patient outcomes. Radiotherapy is employed for cases where the disease has recurred locally and cannot be removed surgically. Arterial embolization involves interrupting the blood supply to tumors in areas that cannot be operated on. Bisphosphonates are drugs that can be given before or after surgery to reduce the chances of recurrence by inhibiting osteoclast activity.Denosumab, a monoclonal antibody that targets RANKL, has shown promising results in reducing tumor size, facilitating surgical treatment, and improving outcomes. In conclusion, this review provides valuable insights into the epidemiology, clinical features, and multidisciplinary management approaches for GCTB. Accurate diagnosis, appropriate staging, and individualized treatment strategies are essential for optimizing patient outcomes in GCTB management.

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.

Background: Scoliosis is defined as a Three-dimensional deformity that can be diagnosed by measuring the spinal curves. For measuring the curvature, we employ Cobb’s method to calculate Cobb’s angle on the anteroposterior radiograph of the spine. The commonest type of scoliosis is adolescent idiopathic scoliosis (AIS) that is seen in adolescent females. Our literature research revealed a scarcity of systematic reviews regarding the role of Scoliosis specific exercises (SSE) in reducing Cobb’s angle in scoliosis. This review of the literature evaluates the role of SSE compared to the standard treatment practices in reducing Cobb’s angle in scoliosis patients. Methods: The literature was scrutinized according to PRISMA guidelines where PubMed, Google Scholar, and Cochrane Library were searched for randomized controlled trials in the English language with the search words “Cobb’s angle”, “physiotherapy”, “outcomes”, “scoliosis specific exercises”, “correction of” and “scoliosis” in different combinations published between 2016 to 2022. One group i.e. interventional group consisted of SSE with standard care while the other group i.e. control group did not receive SSE. Mean ± SD was used to measure the change in Cobb’s angle. The difference was pooled by standardized mean difference (SMD) into forest plots. The risk of bias was assessed by the Oxford quality scoring system (OQSS). Results: The SMD was pooled from five studies where SMD remained -0.717 (-1.142, -0.291) (CI=95%, p-value< 0.05) between the intervention and control group. Heterogeneity by the I2 test was reported to be moderate and non-significant (I2 = 52.42%, p-value>0.05) Conclusion: We concluded that SSE produced a decrease in Cobb’s angle compared to the group without SSE. However, good quality randomized controlled trials with larger sample sizes and longer follow-ups are needed to strengthen the role of SSE. Keywords: Low Angle Scattering, Physical Therapy Modalities, Scoliosis.

In developing countries, smart grids are nonexistent, and electricity theft significantly hampers power supply. This research introduces a lightweight deep-learning model using monthly customer readings as input data. By employing careful direct and indirect feature engineering techniques, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), UMAP (Uniform Manifold Approximation and Projection), and resampling methods such as Random-Under-Sampler (RUS), Synthetic Minority Over-sampling Technique (SMOTE), and Random-Over-Sampler (ROS), an effective solution is proposed. Previous studies indicate that models achieve high precision, recall, and F1 score for the non-theft (0) class, but perform poorly, even achieving 0 %, for the theft (1) class. Through parameter tuning and employing Random-Over-Sampler (ROS), significant improvements in accuracy, precision (89 %), recall (94 %), and F1 score (91 %) for the theft (1) class are achieved. The results demonstrate that the proposed model outperforms existing methods, showcasing its efficacy in detecting electricity theft in non-smart grid environments.

Cardiovascular disease is one of the leading causes of death in the world. Heart failure is a cardiovascular disease in which the heart is unable to pump sufficient blood to fulfill the body’s requirements and can lead to fluid overload. Traditional solutions are not adequate to address the progression of heart failure. Herein, we report a body-mounted wearable sensor to monitor the parameters related to heart failure. These include heart rate, blood oxygen saturation, thoracic impedance, and activity status. The device is compact and wearable and measures the parameters continuously in real time. The device is an Internet of Things (IoT) device connected with a cloud-based database enabling the parameters to be visualized on a mobile application.

Objective: This study aims to assess the efficacy of Ultrasonographic Guided Tunneled Hemodialysis Catheters in patients with ESRD. The primary objectives include assessing catheter placement success, blood flow adequacy, and the prevention of catheter related blood stream infections. Study Design: A cross-sectional study. Place and Duration of Study: The study was conducted at the Department of Nephrology, Central Park Medical College and Hospital Lahore, Pakistan from December 2022 to May 2023. Methods: In this study, enrolled 40 end stage renal disease patients aged 20 to 60 years. Tunneled Hemodialysis Catheters (THCs) were inserted under ultrasonographic guidance, predominantly in the right internal jugular vein. Demographic and clinical data were recorded, and blood flow was assessed for dialysis adequacy. The study followed STROBE guidelines and obtained ethical clearance. Results: Of the 40 patients, 50% were male, 85% had hypertension, and 87.5% had diabetes. Ultrasonography guided Tunneled Hemodialysis Catheters were predominantly placed in the right internal jugular vein (55%) with overall success rate of 100 percent. Mean blood flow was 317 ml/minute, and patients had a history of 4.29 ± 1.18 previous catheters. No CRBSIs were reported within the study period. Conclusion: Ultrasonographic guided Tunneled Hemodialysis Catheters demonstrated effective placement, adequate blood flow, and a notable absence of catheter related blood stream infections. The study contributes valuable insights into the role of Tunneled Hemodialysis Catheters in ESRD care, emphasizing their significance as an interim solution while awaiting arteriovenous fistula maturation. How to cite this: Sikandar MZ, Javed M, Waheed MA, Khan AA, Amjad H, Moeen SMH. Navigating Vascular Access Challenges in End-Stage Renal Disease: A Comprehensive Study of Ultrasonographic Guided Tunneled Hemodialysis Catheters. Life and Science. 2024; 5(4): 477-481. doi: http://doi.org/10.37185/LnS.1.1.541

Cardiovascular diseases are a leading cause of death worldwide. They mainly include coronary artery disease, rheumatic heart disease, andcerebrovascular disease, and. Cardiovascular diseases can be better managed and diagnosed using wearable devices. Wearable devices, in comparison to traditional cardiovascular diagnostic tools, are not only inexpensive but also have the potential to provide continuous real-time monitoring. This paper reviews some of the recent advances in cardiovascular wearable devices. It discusses traditional implantable devices for cardiovascular diseases as well as wearable devices. The different types of wearable devices are categorized based on different technologies, namely using galvanic contact, photoplethysmography (PPG), and radio frequency (RF) waves. It also highlights the use of artificial intelligence (AI) in cardiovascular disease diagnostics as well as future perspectives on cardiovascular devices.