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Wearing appropriate personal protective equipment (PPE) for spraying pesticides is one of the methods to reduce dermal exposure to toxic pesticides. The main issues with such PPEs are comfortability and durable functionality. These issues were addressed by developing a bi-layer fabric treated with functionalized iron molybdate (Fe2(MoO4)3) nanoparticles (NPs). Three structures of bi-layer fabrics were developed to get the most comfortable fabric one with an upper layer of cotton (1*1 woven) and an underlayer of polyester (3/1 twill weave). The Fe2(MoO4)3 NPs were synthesized and functionalized with two silanes, i.e., Octadecyltrimethoxysilane and 1,2 Bis(triethoxysilyl)ethane. Fourier transform-infrared spectroscopy and thermogravimetric analysis were done to confirm the binding of silanes with NPs. Functionalized Fe2(MoO4)3 NPs were coated on selected fabric by dip-dry coating, and surface topography was assessed by Scanning Electron Microscopy (SEM). The water contact angle of 151° was exhibited by modified fabric. The photocatalysis of Benzoepin (C9H6Cl6O3S) by Fe2(MoO4)3 NPs under visible light was 75%. The UPF values obtained were 55 and 50 before and after washing, respectively. The developed superhydrophobic fabric could be efficiently used for durable pesticide-resistant clothing because the pesticide would not be adsorbed/absorbed by the fabric. If absorbed it could be possibly degraded by Fe2(MoO4)3 NPs rescuing the wearer from the hazardous effects of pesticides as well as UV rays. [ABSTRACT FROM AUTHOR]

Background: The objective of the study was to analyze the epidemiology of risk factors associated with fatal road traffic accidents in Lahore-Punjab, Pakistan. Methods: An Analytical cross-sectional study was conducted in Lahore, between July 2019 and September 2019. Data of 290 road traffic accidents was collected from office of Rescue 1122. To collect data systematic Random Sampling technique was used. Data was collected on preformed pretested questionnaire. Data was entered and analyzed in SPSS version 23. Chisquare test of significance was applied, p-value of less than 0.05 was considered as significant. Results: Mean age of victims of RTA was 31.4±14.7. About 28.3% of the total accidents were observed among 20-29 years age group. Out of total 290 victims, 239 (82.4%) were males. Majority, 228(78.6%) of the accidents involved Motorcyclists. Significant association was found between fatal accidents and injury to head (p-value = 0.00), Concussion (p-value=0.00), with No fractures of limbs (p-value= 0.04) and critical condition of victim (p-value=0.00). Conclusion: Road Traffic Accidents were more common among males of 20-29 years age. Most of the victims were motorcyclists. Head Injuries, concussion and critical conditions of victims has significant association with fatality. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease. Methods: In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM). Results: Our results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions. Conclusion: This VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches. [ABSTRACT FROM AUTHOR]

A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work. [ABSTRACT FROM AUTHOR]

Background: Provision of health information and delivery of health care services by means of electronic information and communications technology (ICT) to distant patients, separated by geography, time, social and cultural barriers is termed as telehealth. Objective: To evaluate the provision of telehealth services for Gynae and Obstetrics in relation to satisfaction of patients and fetal outcomes. Methods: An analytical cross-sectional study was conducted between 18-5-21 to 31-12-21 in Gynae and obstetrics unit of Farooq Hospital Westwood branch. A sample of 108 respondents was collected through simple random sampling technique. In order to analyze the data, SPSS version 23 was utilized, and chi square test was used to determine the associations between variables fixing p value at less than 0.05. Results: Most common device used for appointment of telehealth services was smartphone (87%) (p=0.02) and most important barrier in telehealth services was network issues reported by (55.5%) (p=0.01) of respondents. Majority of the respondents (84.1%) were satisfied with telehealth service utilization (p=0.00). Strong association was seen between utilization of services and easing of appointment (p=0.00), understandability of language (p=0.00), need based services (p=0.00), convenience (p=0.00), comfort (p=0.00), cost effectiveness (p=0.01), better standard of care (p=0.01), careful response of physicians (p=0.02) and sympathetic attitude (p=0.00). Conclusion: High satisfaction rate was observed with provision of telehealth services in Pakistan and most important barrier was network issues. [ABSTRACT FROM AUTHOR]

Objective: Schizophrenia (SZ) is a functional mental condition that has a significant impact on patients' social lives. As a result, accurate diagnosis of SZ has attracted researchers' interest. Based on previous research, resting-state functional magnetic resonance imaging (rsfMRI) reported neural alterations in SZ. In this study, we attempted to investigate if dynamic functional connectivity (dFC) could reveal changes in temporal interactions between SZ patients and healthy controls (HC) beyond static functional connectivity (sFC) in the cuneus, using the publicly available COBRE dataset. Methods: Sliding windows were applied to 72 SZ patients' and 74 healthy controls' (HC) rsfMRI data to generate temporal correlation maps and, finally, evaluate mean strength (dFC-Str), variability (dFC-SD and ALFF) in each window, and the dwelling time. The difference in functional connectivity (FC) of the cuneus between two groups was compared using a two-sample t-test. Results: Our findings demonstrated decreased mean strength connectivity between the cuneus and calcarine, the cuneus and lingual gyrus, and between the cuneus and middle temporal gyrus (TPOmid) in subjects with SZ. Moreover, no difference was detected in variability (standard deviation and the amplitude of low-frequency fluctuation), the dwelling times of all states, or static functional connectivity (sFC) between the groups. Conclusions: Our verdict suggest that dynamic functional connectivity analyses may play crucial roles in unveiling abnormal patterns that would be obscured in static functional connectivity, providing promising impetus for understanding schizophrenia disease. [ABSTRACT FROM AUTHOR]

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity. [ABSTRACT FROM AUTHOR]