IMPACTS OF TRANSFUSION BLOOD ON FUNCTIONS OF KIDNEYS AFTER ORTHOPEDIC SURGERY

Objective: The impact of the transfusion of blood before the surgery on the function of the kidneys remained as a part of subject in different research works. The aim of this study is to investigate the impacts of transfusion of blood on the function of kidneys after surgery in the patients who underwent orthopedic surgical intervention. Methodology: Total one hundred and thirty-six patients who underwent surgical intervention because of different orthopedic pathologies from July 2018 to January 2019 were under evaluation. The division of the patients carried out into 2 groups depending upon the amount of the transfusion of blood. Total 69.80% (n: 95) patients who found with transfusion of lower than 3 units were the part of Group-1 & 30.20% (n: 41) patients who received three or greater than 3 blood units were the part of Group-2. Results: We found no statistical disparities between the patients of both groups before surgery regarding sex, hypertension, DM, failure of the kidney & habits of cigarette smoking. No disparities between the patients of both group regarding stay in hospital after surgery, pulmonary and associated other complications & mortality. When the comparison of the blood parameters of the patients of both groups carried out describing the functions of kidneys and other organ systems, we detected no significant disparity. Conclusion: Transfusions of blood have no negative impacts on after surgery BUN & levels of creatinine in the patients with underwent surgery for the orthopedic complications. KEY WORDS: Transfusion, Arthroplasty, Blood, Kidney, Orthopedic, Surgery, Pulmonary, Intervention

Graphene: The Material of Today and Tomorrow

Graphene has astounding aptitudes owing to its unique band structure characteristics outlining its enhanced electrical capabilities for a material with the highest characteristic mobility known to exist at room temperature. Graphene, one-atom-thick, a planar sheet of carbon atoms densely packed in a honeycomb crystal lattice, has grabbed considerable attention due to its exceptional electronic and optoelectronic properties. Reported properties and applications of this two-dimensional form of carbon structure have opened up new opportunities for the future devices and application in various fields. Though graphene is recognized as one of the best electronic materials, synthesizing single sheet of graphene has been less explored. This review article aims to present an overview of the progression of research in graphene, in the area of synthesis, properties and applications. Wherever applicable, the limitations of present knowledge base and future research directions have also been discusse

Pollination of Grewia asiatica (Malvaceae) by Megachile cephalotes (Hymenoptera: Megachilidae): Male vs. Female Pollination

Phalsa, Grewia asiatica is a multi-purpose crop while cross-pollination can significantly improve its reproductive success. Megachile bees (Megachilidae) are the most important group of pollinators of G. asiatica. In this study we observed the foraging behavior of Megachile cephalotes and its ultimate impact on reproductive success of phalsa at Bahawalpur (Punjab), Pakistan. Although visitation rate and stay time were statistically similar in both the sexes but visitation frequency (2.06±0.14 individuals/120 seconds) and pollen deposition (39.35±3.17 pollen grains /stigma/visit) of females were significantly higher than that of males (0.44±0.06 individuals/120 seconds and 12.05±1.19 pollen grains/stigma/visit, respectively). The environmental factors (i.e. ambient temperature, relative humidity, sunlight intensity and wind speed) greatly influenced -either positively or negatively- both the sexes (Pearson’s correlation). Female pollinated fruits were significantly greater in weight (0.41±0.017 g) followed by open (0.31±0.012 g) and male (0.27±0.011 g) pollinated fruits. Percent weight loss remained significantly lower in female pollinated fruits than open and male pollinated fruits until12 hours after harvest. Fruit wrinkling significantly increased with the increase in post-harvest intervals in open, female and male pollinated fruits while fruit color changed only in female pollinated fruits. The results of present study suggest female M. cephalotes as the efficient pollinators of G. asiatica in terms of it reproductive success and post-harvest parameters. Future studies should focus biology and ecology of M. cephalotes with special focus on its artificial nesting

One factor at a time analysis to modify potting technique for manufacturing of bubble-free high-voltage polyester insulated automotive coils

The current study focuses on minimising the bubbles in polyester-insulated ignition coils, which were produced with a defect level of ~21–25% or 210–250 coils per 1000 batch size by using the potting method. This high-level rejection makes a substantial financial impact by increasing waste material, manufacturing, and after-sales costs. Hence, to control the bubbled problem without using expensive and maintenance-heavy techniques, the process parameters in the potting method were alternated and investigated using one factor at a time, which played a vital role in the formation/ reduction of bubbles in the ignition coil insulation. Process parameters, including pre/process heating, the appropriate MEKP/cobalt naphthenate ratio, the pouring amount/increments, and the stirring speeds, reduced the bubble formation per lot from 205 ± 30 to 146 ± 25, 108 ± 21, 61 ± 17, and 10 ± 2 per 1000 lot accordingly. In addition, a comparative study was conducted in terms of performance and life cycle endurance, using Japanese and Indian standards. Furthermore, an after-sale warranty claim also supports the proposed changes in the potting technique. This modification may reduce the after-sales rejection within two years to approximately ~85%. This modification in the potting technique is extremely cost-effective in comparison to expensive processes, i.e., vacuum-pressure impregnation and vacuum impregnation, which require extensive labour and maintenance

APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm

Big Data Management in Drug–Drug Interaction: A Modern Deep Learning Approach for Smart Healthcare

The detection and classification of drug–drug interactions (DDI) from existing data are of high importance because recent reports show that DDIs are among the major causes of hospital-acquired conditions and readmissions and are also necessary for smart healthcare. Therefore, to avoid adverse drug interactions, it is necessary to have an up-to-date knowledge of DDIs. This knowledge could be extracted by applying text-processing techniques to the medical literature published in the form of ‘Big Data’ because, whenever a drug interaction is investigated, it is typically reported and published in healthcare and clinical pharmacology journals. However, it is crucial to automate the extraction of the interactions taking place between drugs because the medical literature is being published in immense volumes, and it is impossible for healthcare professionals to read and collect all of the investigated DDI reports from these Big Data. To avoid this time-consuming procedure, the Information Extraction (IE) and Relationship Extraction (RE) techniques that have been studied in depth in Natural Language Processing (NLP) could be very promising. Since 2011, a lot of research has been reported in this particular area, and there are many approaches that have been implemented that can also be applied to biomedical texts to extract DDI-related information. A benchmark corpus is also publicly available for the advancement of DDI extraction tasks. The current state-of-the-art implementations for extracting DDIs from biomedical texts has employed Support Vector Machines (SVM) or other machine learning methods that work on manually defined features and that might be the cause of the low precision and recall that have been achieved in this domain so far. Modern deep learning techniques have also been applied for the automatic extraction of DDIs from the scientific literature and have proven to be very promising for the advancement of DDI extraction tasks. As such, it is pertinent to investigate deep learning techniques for the extraction and classification of DDIs in order for them to be used in the smart healthcare domain. We proposed a deep neural network-based method (SEV-DDI: Severity-Drug–Drug Interaction) with some further-integrated units/layers to achieve higher precision and accuracy. After successfully outperforming other methods in the DDI classification task, we moved a step further and utilized the methods in a sentiment analysis task to investigate the severity of an interaction. The ability to determine the severity of a DDI will be very helpful for clinical decision support systems in making more accurate and informed decisions, ensuring the safety of the patients

Misuse of Antibiotics in Poultry Threatens Pakistan Communitys Health

A survey was conducted from February 2022 to May 2022 on the usage of antibiotics at a poultry farm in different areas of Multan, Punjab Pakistan. A well-organized questionnaire was used for the collection of data. Sixty poultry farms were surveyed randomly in the Multan district. All of these Farms were using antibiotics. Antibiotics are commonly used for the treatment of diseases. Some are used as preventive medicine and a few are used as growth promotors. neomycin, erythromycin, oxytetracycline, streptomycin, and colistin are the broad-spectrum antibiotics that are being used commercially. Enrofloxacin and Furazolidone are the common antibiotics that are being used in Studies these days. The class of Fluoroquinolones is commonly used in poultry farms. Thirty-three patterns of antibiotic usage were observed at poultry farms. multi-drug practices were also observed on various farms. In this study, 25% of antibiotics are prescribed by the veterans while more than 90 % were acquired from the veterinary store. This study provides information about the antibiotics which are commonly being used in the study location district Multan. It is expected that the finding of this survey will be helpful in the development of new strategies against the misuse of antibiotics on farms

SmartBin: An Approach to Smart Living Community Using IoT Techniques and Tools

Nowadays, individuals are getting steadily dynamic in achieving the possible ways to clean their environment. The concerned teams have initiated other developments to build tidiness. Previously, prior data on filling the trash container was required, which cautions and sends cautioning messages to the city workers for cleaning the trash receptacle on schedule and protecting the city. In this framework, numerous dustbins through urban areas from various regions are associated with utilizing IoT innovation. This program can be used conveniently to verify the status of the dust bin, the garbage in the dust containers, clean the dust bin on time, and maintain the atmosphere's safety and prevent contamination from overflows from the dust containers. So, people don't have to test everyone's work manually, so they'll get a warning if the container is full. A sensor over the garbage container would be placed to detect the full amount of waste, and when it exceeds the excessive volume, a warning will be transmitted to the company office. The proposed framework based on Arduino IDE, cloud computing concept and Load Sensor will help clean any city. Load Sensors are utilized to distinguish the dimension of trash gathered in the containers. The application also gets Latitude and Longitude estimations of the territory where the Garbage Bins are put

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020