COVID-19 vaccine uptake and vaccine hesitancy in Indian patients with cancer: A questionnaire-based survey

Background: Patients with cancer are at a higher risk of severe forms of coronavirus disease 2019 (COVID-19) and mortality. Therefore, widespread COVID-19 vaccination is required to attain herd immunity. Objective(s): We aimed to evaluate the uptake of the COVID-19 vaccine in Indian patients with cancer and to collect information regarding vaccine hesitancy and factors that contributed to vaccine hesitancy. Material(s) and Method(s): This was a questionnaire-based survey conducted between May 7, 2021 and June 10, 2021 in patients aged 45 years and over, with solid tumors. The primary end points of the study were the proportion of Indian patients with cancer aged 45 years and older who had not received the COVID-19 vaccine, and the reasons why these patients had not received the COVID-19 vaccine. Our secondary end points were the proportion of patients with a history of COVID-19 infection, and the proportion of the patients who had vaccine hesitancy. Additionally, we attempted to assess the factors that could impact vaccine hesitancy. Result(s): A total of 435 patients were included in the study. Of these, 348 (80%) patients had not received even a single dose of the COVID-19 vaccine;66 (15.2%) patients had received the first dose, and 21 (4.8%) had received both the doses. Approximately half (47.1%) of the patients reported that they took the COVID-19 vaccine based on the advice from a doctor. The reasons for not taking the COVID-19 vaccine could be considered as vaccine hesitancy in 259 (77%) patients. The two most common reasons were fear in 124 (38%) patients (fear of side-effects and of the impact of the vaccine on the cancer/therapy) and lack of information in 87 (26.7%) patients. On the multivariate analysis, the two factors found to be significantly associated with vaccine hesitancy were a lower educational level (OR, 1.78;95% CI, 1-3.17;P = 0.048) and a lack of prior advice regarding the COVID-19 vaccine (OR, 2.80;95% CI, 1.73-4.53;P < 0.001). Conclusion(s): Vaccine hesitancy is present in over half of our patients, and the most common reasons are a fear of the vaccine impacting the cancer therapy, fear of side-effects, and lack of information. Widespread vaccination can only be attained if systematic programs for education and dissemination of information regarding the safety and efficacy of the COVID-19 vaccine are given as much importance as fortification of the vaccination supply and distribution system.Copyright © 2021 Cancer Research, Statistics, and Treatment Published by Wolters Kluwer - Medknow.

MEDICLOUD: a holistic study on the digital evolution of medical data

The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors' authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.Copyright © 2022 Digital Chinese Medicine

Impact of Covid-19 on the Prevalence of Intrahepatic Cholestasis of Pregnancy

Background: The virulent pathogen SARS-CoV-2 first appeared in the Chinese province of Hubei in December 2019. Pregnant women were a high-risk population in the pandemic because immune system alterations that occur during pregnancy make them more vulnerable to foreign infections. Late-pregnancy cholestasis is a dangerous liver condition that can cause the foetus to experience potentially fatal problems like early birth and stillbirth. In the present study we were testing the Bile acid level during pregnancy patients after covid pandemic. Objective(s): To evaluate the prevalence of intrahepatic cholestasis in pregnant patients after Covid -19 pandemic. Material(s) and Method(s): This cross-sectional study was conducted at department of Dr.fida painless and General Hospital Peshawar from jan 2022 to Dec 2022. We enrolled 186 pregnant patients after fulfilling the inclusion criteria. 5 ml blood sample were also taken from the patients. Serum was extracted and Bile acid test were performed in clinical laboratory. Data were collected in predesign questionnaire. Result(s): Total 186 patients were enrolled in the study with mean age of 37.18+/-6.39 years (Range 18-45 years). The mean value of all enrolled patients was 31.38+/-5.79 with minimum and maximum value of bile acids 20 micromol/L and 40.6.00 micromol/L. In our study 95 (56.5%) of patients belongs to 36 to 45 years of age group followed by age group of 26 to 35 years in which 60 (35.7%) patients and 13 (7.7%) patients were belongs to age group of 18 to 25 years. Practical implication: This study will help the clinical practitioner to take care of pregnant patients in order to avoid the prevalence of intrahepatic cholestasis. Conclusion(s): It is concluded from this research study that prevalence of intrahepatic cholestasis in pregnancy has increased after Covid-19 pandemic.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Acute Bronchiolitis in Term Newborns Following Relaxation in COVID-19 Appropriate Behavior

Objective Neonatal bronchiolitis is not well characterized. We studied the profile of acute bronchiolitis in term newborns during a respiratory syncytial virus (RSV) surge following relaxation in coronavirus disease 2019 (COVID-19) appropriate behavior.Methods This was a retrospective descriptive study performed in the neonatology division of a tertiary care pediatric hospital at Srinagar, Jammu and Kashmir, India. Term neonates (born at =37 completed gestational weeks) from 7 up to 28 days of life admitted with bronchiolitis over a 1-month period (November 2021) were included.Results Out of total 480 neonatal admissions over a month, 35 (7%) had acute bronchiolitis. Eight neonates were excluded. Out of 27 included neonates, 13 were males. Mean age at presentation was 20 days. All neonates were born at term (=37 completed gestational weeks). Cough (26), rapid breathing (20), and lower chest indrawing (20) were the predominant presenting features. Median SPO2 was 87% (interquartile range 85-92%). Fourteen (52%) neonates needed admission to neonatal intensive care unit. Respiratory support was needed in the form of oxygen through nasal prongs in 24 (89%) newborns. Heated humidified high-flow nasal cannula (HHHFNC) and bubble continuous positive airway pressure were used in five neonates each. Two neonates were mechanically ventilated. The mean duration of the hospital stay was 6.2 days. All neonates survived.Conclusion A series of 27 term neonates with bronchiolitis during an RSV surge is reported in the aftermath of lifting of COVID-19 restrictions. Many of these neonates were sick enough to require significant respiratory support. The outcome was good in all neonates.

Multi-branch sustainable convolutional neural network for disease classification

Pandemic and natural disasters are growing more often, imposing even more pressure on life care services and users. There are knowledge gaps regarding how to prevent disasters and pandemics. In recent years, after heart disease, corona virus disease-19 (COVID-19), brain stroke, and cancer are at their peak. Different machine learning and deep learning-based techniques are presented to detect these diseases. Existing technique uses two branches that have been used for detection and prediction of disease accurately such as brain hemorrhage. However, existing techniques have been focused on the detection of specific diseases with double-branches convolutional neural networks (CNNs). There is a need to develop a model to detect multiple diseases at the same time using computerized tomography (CT) scan images. We proposed a model that consists of 12 branches of CNN to detect the different types of diseases with their subtypes using CT scan images and classify them more accurately. We proposed multi-branch sustainable CNN model with deep learning architecture trained on the brain CT hemorrhage, COVID-19 lung CT scans and chest CT scans with subtypes of lung cancers. Feature extracted automatically from preprocessed input data and passed to classifiers for classification in the form of concatenated feature vectors. Six classifiers support vector machine (SVM), decision tree (DT), K-nearest neighbor (K-NN), artificial neural network (ANN), naïve Bayes (NB), linear regression (LR) classifiers, and three ensembles the random forest (RF), AdaBoost, gradient boosting ensembles were tested on our model for classification and prediction. Our model achieved the best results on RF on each dataset. Respectively, on brain CT hemorrhage achieved (99.79%) accuracy, on COVID-19 lung CT scans achieved (97.61%), and on chest CT scans dataset achieved (98.77%). © 2023 Wiley Periodicals LLC.

A Static Machine Learning Based Evaluation Method for Usability and Security Analysis in E-Commerce website

Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. An offline static model is trained. A static model is trained offline. In other words, we train the model once and then use it for a set period of time. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economic market’s "brick-and-mortar" model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyses usability assessments using statistical methods, as well as security assessments using online e-commerce security scanner tools, in order to investigate e-business standards that take into account the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B that represents the (3 X 1) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category. Author

Anxiety in Paediatric Patients: A Summary of Routinely Recorded Data at Gosh

Aims We performed an analysis of GOSH's Electronic Patient Record (EPR) data derived insights relating to patients diagnosed with an ICD-10 anxiety code. This analysis examined trends in frequency distribution and duration of anxiety diagnoses over time, alongside the medication administrations and procedures performed on these patients. Methods Routine data for all patients diagnosed with an ICD-10 anxiety code (F41-) from July 2019 to March 2022 were extracted, de-identified, and analysed in the secure GOSH Digital Research Environment (DRE). The Python package Pandas was used to clean and analyse data. Interactive visualisations were created using Plotly. Medication drug classes for these patients (Hypnotics, Anxiolytics, and Antidepressants), and OPCS-4-classification-identified-procedures were analysed. Results Across 1573 patients in the cohort, 'Anxiety disorder, unspecified' (F419) was the most common anxiety type until 2021, gradually being replaced by 'Other specified anxiety disorders' (F418). The monthly sum of anxiety diagnoses demonstrated a seasonal variation, peaking in July 2019 and July 2021, with a trough spanning UK COVID-19 lockdowns. Chronic Kidney Disease, Autism, and other developmental disorders were the most prevalent comorbidities. Stratifying by drug class, Hypnotics & Anxiolytic administrations were more popular than Antidepressants for patients diagnosed with anxiety. Melatonin was the most administered medication. The OPCS-4 Class 'U' ('Diagnostic imaging, testing and rehabilitation') was the most common group of procedures performed on the same day as an anxiety diagnosis. Transthoracic Echocardiography and CSF Injection were most prevalent. Conclusion This analysis of EPR data found a seasonal variation in anxiety diagnosis frequency, with a gradual change in the specific type. Hypnotics & Anxiolytics are more popular than Antidepressants. Anxiety diagnoses relate mostly to imaging and testing procedures. Further work includes the same analyses and trends on earlier data.

Using Text Mining and Graph Analytics to Optimally Identify Related Biomedical Research Publications

Background There are over 30,000 scientific journals with close to two million articles being published each year. We explore how text mining and graph analytics can be used to streamline the process of identifying relevant papers within a specific subject area making the process more objective and reducing bias. Methods A broad search criterion deployed in PubMed, IEEE and ACM search engines returns a set of titles and s. Text mining routines are used to split the into sentences and then Named Entity Recognition plus Linking to the Universal Medical Language System (UMLS) ontology (NER +L) applied to each sentence to identify clinical concepts. A graph was created for all concepts occurring in the same sentence and then the concepts were ranked using eigenvector centrality scores. The overall period covering all s was then split into several sub-periods and for each sub-period graphs created, and the concepts ranked. Results A search for epilepsy treatment in children returned 34k s over a period from 1950 to-date. The s were sub-divided into 12 sub-periods including 2020-21 and 2022. Having created graphs for all s and each subperiod, a common set of concepts across all periods was identified these were then ed from the sub-period ranked lists. The ranked concepts for 2020-21 identified 'COVID-19' and 'lockdown' as being newly used concepts. The rankings for 2022 identified new genes and medications which are being researched, as well as indicating which medications are falling in research interest. Conclusions This study demonstrates the feasibility of using text mining and graph analytics to objectively identify papers for manually review given a broad search criterion. This approach is both efficient and reduces biasand is applicable to any domain/clinical area without requiring an extensive domain knowledge.

Social-related factors in integrated UTAUT model for ZakaTech acceptance during the COVID-19 crisis

Purpose: Despite the significant growth in Islamic economies and the increasing number of Muslim youths inclining digital services, empirical-based research addressing the adoption of digital Islamic services is still scarce. Particularly, as a new term in the Islamic finance industry, ZakaTech has recently emerged as a modern term describing novel technologies adopted by zakat (compulsory levy on all believing and practicing high-net-worth Muslims) institutions;yet, it has largely been neglected in the literature. Therefore, this paper aims to propose an integrated model that scrutinizes the factors of unified model of acceptance and use of technology (UTAUT) of ZakaTech, combined with social cognitive theory (SCT), especially in a time of COVID-19 social distancing. Design/methodology/approach: The UTAUT–SCT model was validated via SmartPLS structural equation modeling by using a valid sample of 510 users (individual zakat payers) from Saudi Arabia. Findings: The results demonstrated the suitability of the integrated UTAUT–SCT model in predicting zakat payers' intention to use ZakaTech services. This proposed model has 70% explanatory power to explain variance in intention. All UTAUT constructs are statistically significant, except for effort expectancy. Social isolation caused by the pandemic and trust in e-zakat system exerted a significant influence on the inclination to uptake ZakaTech services. Originality/value: To the best of the authors' knowledge, this research is among the first research that studies Muslims' adoption of ZakaTech during COVID-19. Particularly, this study could add value to FinTech acceptance literature by empirically examining an integrated framework of UTAUT–SCT in a context as modern and unique as ZakaTech. © 2023, Emerald Publishing Limited.

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

Vaccine Hesitancy in COVID-19: A Behavioural Economics Approach—A Systematic Literature Review

The vaccination drive for the COVID-19 pandemic was initiated globally more than a year ago, with booster shots being the new addition currently. There are some setbacks regarding the acceptance of the vaccine that the government needs to tackle to achieve a fully vaccinated ecosystem. Vaccine hesitancy is not a new concept and has been witnessed by people for decades. In simple terms, vaccine hesitancy refers to a situation where people are reluctant to get vaccinated despite its availability. This is due to technological retrogression, superstitions, doubt towards the government and misinformation. This paper is a systematic literature review to analyse the behavioural economics theories shown by people towards vaccines in the SARS-CoV-2 pandemic. We aim to connect psychological and economic factors that lead to this hesitancy through behavioural economics. Availability bias, omission bias, confirmation bias, incentives, anticipated regret, illusory correlation, recency effect, tailoring and framing are the biases that influence decision-making under the behavioural economics framework. This paper is an attempt to analyse these principles and explain potential barriers to vaccine acceptance and intervention strategies for medical professionals and the state. © 2022 SAGE Publications India Pvt. Ltd.

Medications safety for special population case studies: Pediatrics

Medication errors are among the most common medical errors, and studies have shown that the pediatric population is particularly vulnerable. Errors can occur at any stage of the medication process. We tried to build various cases, which highlighted different aspects of drug safety in pediatrics. The case studies focused on vancomycin infusion, supportive treatment in COVID-19-related multisystem inflammatory illness, side effect of antitubercular treatment drugs, management of respiratory failure, low cardiac functioning, acyclovir nephrotoxicity, stress ulcer, cyclophosphamide-induced hemorrhagic cystitis in rhabdomyosarcoma, blood pressure after aortic coarctation elective surgery, and use of paracetamol instead of NSAIDs in pediatrics. These cases would be useful in both as a diagnostic tool and as a way of monitoring certain conditions. © 2023 Elsevier Inc. All rights reserved.

Pioneering wound treatment in a suffragette military hospital in World War 1

In the face of massive numbers of casualties returning to the UK in World War 1, health services were rapidly reorganized under the leadership of Sir Alfred Keogh. Hundreds of military hospitals were set up. Sir Alfred personally asked two women doctors, both militant suffragettes and members of the British Women's Social and Political Union, to set up and run a hospital in London. This remarkable hospital was to pioneer new antiseptic treatments for wounds. Endell Street Military Hospital was set up in 1915 by doctors Flora Murray and Louisa Garrett Anderson. The hospital was staffed and run solely by women, treating 26 000 patients in 520 beds over the course of the war. One of their most heroic contributions was to the care of wounds in injured soldiers returning from France. Throughout the war wound infections led to the deaths of thousands of soldiers and contributed to significant morbidity such as limb loss in countless others. In 1916 James Rutherford Morrison, Professor of Surgery in Durham, invented bismuth iodoform paste (BIPP) for the treatment of wound infections. The paste has significant antimicrobial properties. The Endell Street doctors contacted Morrison in June 1916 and started using his formulation on injured patients. By early 1917 they had treated > 400 patients with gunshot wounds, compound fractures, septic wounds, through-and-through wounds and foreign body wounds with BIPP, reporting their findings in The Lancet (Garrett Anderson L, Chambers H. The treatment of septic wounds with bismuth-iodoform-paraffin paste. Lancet 1917;189: 331-3). They reported no cases of tetanus or gas gangrene and were able to explain side-effects such as iodine and bismuth poisoning, why it occurred and how it could be avoided. BIPP has been in use constantly since 1916, and is still used today in ear, nose and throat departments, especially for packing nasal cavities. By changing from the traditional eusol (sodium hypochlorite solution) to BIPP Drs Murray and Anderson reduced dressing changes from daily to once every 7-14 days, saving staff time, costs and hugely improving outcomes. These women doctors saved hundreds of lives and pioneered wound treatments that are still used today. Both were awarded the CBE for their services, but sadly the hospital staff were sacked at the end of the war, when the hospital closed. This form of pioneering work, conducted under great strain with limited resources is still to be seen today in the COVID-19 pandemic.

Point prevalence audit surveys of respiratory tract infection consultations and antibiotic prescribing in primary care before and during the COVID-19 pandemic in Ireland.

BACKGROUND: Respiratory tract infections (RTIs) are the most common reason for prescribing antibiotics in general practice. The COVID-19 pandemic has impacted on antibiotic prescribing and delivery of primary care in Ireland. OBJECTIVES: To assess the quality of antibiotic prescribing, the impact of the COVID-19 pandemic and identify opportunities for antimicrobial stewardship (AMS) in Ireland. METHODS: Point prevalence audit surveys for RTI consultations were conducted as part of a European study at three time periods: January-February 2020, March-May 2020 and March-May 2021. Antibiotic prescribing was assessed and comparisons made between the three time periods. RESULTS: In total, 765 consultations were recorded, which were mainly face to face before the pandemic, but changed to predominantly remote consultations during the pandemic surveys in 2020 and 2021 (82% and 75%). Antibiotics were prescribed in 54% of RTI consultations before the pandemic. During pandemic surveys, this dropped to 23% in 2020 and 21% in 2021. There was a decrease in prescribing of Red (reserve) agents in 2021. Assessment against indication-specific quality indicators showed a high proportion of consultations for bronchitis and tonsillitis resulting in an antibiotic prescription (67% and 85%). Point-of-care testing (POCT) to aid diagnosis of RTIs were utilized in less than 1% of consultations. CONCLUSIONS: During the COVID-19 pandemic, there was a reduction in antibiotic prescribing. Opportunities identified to support AMS in primary care in Ireland are targeted initiatives to reduce antibiotic prescribing for bronchitis and tonsillitis and introducing POCT to support appropriate antibiotic prescribing.

Potential Cardiac Tamponade Development Secondary to SARS-CoV-2 Infection

Upon initial discovery in late 2019, severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, has managed to spread across the planet. A plethora of symptoms affecting multiple organ systems have been described, with the most common being nonspecific upper respiratory symptoms: cough, dyspnea, and wheezing. However, the cardiovascular system is also at risk following COVID-19 infection. Numerous cardiovascular complications have been reported by physicians globally, in particular cardiac tamponade Physicians must hold a high index of suspicion in identifying and treating patients with cardiac tamponade who may have contracted the novel coronavirus. This review will describe the current epidemiology and pathophysiology of SARS-CoV-2 and cardiac tamponade, highlighting their clinical course progression and the implications it may have for the severity of both illnesses. The paper will also review published case reports of cardiac tamponade, clinical presentation, and treatment of this complication, as well as the disease as a whole. © 2022 Elsevier Inc.

Synthesis and performance evaluation of copper and magnesium-based metal organic framework supported ionic liquid membrane for CO2/N2 separation

The CO2 emission is enhancing drastically because of the continuous emission from industries and transport sector. Although the CO2 emission had decreased in the first half of 2020 by 8.8% due to COVID-19 restrictions however, it is again on the rise and it might exceed the estimated level in 2030. The current methods used for CO2 separation have serious operational and environmental constraints. To overcome these problems we have devised a supported ionic liquid membrane (SILM) incorporated with the blend of bimetallic metal-organic framework (MOF) of copper and magnesium ions (CuxMgx) and Trihexyltetradecylphosphonium chloride [P66614] [Cl] ionic liquid (IL). CuxMgx MOF were synthesized and characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), and energy dispersive X-ray analysis (EDX). CuxMgx MOF with [P66614] [Cl] IL were immobilized on a flat sheet of polytetrafluoroethylene (PTFE) membrane. Single gas permeation tests of membranes loaded with 0.2/0.8 wt/wt% MOF/IL solution showed the highest CO2 permeability of 2937 Barrer and CO2/N2 selectivity of 33.26. The performance of SILM was also investigated with different water loadings of (30 wt % and 50 wt %) in addition to MOF/IL solution and at different feed pressure varying from 0.5 to 2 bars. Membranes showed enhancement in CO2 permeability to 3738 and 4628 Barrer whereas CO2/N2 selectivity decreased to 23.53 and 21.8 with membranes loaded with 30 and 50 wt % water, respectively, at a feed pressure of 2 bar. The gas permeation results show that the incorporation of CuxMgx MOF with IL in polymeric membrane enhances the CO2/N2 separation under humid conditions but slightly decreases CO2/N2 selectivity with an increase in feed pressure. The SILM synthesized in this research is highly viable for industrial flue gases because of the incorporation of phosphonium-based ILs that have high thermal stability. © 2022

Simulation in Urological Training: The Hybrid Model of Institute of Kidney Diseases Peshawar A Comparative Study

Background:"I fear the man who has practised one kick 10,000 times." Lee Bruce This aphorism highlights the growing importance of simulation in postgraduate urology training, especially during the COVID 19 pandemic, when all teaching and training activities were stopped, jeopardising postgraduate residents' education. Postgraduate residents must perform hours of surgical training to overcome urological learning curves. According to study, residents educated on simulators boost their summative scores. By introducing simulation to urology training in a way comparable to the well-known Halsted apprenticeship model, the current study emphasises the hybrid model of IKD. Objective(s): to compare the formative assessment results between residents taught on simulators and residents in the conventional apprenticeship model on factors of communication skills, technical competence, and overall capacity to conduct procedure on OSAT and DOPS. Material(s) and Method(s): from 2019 to 2021 this comparative study was conducted in the Department of Urology by Team C at the Institute of Kidney Diseases Peshawar. Group A (10 residents) and Group B (10 residents, 5 from the second and third years) received STEPS method OT instruction in the first phase. These simulators were used to impart knowledge to Group "B" Harvey for counseling and medical examinations Simulator for PCNL The second phase included a six-month training assignment swap between the two groups. A standard QSAT and DOPS proforma was used to evaluate each resident. Data analysis was done using SPSS 24.0. Result(s): Residents in Group A, who were originally exposed to the conventional technique, considerably outperformed Group B on Harvey (mean: 50.5;standard deviation: 2.21.1) in terms of communication skills, professionalism, and ethical concern during the first phase (p 0.001). However, the Group p0.05 shown considerably higher technical proficiency and overall process performance capacity. The mean technical skill and overall capacity to finish the process had a somewhat positive association in phase 1 in favour of group B (r=0.630, p 0.01). All QSAT and DOPS metrics significantly improved in the second phase. However, both groups did not vary significantly (p> 0.05). According to Pearson coefficient correlation, both groups considerably overcame their gaps in technical proficiency, communication skills, and procedural competence. (P= 0.001) Results are shown in Figures 1 through 06 and Tables 1 through 2. Conclusion(s): To improve the standard of urology residency in Pakistan, a hybrid paradigm that includes both simulation and actual performance is necessary. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

The Accuracy 0f CT Chest in Determining Between Ground Glass Opacities And Those Caused by Covid-19 And Other Medications

Introduction: Diagnostic ct scanning of the chest is currently being investigated for its ability to distinguish between ground glass opacities (GGO) caused by coronavirus 2019 (COVID-19) and GGO produced by other causes. Place and Duration: From January 2022 until June 2022, I will work as a Radiologist at Qazi Hussain Ahmad Hospital in Nowshera. Method(s): This study was cross sectional study carried out at the Qazi Hussain Ahmad Hospital, Nowshera for a period of six months. The overall sample size in the current study was100 non-contrast chest CTs. Eexperienced radiologists analyzed the CT images of the chest after redacting any personal information. laboratory results and the patient's medical history were noted. Result(s): The participants comprised 46 people with COVID19 and 100 without COVID19 who also had ground glass opacities on chest CT. There was no statistically significant difference in age between the groups (p-value = 0.212). Out of the non-COVID-19 GGO cases, three patients have hypersensitivity pneumonia in 3, eosinophilic pneumonia in 3, interstitial pneumonia in 7, pulmonary pneumonia in 3, pulmonary fibrosis in 7, drug-induced lung damage in 7, pulmonary alveolar hemorrhage in 3, and pulmonary emphysema in 11. Practical implication: This study will provide physician with the data to compare the likelihood that COVID-19 causes ground glass opacities on a chest CT scan versus the likelihood that they are caused by other probable causes Conclusion(s): Moreover, the specificity of chest CT in differentiating COVID-19 from viral pneumonia is only intermediate, and the specificity of chest CT in distinguishing COVID-19 from other reasons of ground glass opacities is poor. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Detection of Diseases in Pandemic: A Predictive Approach Using Stack Ensembling on Multi-Modal Imaging Data

Deep Learning (DL) in Medical Imaging is an emerging technology for diagnosing various diseases, i.e., pneumonia, lung cancer, brain stroke, breast cancer, etc. In Machine Learning (ML) and traditional data mining approaches, feature extraction is performed before building a predictive model, which is a cumbersome task. In the case of complex data, there are a lot of challenges, such as insufficient domain knowledge while performing feature engineering. With the advancement in the application of Artificial Neural Networks (ANNs) and DL, ensemble learning is an essential foundation for developing an automated diagnostic system. Medical Imaging with different modalities is effective for the detailed analysis of various chronic diseases, in which the healthy and infected scans of multiple organs are compared and analyzed. In this study, the transfer learning approach is applied to train 15 state-of-the-art DL models on three datasets (X-ray, CT-scan and Ultrasound) for predicting diseases. The performance of these models is evaluated and compared. Furthermore, a two-level stack ensembling of fine-tuned DL models is proposed. The DL models having the best performances among the 15 will be used for stacking in the first layer. Support Vector Machine (SVM) is used in Level 2 as a meta-classifier to predict the result as one of the following: pandemic positive (1) or negative (0). The proposed architecture has achieved 98.3%, 98.2% and 99% accuracy for D1, D2 and D3, respectively, which outperforms the performance of existing research. These experimental results and findings can be considered helpful tools for pandemic screening on chest X-rays, CT scan images and ultrasound images of infected patients. This architecture aims to provide clinicians with more accurate results.