Search

Background: Discharge Against Medical Advice (DAMA) is an obnoxious reality and dissatisfying event to both ends - patient and healthcare provider. It’s pertinent that periodical survey at single or multicentre be done to identify the predictors and seek solutions and suggest recommendations. Aim and Objectives: Aim of this study was to analyze the predictors of DAMA cases. Objectives were to find out prevalence and predictors of DAMA cases. Methodology: A prospective cross sectional analytical study was done at a tertiary level multi super speciality hospital of central Gujarat. Participants were all those cases who seek DAMA for one other attribute and consented to participate in the study by non-randomised method. After consent, details from DAMA cases were collected in pre-validated questionnaire forms from patients and relatives. All data was analysed statistically for descriptive and analytical statistics. Observations: Maximum DAMA cases were of age group 20-59 years (55%). followed by 60-100 years (25%). Most common reason for obtaining DAMA (56%) was financial constraints. Most of DAMA patients who were having financial constraints (70 & 46) were admitted to Medical Intensive Care Unit (33.11%) and Male Medicine ward (21.22%) respectively. Conclusion: DAMA cases are damaging not only patient morbidity and mortality wise, but also burdens healthcare staff and resources negatively. The reasons for DAMA cases should be discussed and analysed at every healthcare facilities regularly to prevent this futile dissatisfying event.

Successful osseointegration is observed predictably for submerged implants requiring a two-stage procedure as well as for non-submerged implants characterized by one-stage surgical procedure. This study was aimed at evaluating and comparing crestal bone alterations around submerged and non-submerged implant radiographically. Total 45 patients aged between 20 to 50 years with missing mandibular posterior teeth were divided into 3 groups [Submerged implants (n=15), Non-submerged implants with anatomic healing abutment (n=15) and Non-submerged implants with esthetic healing abutment (n=15)]. Radiographic evaluation of mesial and distal marginal bone loss was done at 1 month and 3 months. Statistically significant differences were found between submerged dental implants and non-submerged dental implants with anatomical type of healing abutment designs (P < 0.001) and between the two non-submerged dental implant groups with different types of healing abutments (P < 0.001) at 1 month and 3 months. But there was no statistically significant difference between submerged dental implants and non-submerged dental implants with esthetic type of healing abutments (P > 0.05) at 1 month and 3 months. It was concluded from this study that bone resorption during the osseointegration period using the non-submerged technique varied significantly depending on the morphology of the healing abutment used. The non-submerged technique with an esthetic healing abutment produced an equally predictable outcome compared with the submerged technique. &nbsp

In recent years, ever-increasing scientific knowledge and modern high-tech advancements in micro- and nano-scales fabrication technologies have impacted significantly on various scientific fields. A micro-level approach so-called “microfluidic technology” has rapidly evolved as a powerful tool for numerous applications with special reference to bioengineering and biomedical engineering research. Therefore, a transformative effect has been felt, for instance, in biological sample handling, analyte sensing cell-based assay, tissue engineering, molecular diagnostics, and drug screening, etc. Besides such huge multi-functional potentialities, microfluidic technology also offers the opportunity to mimic different organs to address the complexity of animal-based testing models effectively. The combination of fluid physics along with three-dimensional (3-D) cell compartmentalization has sustained popularity as organ-on-a-chip. In this context, simple humanoid model systems which are important for a wide range of research fields rely on the development of a microfluidic system. Development of microfluidic-based technology bridges the gap between in vitro and in vivo models offering new approaches to research in medicine, biology, and pharmacology, among others.

Background: Globally, among the leading preventable causes of premature deaths tobacco stands on the top. The consumption patterns of tobacco in various forms of chewing and smoking vary across different regions and socioeconomic levels. Objectives: To study tobacco consumption pattern in two districts of Gujarat and compare among them. Methodology: A cross sectional study was carried out on 504 participants during January 2015 to September 2016 among 15-64 years age group. A pre designed and pre tested Questionnaire was used to collect data on tobacco consumption pattern. Descriptive and analytical statistical methods were used for the data analysis. Results: Smoking was reported among 11.51% and 18.25% in Gandhinagar and Mehsana district respectively. Out of which around 90% of them in both district were smoking daily. 34.52% of the studied population in Mehsana district was using smokeless tobacco as compared to Gandhinagar district (26.19%). Initiation of smoking was in later age as compared to smokeless tobacco. Conclusion: Present study concludes that large number of people including younger population was using smokeless tobacco in both districts. Early initiation of use of smokeless tobacco suggests an urgent need for action.

Introduction: Postnatal period brings many physiological changes in mothers. This period is crucial for the mother as there is a new addition to her life. Hence the health of the postnatal mother is associated with the type of delivery. Objective: This study aims to assess and compare the problems faced during postnatal period among mothers with normal vaginal delivery and cesarean delivery. Methods: Quantitative research approach & a comparative descriptive research design was used with 60 postnatal mothers who had fulfilled the inclusion criteria, among them 30 mothers with normal vaginal delivery and 30 mothers with cesarean delivery were selected using purposive sampling technique. Data was collected using modified standardized observation checklist to assess the problems faced during postnatal period among mothers with normal vaginal delivery and cesarean delivery. Setting: The postnatal ward of selected private maternity care hospital-N.D. Desai medical college and hospital of Nadiad city, Gujarat. Results: There was statistically significant difference found in problems faced during postnatal period in group 1 and group 2 which shows that mean difference between group 1 and group 2 was 1.79 and standard deviation 0.15. In group 1 overall percentage of severity of pain according to mild, moderate, severe and no pain criteria were 57%, 20%, 0% and 23% respectively and in group 2 overall percentage of severity of pain according to mild, moderate, severe and no pain criteria were 0%, 70%, 30% and 13% respectively Conclusion: The study concluded that postnatal mothers with cesarean delivery have to face more problems than those following normal vaginal delivery. The level of pain score was also high in mothers with cesarean delivery.

Participation of humans in clinical research is always remained questionable. Hence evaluation of such doubt helps to conclude the perception about such participation. This research presents the process for development and validation of questionnaire for Healthy Adult Human Participants of Early Phase Bioequivalence Pharmacokinetic Endpoint Study. For development of questionnaire, literature search, experts’ discussion and authors’ experience was used for domain identification and its segregation for different variables. For validity of questionnaire, face validity and content validity was performed. Modification was done based on response from experts during non-quantitative face validity. % of overall agreement was 94.55 for question asked in face validity. While, Content Validity Ratio and Content Validity Index was calculated using the process mentioned by Lawshe and Lynn respectively. Initially 83 items were identified but based on validation 84 items were finalized after removal of three and addition of four questions. Deleted three items had Content Validity Ratio of 0.00, 0.67 and 0.67 and which were below accepted level of 0.99. While, I-CVI was observed from range of 0.83 to 1.00 and S-CVI values were above acceptable level of 0.90 for S-CVI (S-CVI/ Ave) and 0.80 for S-CVI (S-CVI/UA) for whole questionnaire and each part.

This paper presents the results of Demographic and Participation Details of Healthy Adult Human Participants of Early Phase Bioequivalence Pharmacokinetic Endpoint Study. For that data of 50 participants was collected using self-administered questionnaire. After ethics approval, data were collected between between Jul-21 and Aug-21 from Gujarat state of India. Results of demographic and participation details are tabulated by its frequency and percentage. Participants are participating more whose age range were 18-41 years, income less than one lakh, education below Higher Secondary and having private job or wage-earner. Age of first time participation was found in range of 18-41 years and frequency of number studies in which participant participated were found from 01 to 20 studies. Also Chi-Square results suggested there is significant (p < 0.05) relation (I) between the Education and Age of first time participation (II) between the occupation and number of times participated.

Nowadays health agencies of regulated markets are becoming stringent regarding the bio-studies. So clinical/contract research organization (CRO) need to be selected carefully after their detailed assessment. As per requirement of bio-study CRO should be assessed with the study specific checklist of questions before awarding the bio-study. Questions related to various services of study like: Clinic phase, analytical phase, Pharmacokinetic and statistical phase, ethics approval, QA/QC, record handing etc. and related to CRO capabilities are discussed here with their relevance to conclude the abilities of CRO for successful execution of bio-study. Hence, this paper focuses all possible questions which need to be assessed before selection of CRO mainly for Bioavailability/Bioequivalence (BABE) study in healthy adult volunteers. Properly selected CRO will help for smooth execution of study and quality report and subsequently, hassle-free approval of dossier submitted to regulatory agency.

BACKGROUND More people are turning to internet pharmacies to purchase their prescription medicines. This kind of purchase is associated with serious risks, including the risk of buying fake medicines, which are widely available on the internet. This underresearched issue has been highlighted by many newspaper articles in the past few years. Newspapers can play an important role in shaping public perceptions of the risks associated with purchasing prescription medicines on the internet. Thus, it is important to understand how the news media present this issue. OBJECTIVE This study aimed to explore newspaper coverage of the problem of purchasing fake prescription medicines on the internet. METHODS Newspaper articles were retrieved from the ProQuest electronic database using search terms related to the topic of buying fake prescription medicines on the internet. The search was limited to articles published between April 2019 and March 2022 to retrieve relevant articles in this fast-developing field. Articles were included if they were published in English and focused on prescription medicines. Thematic analysis was employed to analyze the articles, and the Theory of Planned Behavior framework was used as a conceptual lens to develop the coding of themes. RESULTS A total of 106 articles were included and analyzed using thematic analysis. We identified 4 superordinate themes that represent newspaper coverage of the topic of buying prescription medicines on the internet. These themes are (1) the risks of purchasing medicines on the internet (eg, health risks and product quality concerns, financial risks, lack of accountability, risk of purchasing stolen medicines), (2) benefits that entice consumers to make the purchase (eg, convenience and quick purchase, lower cost, privacy of the purchase), (3) social influencing factors of the purchase (influencers, health care providers), and (4) facilitators of the purchase (eg, medicines shortages, pandemic disease such as COVID-19, social media, search engines, accessibility, low risk perception). CONCLUSIONS This theory-based study explored the news media coverage of the problem of fake prescription medicines being purchased on the internet by highlighting the complexity of personal beliefs and the range of external circumstances that could influence people to make these purchases. Further research is needed in this area to identify the factors that lead people to buy prescription medicines on the internet. Identifying these factors could enable the development of interventions to dissuade people from purchasing medicines from unsafe sources on the internet, thus protecting consumers from unsafe or illegal medicines.

Background The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves. Methods In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression. Findings Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240·4 cases per 100 000 people vs 136·0 cases per 100 000 people; admissions, 27·9 admissions per 100 000 people vs 16·1 admissions per 100 000 people; deaths, 8·3 deaths per 100 000 people vs 3·6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1·19, 95% CI 1·18–1·20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40–64 years (adjusted odds ratio [aOR] 1·22, 95% CI 1·14–1·31), and older than 65 years (aOR 1·38, 1·25–1·52), compared with younger than 40 years; of Mixed race (aOR 1·21, 1·06–1·38) compared with White race; and admitted in the public sector (aOR 1·65, 1·41–1·92); and less likely to be Black (aOR 0·53, 0·47–0·60) and Indian (aOR 0·77, 0·66–0·91), compared with White; and have a comorbid condition (aOR 0·60, 0·55–0·67). For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1·31, 95% CI 1·28–1·35). In-hospital case-fatality risk increased from 17·7% in weeks of low admission (8000 admissions; aOR 1·24, 1·17–1·32). Interpretation In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage. Funding DATCOV as a national surveillance system is funded by the National Institute for Communicable Diseases and the South African National Government.

Summary Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15–49 years and a tuberculosis prevalence of 0·7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. Methods In this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. Findings Among the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23·3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37·4%) of 163 350, diabetes in 43 885 (27·4%) of 159 932, and HIV in 13 793 (9·1%) of 151 779. Tuberculosis was reported in 5282 (3·6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1·34, 95% CI 1·27–1·43), past tuberculosis (1·26, 1·15–1·38), current tuberculosis (1·42, 1·22–1·64), and both past and current tuberculosis (1·48, 1·32–1·67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1·45, 95% CI 1·22–1·72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29·2% compared with 30·8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with increased COVID-19 in-hospital mortality risk in both people with HIV and HIV-uninfected individuals. Interpretation Individuals identified as being at high risk of COVID-19 in-hospital mortality (older individuals and those with chronic comorbidities and people with HIV, particularly those not on ART) would benefit from COVID-19 prevention programmes such as vaccine prioritisation as well as early referral and treatment. Funding South African National Government.

BACKGROUND Many people in the United Kingdom are turning to the internet to obtain prescription-only medicines (POMs). This introduces substantial concerns for patient safety, particularly owing to the risk of buying fake medicines. To help reduce the risks to patient safety, it is important to understand why people buy POMs on the web in the first place. OBJECTIVE This study aimed to identify why people in the United Kingdom purchase medicines, specifically POMs, from the internet, and their perceptions of risks posed by the availability of fake medicines on the web. METHODS Semistructured interviews were conducted with adults from the United Kingdom who had previously purchased medicines on the web. Purposive sampling was adopted using various methods to achieve diversity in participants’ experiences and demographics. The recruitment was continued until data saturation was reached. Thematic analysis was employed, with the theory of planned behavior acting as a framework to develop the coding of themes. RESULTS A total of 20 participants were interviewed. Participants had bought various types of POMs or medicines with the potential to be misused or that required a higher level of medical oversight (eg, antibiotics and controlled medicines). Participants demonstrated awareness of the presence and the risks of fake medicines available on the internet. The factors that influence participants’ decision to buy medicines on the web were grouped into themes, including the advantages (avoiding long waiting times, bypassing gatekeepers, availability of medicines, lower costs, convenient process, and privacy), disadvantages (medicine safety concerns, medicine quality concerns, higher costs, web-based payment risks, lack of accountability, and engaging in an illegal behavior) of purchasing medicines on the web, social influencing factors (interactions with health care providers, other consumers’ reviews and experiences, word of mouth by friends, and influencers’ endorsement), barriers (general barriers and website-specific barriers) and facilitators (facilitators offered by the illegal sellers of medicines, facilitators offered by internet platforms, COVID-19 outbreak as a facilitating condition, and participants’ personality) of the purchase, and factors that lead people to trust the web-based sellers of medicines (website features, product appearance, and past experience). CONCLUSIONS In-depth insights into what drives people in the United Kingdom to buy medicines on the web could enable the development of effective and evidence-based public awareness campaigns that warn consumers about the risks of buying fake medicines from the internet. The findings enable researchers to design interventions to minimize the purchasing of POMs on the web. A limitation of this study is that although the interviews were in-depth and data saturation was reached, the findings may not be generalizable, as this was a qualitative study. However, the theory of planned behavior, which informed the analysis, has well-established guidelines for developing a questionnaire for a future quantitative study.

Introduction During the COVID-19 pandemic, most educational institutions have opted for online education rather than traditional modes of education to protect their employees and students. Online education has been gaining momentum in almost all countries around the world. This coincides with the recently introduced competency-based medical education in India which has embraced online education. This poses a new challenge for the institutions involved, the instructors or teachers, and the students since they must adapt quickly to the new mode of learning. Online education requires teachers to improve their competency in three major areas; pedagogy, technology, and content knowledge. Some of the challenges include; lack of technological skill, poor time management and lack of infrastructure. As technology rapidly advances, health care education systems must also advance in tandem. To implement the new competency-based system and online education, the institutions and the individuals must realize the importance of online education, identify the barriers and quickly work on solutions for success. Methods This review was conducted based on various research papers on the topic of online medical education, the challenges faced by faculty members, and the opinion of students on this dilemma. Search terms included online medical education, COVID19, competency-based medical education. Conclusion This review identified various challenges posed by online education on the current medical curriculum, faced by both faculty members and students, especially under the light of the Competency-Based Undergraduate Curriculum for Indian Graduates. Different solutions were proposed to overcome these challenges.

Working in Africa provides neuroscientists with opportunities that are not available in other continents. Populations in this region exhibit the greatest genetic diversity; they live in ecosystems with diverse flora and fauna; and they face unique stresses to brain health, including child brain health and development, due to high levels of traumatic brain injury and diseases endemic to the region. However, the neuroscience community in Africa has yet to reach its full potential. In this article we report the outcomes from a series of meetings at which the African neuroscience community came together to identify barriers and opportunities, and to discuss ways forward. This exercise resulted in the identification of six domains of distinction in African neuroscience: the diverse DNA of African populations; diverse flora, fauna and ecosystems for comparative research; child brain health and development; the impact of climate change on mental and neurological health; access to clinical populations with important conditions less prevalent in the global North; and resourcefulness in the reuse and adaption of existing technologies and resources to answer new questions. The article also outlines plans to advance the field of neuroscience in Africa in order to unlock the potential of African neuroscientists to address regional and global mental health and neurological problems.

Purpose A literature review and meta-synthesis of qualitative research had enabled us to develop a grounded theory explaining the difficulties breast cancer survivors face with the initial decision to accept long-term endocrine therapy, and the everyday challenges of continuing or deciding to stop treatment early. Our objective was to interview a cohort of women in a UK setting to corroborate and complete the grounded theory with the end users’ primary involvement. Methods A semi-structured interview schedule was written based on the existing grounded theory. Fourteen women with a history of hormone-positive breast cancer were recruited and interviewed. The audio-recorded interviews were transcribed and analysed against the existing grounded theory. Results The findings were compatible with the core theory ‘Hobson’s choice or a horned dilemma’ and its constituent categories previously developed, with additional concepts identified and added to our paradigm models. Importantly, we found that some women who started with a strong sense of commitment to their treatment changed their mind as they experienced the medication side effects over time, impacting on their persistence with long-term endocrine therapy. Conclusion The findings indicate an opportunity for health providers to intervene and influence women’s waning perceptions of the necessity of their treatment, for example upon experiencing the side effects. Interventions could involve the provision of side effect management strategies via accessible resources.

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate the facts to shift the HM&D technology from costly sensor to economic microphone based mechanism. The adopted strategy is capable to reduce the bulky data transmission on the internet, and to increase the up-time of AgVs. We experimented on the essential red hot chili peppers system of the AgV’s backbone hydraulic system—the hydraulic filter and pump. The measurement system analysis is adopted to determine the preciseness of data captured near the considered components. The envision of the correlation between the collected data extracts significant information to draw the facts to embrace the future HM&D technology shift. Correlation between the signals captured from costly sensors and Microphone for the generated faults in hydraulic components demonstrates the effectiveness of audio to replace existing HM&D technology.

In the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users’ need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.

By the increasing growth of the Internet of Things (IoT) which provides interconnection and communications between electronic devices and corresponding sensors, a large volume of data is exchanged by multi-input multi-output (MIMO) telecommunication systems. In the case of IoT, reducing the data volume by removing the data redundancy results in more green communication by less power consumption for data transmission and less required storage memory. An approach to avoid the pilot data thus having less redundant data is using blind techniques. This research work presents an improvement to Stone’s blind source separation (BSS) precision, robustness, and computation load and its application to blind MIMO IoT interference channel estimation, multi-nodes IoT data detection, separation and identification in a MIMO-OFDM IoT network. Stone’s BSS is based on complexity conjecture indicating the independent sources have higher predictability than the mixtures. The presented improvement to Stone’s BSS is by a probabilistic modification to the short-term predictability merit by acquiring the prediction coefficients proportional to probability weights which follow a super Gaussian distribution assumption for sources. The probabilistic modification to Stone’s BSS (P-Stone) makes it maximally compatible with a pre-specified probability distribution model, and thereof the signal recovery is not only due to predictability maximization, but it is also inherent to increasing non-gaussianity which results in more independent recoveries, and less dependent on serial dependency of sources. Despite the Stone’s BSS, the proposed merit function does not need any long-term predictor; thus, it achieves around fifty times lower complexity load using just short-term predictors. The superiority of P-Stone to Stone BSS, AMUSE, and SOBI as well-known second-order techniques has been statistically evaluated and clarified through the experiments over MIMO-IoT networks of different combinations. As well, the comparative analysis over multimedia mixtures of music, speech, and images demonstrates its efficiency dominance.

Co-administration of multiple intravenous (IV) medicines down the same lumen of an IV catheter is often necessary in the intensive care unit (ICU) while ensuring medicine compatibility. This study explores ICU nurses' views on the everyday practice surrounding co-administration of multiple IV medicines down the same lumen. Qualitative study using focus group interviews. Three focus groups were conducted with 20 ICU nurses across two hospitals in the Thames Valley Critical Care Network, England. Participants' experience of co-administration down the same lumen and means of assessing compatibility were explored. All focus groups were recorded, transcribed verbatim, and analysed using thematic analysis. Functional Resonance Analysis Method was used to provide a visual representation of the co-administration process. Two key themes were identified as essential during the process of co-administration, namely, venous access and resources. Most nurses described insufficient venous access and lack of compatibility data for commonly used medicines (eg, analgesics and antibiotics) as particular challenges. Strategies such as obtaining additional venous access, prioritizing infusions, and swapping line of infusion were used to manage IV administration problems where medicines were incompatible, or of unknown or variable compatibility. Nurses use several workarounds to manage commonly encountered medication compatibility problems that may lead to delays in therapy. Organizations should review and tailor compatibility resources towards commonly administered medicines using an interdisciplinary approach. Developing a clinical decision-making pathway to minimise variability while promoting safe co-administration practice should be prioritised. This study highlights several ways ICU nurses are able to manage challenges associated with co-administration and the need for the development of a more robust and comprehensive compatibility resource that is relevant to everyday practice through collaboration between nurses and pharmacists.

Othman AlOmeir, Nilesh Patel, Parastou Donyai Department of Pharmacy, University of Reading, Reading, Berkshire, UKCorrespondence: Othman AlOmeir; Parastou Donyai, Email aalomear@su.edu.sa; p.donyai@reading.ac.ukObjective: Non-adherence to adjuvant hormone therapy prescribed orally in the treatment of breast cancer is complex as the literature has shown. Many women find it hard to adhere to the hormonal medicines they are prescribed and expected to take for at least 5 years following the initial management of their breast cancer. Arguably, communicating other women’s ‘trials, tribulations, and triumphs’ with medication-taking could help newly-diagnosed patients to better prepare for the journey ahead. Our objective was to visually represent women’s experiences with these medicines using data synthesized in the literature.Methods: Three schematics were drawn for each phase of medication-taking, namely, starting out, adherence, and cessation. The schematics were validated by interviewing a panel of healthcare professionals (n=10) and calculating a Content Validity Index (CVI). The edited drawings were discussed with a separate panel of breast cancer survivors (n=14) whose responses were elicited qualitatively in one-to-one interviews.Results: A total of 76 individual pictograms were drawn across the three schematics. The 13 pictograms that had an item-level CVI< 0.8 were modified according to feedback resulting in three final schematics with an overall CVI of 87%, 87% and 80%, respectively.Conclusion: Synthesised summaries of women’s experiences with oral hormone therapy for breast cancer were visualised via three validated schematics. The schematics could aid patient-professional communication to help anticipate and tackle negative experiences and support decisions to take hormone medication in breast cancer.Keywords: adherence, medication, breast cancer, hormone therapy, qualitative research, pictogram

Background More people are turning to internet pharmacies to purchase their prescription medicines. This kind of purchase is associated with serious risks, including the risk of buying fake medicines, which are widely available on the internet. This underresearched issue has been highlighted by many newspaper articles in the past few years. Newspapers can play an important role in shaping public perceptions of the risks associated with purchasing prescription medicines on the internet. Thus, it is important to understand how the news media present this issue. Objective This study aimed to explore newspaper coverage of the problem of purchasing fake prescription medicines on the internet. Methods Newspaper articles were retrieved from the ProQuest electronic database using search terms related to the topic of buying fake prescription medicines on the internet. The search was limited to articles published between April 2019 and March 2022 to retrieve relevant articles in this fast-developing field. Articles were included if they were published in English and focused on prescription medicines. Thematic analysis was employed to analyze the articles, and the Theory of Planned Behavior framework was used as a conceptual lens to develop the coding of themes. Results A total of 106 articles were included and analyzed using thematic analysis. We identified 4 superordinate themes that represent newspaper coverage of the topic of buying prescription medicines on the internet. These themes are (1) the risks of purchasing medicines on the internet (eg, health risks and product quality concerns, financial risks, lack of accountability, risk of purchasing stolen medicines), (2) benefits that entice consumers to make the purchase (eg, convenience and quick purchase, lower cost, privacy of the purchase), (3) social influencing factors of the purchase (influencers, health care providers), and (4) facilitators of the purchase (eg, medicines shortages, pandemic disease such as COVID-19, social media, search engines, accessibility, low risk perception). Conclusions This theory-based study explored the news media coverage of the problem of fake prescription medicines being purchased on the internet by highlighting the complexity of personal beliefs and the range of external circumstances that could influence people to make these purchases. Further research is needed in this area to identify the factors that lead people to buy prescription medicines on the internet. Identifying these factors could enable the development of interventions to dissuade people from purchasing medicines from unsafe sources on the internet, thus protecting consumers from unsafe or illegal medicines.

Background Many people in the United Kingdom are turning to the internet to obtain prescription-only medicines (POMs). This introduces substantial concerns for patient safety, particularly owing to the risk of buying fake medicines. To help reduce the risks to patient safety, it is important to understand why people buy POMs on the web in the first place. Objective This study aimed to identify why people in the United Kingdom purchase medicines, specifically POMs, from the internet, and their perceptions of risks posed by the availability of fake medicines on the web. Methods Semistructured interviews were conducted with adults from the United Kingdom who had previously purchased medicines on the web. Purposive sampling was adopted using various methods to achieve diversity in participants’ experiences and demographics. The recruitment was continued until data saturation was reached. Thematic analysis was employed, with the theory of planned behavior acting as a framework to develop the coding of themes. Results A total of 20 participants were interviewed. Participants had bought various types of POMs or medicines with the potential to be misused or that required a higher level of medical oversight (eg, antibiotics and controlled medicines). Participants demonstrated awareness of the presence and the risks of fake medicines available on the internet. The factors that influence participants’ decision to buy medicines on the web were grouped into themes, including the advantages (avoiding long waiting times, bypassing gatekeepers, availability of medicines, lower costs, convenient process, and privacy), disadvantages (medicine safety concerns, medicine quality concerns, higher costs, web-based payment risks, lack of accountability, and engaging in an illegal behavior) of purchasing medicines on the web, social influencing factors (interactions with health care providers, other consumers’ reviews and experiences, word of mouth by friends, and influencers’ endorsement), barriers (general barriers and website-specific barriers) and facilitators (facilitators offered by the illegal sellers of medicines, facilitators offered by internet platforms, COVID-19 outbreak as a facilitating condition, and participants’ personality) of the purchase, and factors that lead people to trust the web-based sellers of medicines (website features, product appearance, and past experience). Conclusions In-depth insights into what drives people in the United Kingdom to buy medicines on the web could enable the development of effective and evidence-based public awareness campaigns that warn consumers about the risks of buying fake medicines from the internet. The findings enable researchers to design interventions to minimize the purchasing of POMs on the web. A limitation of this study is that although the interviews were in-depth and data saturation was reached, the findings may not be generalizable, as this was a qualitative study. However, the theory of planned behavior, which informed the analysis, has well-established guidelines for developing a questionnaire for a future quantitative study.

Background: Obesity is a positive risk factor in development of hypertension, diabetes, gallbladder diseases, coronary heart diseases, and few types of cancers. Body mass index (BMI) is an inexpensive and easy screening method for weight category: Underweight, healthy weight, overweight, and obesity. Only a few studies have been conducted until now which focuses on finding any relationship between BMI and blood group of school going children. Aims and Objectives: The aims of this were as follows: (a) To find out BMI in school children of study center and (b) to find out correlation of BMI with blood groups and other variables of participants. Materials and Methods: It was a cross-sectional study conducted among 101 children of school going age group 10–15 years at one of the private schools of Ahmedabad, Gujarat. A questionnaire was prepared which included sections of demographic details, blood group, diet history, and family history. Results: About 50% of participants were from of either age 13 or 14. There was almost same proportion of participants of both sexes. About 70% of participants were found to be underweight, while about 15% were overweight. Overweight category of BMI is most commonly seen in children having blood group “A” while no child of “O” blood group was found to be overweight. This association was statistically significant. Conclusion: Almost 70% of participants were underweight, while about 15% were overweight. Overweight was most commonly seen in children having blood group “A.” No child of “O” blood group was found to have overweight.

This chapter illustrates the role of evolutionary optimization in designing AI end-devices to monitor the efficiency of agriculture vehicles (AgVs) mainly on the field via economic sound-based IoT sensors. Due to the application of AI on end-devices, there is a certain limitation for memory and complexity of the deployed algorithms. In such a condition, a machine learning model with optimal structure is of favorite. Lightweight is an aspect of the model as its model structure is optimized for operation of minimum complexity but an acceptable efficiency. The chapter explains that how this target can be achieved by the deployment of metaheuristic evolutionary optimizers. The AI model with optimum complexity and structure is suitable especially for deployment on smartphones. The optimization assists the designer in achieving not only the lightweight structure but also a maximized efficiency for recognition via built-in economic sensors such as smartphone microphones.

Currently, the development of smart systems for recognition and sensing of volatile organic compounds (VOCs) in medical, agricultural, biometric, security and safety, applications is an emerging research area. This review presents an introduction to the field of VOC recognition by analytical methods and sensing by chemical sensors. The role of pattern recognition methods in the analysis of sensor array response is briefly discussed. Besides, the electronic nose (E-Nose) system (a bio-inspired prototype of the natural olfaction system by combining a chemical sensor array and pattern recognition methods) and its significance for VOCs recognition and sensing in different applications is explained. The study concludes current constraint and future prospects of VOC recognition and sensing in real-time applications.

The wind power generation is a rapidly growing grid integrated renewable energy (RE) technology with an installed capacity of 539.291 GW. The capability of the wind energy conversion system (WECS) to remain integrated into the utility network in the case of low voltage events is called low-voltage ride-through (LVRT) capability. This paper offers a comprehensive overview of improvement techniques of the LVRT capability in WECS to increase the wind energy penetration level in the utility grid. Exhibited portrait manifests a broad spectrum of 1) wind turbines, 2) electrical generators used for wind power applications, 3) international grid codes applicable for grid integration of WECS, 4) LVRT fundamentals in WECS, 5) wind turbines LVRT methods by doubly fed induction generator (DFIG), 6) wind turbines LVRT methods by permanent magnet synchronous generators (PMSG), and 7) LVRT methods of wind turbines using squirrel cage induction generator (SCIG). This ready-reckoner paper critically reviews and classifies more than 190 research papers on LVRT issues, practices, and available technologies for grid integration in wind energy systems, and it aims to be a quick reference for the researchers, designers, manufacturers, and engineers working in the same field.

Background:\ud Uptake of non-medical prescribing by pharmacists working in primary care has been slow. This is despite benefits such as quicker and more efficient access to medicines for patients, a reduction in doctor workload and enhanced professional satisfaction. This systematic review explores the views, opinions and attitudes of pharmacists and graduates towards non-medical prescribing.\ud \ud Methods:\ud Medline, ScienceDirect, Embase and the University of Reading Summon Service were searched to identify qualitative and mixed methods papers that examined the views, opinions and attitudes of pharmacists and graduates towards non-medical prescribing. Papers published between January 2003 and September 2017 were included. Studies were quality assessed using the CASP checklist and then analysed using thematic synthesis.\ud \ud Results:\ud After 85 full text articles were assessed, a final 14 studies were eligible for inclusion. The included studies assessed pharmacists currently prescribing and other pharmacists and graduates with familiarity of non-medical prescribing. Thematic synthesis identified two themes: (1) practice environment, and (2) pharmacist’s role. Non-medical prescribing was considered a natural extension to the role of a pharmacist despite difficulties in completing the required training. The ability to then prescribe was dependent on funding and access to medical records, time and support staff. Pharmacists experienced professional rivalry with both support and resistance from members of the primary care team. The provision of training was frequently referred to as unsatisfactory. Pharmacists were motivated to prescribe, deriving increased job satisfaction and sense of professionalism, however, they often felt under prepared for the reality of unsupervised practice. Furthermore, pharmacists reported a cautious approach with a fear of making errors frequently discussed. \ud \ud Conclusions:\ud This review has identified themes and subsequent barriers and facilitators to non-medical prescribing. Many of the barriers are more perceived than real and are diminishing. Consideration of these will assist and advance pharmacist prescribing in primary care, leading to positive outcomes for both patient care and the pharmacy profession.

This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.

Background: The adverse health consequences of overweight and obesity in India leads to higher prevalenceof diabetes mellitus and cardiovascular diseases. Also the compliance of people for routine form of exercisefor BP control has not been very encouraging due to time, place etc constrains.Aim: Therefore we conducted a nonrandomised clinical study to determine the short-term effects ofisotonic handgrip exercise by using smiley balls on blood pressure in healthy normal weight and overweightadolescents with the objective to find a user friendly exercise which help in reducing blood pressure.Method: A non randomized clinical study was conducted on 100 young normal-weight and pre-obese adults(50 Boys and 50 Girls) in the age group of 18–25 years. Isotonic handgrip exercise was performed at therate 20 contractions/minute (2 sec contraction/1 sec relaxation) at maximal intensity for 10 minutes usingsmiley ball. Pulse rate and blood pressure parameters were tested at baseline and immediately after exercisein post-exercise recovery period.Result: Statistically significant reduction was observed in systolic blood pressure(SBP) and mean arterialpressure (MAP) in both pre-obese boys and girls groups while pulse pressure & mean arterial pressure innormal weight girls after exercise regime.Conclusion: We conclude that the exercise regime under consideration can produce some short-termbeneficial effects with respect to blood pressure in especially pre-obese group of adults.

AimTo investigate effect of V˙ O2max) among sedentary adults.MethodsWe studied 53 sedentary urbanites aged ≥50 years, randomised into: (1) male (MS) and (2) female (FS) undertaking three short-duration exercise (5–10 min) daily, and (3) male (ML) and (4) female (FL) exercising 30–60 min 3–5 days weekly. Resting systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate and V˙ O2max were measured at baseline and 8 weekly for 24 weeks.ResultsAt baseline, 50% MS, 61.5% ML, 53.8% FS and 53.8% FL had SBP ≥120 mm Hg, and 14.3% MS, 53.8% ML, 23.1% FS and 38.5% FL had DBP ≥80 mm Hg. At 24 weeks, where SBP remained ≥120 mm Hg, values decreased from 147±19.2 to 132.3±9.6 mm Hg (50% of MS), from 144±12.3 to 128±7.0 mm Hg (23.1% of ML), from 143.1±9.6 to 128.0±7.0 mm Hg (53.8% of FS) and from 152.3±23.7 to 129±3.7 mm Hg (30.8% of FL). For DBP ≥80 mm Hg, MS and FS percentages maintained, but values decreased from 101±15.6 to 84.5±0.7 mm Hg (MS) and 99.0±3.6 to 87.7±4.9 mm Hg (FS). In ML and FL, percentage with DBP ≥80 mm Hg dropped to 15.4% (86.1±6.5 to 82.5±3.5 mm Hg) and (91.4±5.3 to 83.5±0.71 mm Hg). V˙ O2max increased from 26.1±4.4 to 32.0±6.2 for MS, from 25.8±5.1 to 28.8±5.4 for ML (group differences p=0.02), from 20.2±1.8 to 22.7±2.0 for FS and from 21.2±1.9 to 24.2±2.7 for FL (groups differences p=0.38).ConclusionAccumulated moderate intensity exercise bouts of V˙ O2max improvements compared with current recommendations among sedentary adults.

A very recent methodology of data sampling with a sub-Nyquist rate is compressive sensing (CS). CS theory is established based on the higher sparsity of the data containing information. It uses this fact for recovery of the data sampled with much fewer samples than required by the Nyquist sampling theorem. CS addresses two main questions: (i) how to construct a compressive sensing matrix which satisfies the uniqueness required for later recovery, (ii) how to recover the original much longer signal vector than the current available compressively sensed one. This chapter reviews mainly the related theory and solution for the latter question of the signal/image recovery from the compressively sensed signal vectors. It presents a review of the pre-requirements in compressive sensing for the recovery process and summarizes the related theorems. The recovery of the signals after being compressively sensed with and without noise is studied and the main methodologies used are explained.

This chapter illustrates the characteristics of plant genetics-inspired evolutionary optimization (PGEO). The computation strategy of PGEO is inspired by the theory of Mendelian evolution. Presented PGEO optimizer is a binary-coded algorithm based on mainly three concepts from plant genetics: (i) the “denaturation” of DNA of two different species to produce the hybrid “offspring DNA,” (ii) the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations, (iii) the epimutation, through which organism resists for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary training technique. Based on this framework, four evolutionary operators—(1) flipper, (2) pollination, (3) breeding, and (4) epimutation—are created in the binary domain. The chapter gives characteristics and a detailed tutorial to PGEO theory.

Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.

Agents are intelligent entities that act flexibly and autonomously to make wise decisions based on their intelligence and experience. A multi-agent system (MAS) contains multiple intelligent interconnected collaborating agents for solving a problem beyond a single-agent ability. A smart grid combines advanced intelligence systems, control techniques, and sensing methods into an existing utility power network. For controlling smart grids, various control systems with different architectures have already been developed. MASs based control of power system operations has been shown to overcome the limitations of time required for analysis, relaying and protection, transmission switching, communication protocols, and management of plant control. These systems provide an alternative for the fast and accurate power networks control. This paper comprehensively overviews MASs used for the control of smart grids. This is intended to provide a wide spectrum on the status of smart grids, MAS-based control techniques and their implementation for the control of smart grids. Use of MASs to control various aspects of smart grids including management of energy, marketing energy, pricing, scheduling energy, reliability, the security of network, fault handling capability, communication between agents, SG-electrical vehicles, SG-building energy systems, and soft grids have been critically reviewed. More than a hundred publications on the topic of MAS-based control of smart grids are critically examined, classified, and arranged for fast reference.

As a great computational intelligence technique, artificial neural networks (ANNs) have intensively attracted the interest of researchers of artificial intelligence. Due to the easy implementation of ANN, vast types of structures and associated rules, their successful application can be seen in real-life and industrial problems. From a wide variety of ANN such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, and spiking ANN, we describe a multi-layer perceptron ANN with the focus on designing AI-based condition monitoring system. This chapter presents the neuroevolution-based monitoring system to detect the oil filter condition in agricultural (Ag) machines using meta-heuristic METO algorithm. Evolutionary learning algorithm finds the optimal weights of ANN along with the behavior of each neuron.

Compressive sensing is a recent data sampling technique with a variety of advantages over the classical Shannon–Nyquist based technique. The main theoretical approach to compressive sensing is based on the informative value of data according to sparsity where the higher sparsity indicates the higher information content. Therefore, while data samples are linearly mixed and sensed by a much smaller number of sensors and result in compressively sensed data of much less volume, the sparsity maximization is a strong approach to retrieving the original higher volume data from the compressed one. The sparsity analysis is the main approach to the idea of compressive sensing, and an efficient measure of sparsity has a key role in this regard. Although k-sparsity is the sparsity measure in use by compressive sensing techniques, it being well established in the theoretical analysis of compressive sensing, there are a variety of sparsity measures. This chapter reviews the sparsity measures from the k-sparsity already in use to be compared with other more complicated sparsity measures.

Compressive sensing is a recent highly applicative approach. It enables efficient data sampling at a much lower rate than the requirements indicated by the Nyquist theorem. Compressive sensing possesses several advantages, such as the much smaller need for sensory devices, much less memory storage, higher data transmission rate, many times less power consumption. Due to all these advantages, compressive sensing has been used in a wide range of applications. An application field of compressive sensing in health care is in neuro-signal acquisition. This chapter reviews the different aspects of neuro-signal compressive sensing in the literature. It provides an intuitive explanation of the formulas of implementing compressive sensing in neuro-signal-based applications with descriptive figures and block diagrams.

A very recent meta-heuristic optimizer is by inspiration from plant biology where the Mendel law of heredity is implemented through multi-species in two generations. Plant biology-inspired optimizer named as Mendelian Evolutionary Optimization Algorithm (METO), which has several advantages outperforming the state-of-the-art optimizers. It is highly capable of finding the best solution for multimodal problems with global optimal solution and computationally fast. METO not only performs well over the problems with around thirty variables but also performs well on the very high-dimensional problems such as hundred variables. Besides the literature introducing the characteristics of the METO, this chapter investigates the way METO explores the search space of the problem by exchanging the gene’s information between the multi-species. Each plan in a species represents by double strands DNA. Here, we will observe how METO covers the search space and avoid being stuck in a local minimum and moves toward the global solution. In this chapter, we investigate the behavior of the operators of METO such as flipper, pollination, self- and cross-breeding, and epimutation.