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Abstract Although there is ample proof of the advantages of infection prevention and Control (IPC) in acute-care hospitals, there is still some questions about the efficacy of IPC interventions for multidrug-resistant organisms (MDROs), and there is a need for the development of evidence-based practices. No healthcare facility has found a single effective technique to reduce MDRO. However, a multicomponent intervention that included improved barrier protection, chlorhexidine bathing, microbiological monitoring, and staff involvement significantly decreased the likelihood of infection in the patient surroundings with multidrug-resistant organisms. A practical strategy suited to reducing the burden of MDROs and their transmission potential in the critical care unit must be established in light of the global development of AMR. In this review, we summarize key findings of a multicomponent approaches to reduce MDROs in critical care units.

Abstract Objective This study aimed to determine the prevalence of polypharmacy, comorbidities and to investigate factors associated with polypharmacy among adult patients with Chronic Obstructive Pulmonary Disease (COPD). Methods This was a retrospective single-centre cross-sectional study. Patients with a confirmed diagnosis of COPD according to the GOLD guidelines between 28 February 2020 and 1 March 2023 were included in this study. Patients were excluded if a pre-emptive diagnosis of COPD was made clinically without spirometry evidence of fixed airflow limitation. Population characteristics were presented as frequency for categorical variable. Logistic regression analysis was used to identify predictors of polypharmacy. Results The study sample included a total of 705 patients with COPD. Most of the study sample were males (60%). The mean age of the study population was 65 years old. The majority of the study population had comorbid diseases (68%), hypertension and diabetes were the most common co-existent diseases. Around 55% of the study sample had polypharmacy. Females were significantly less likely to be on polypharmacy compared to males (OR = 0.68, 95% CI = [0.50–0.92], P-value = 0.012)). On the other hand, older patients aged 65.4 or more (OR = 2.31, 95% CI = [1.71–3.14], P-value ≤ 0.001), those with high BMI (≥ 29.2) (OR = 1.42, 95% CI = [1.05–1.92], P-value = 0.024), current smokers (OR = 1.9, 95% CI = [1.39–2.62], P-value ≤ 0.001), those who are receiving home care (OR = 5.29, 95% CI = [2.46–11.37], P-value ≤ 0.001), those who have comorbidities (OR = 19.74, 95% CI = [12.70–30.68], P-value ≤ 0.001) were significantly more likely to be on polypharmacy (p ≤ 0.05). Conclusions Polypharmacy is common among patients with COPD. Patients with high BMI, previous ICU hospitalization and older age are more likely to have polypharmacy. Future analytical studies are warranted to investigate outcomes in patients with COPD and polypharmacy.

Background: Productive cough with sputum is a prominent sign generally associated with respiratory diseases, including chronic obstructive pulmonary disease (COPD). Airway clearance devices are an option for COPD management, but physicians’ preferences for and clinical practice with them are not known. Objective: This study aims to explore preferences for and clinical practice with airway clearance devices among physicians in Saudi Arabia. Design: An observational, cross-sectional survey. Methods: A self-administered questionnaire was conducted between October 2022 and September 2023, which included a review of respiratory medication prescriptions by physicians for patients with COPD. The analysis was performed using the Statistical Package for the Social Sciences. Results: The participants were 445 physicians. The majority were female, accounting for 64.3% of the sample. Flutter and Acapella were the most commonly preferred airway clearance devices (45.8% and 20.7%, respectively). Among the participants, 12.6% reported unfamiliarity with any of the mentioned devices. Of the participants, 43.6% “usually” suggested the devices for patients with daily, difficult-to-clear, thick sputum, while 27% “sometimes” recommended them to COPD patients who had experienced four exacerbations or more. In routine clinical practice, physicians prescribe pharmacological therapies as the main treatment. The prescribing data showed that in the last year, there was no record of prescribed airway clearance devices for COPD by physicians. Conclusion: Family and pulmonary physicians prefer Flutter and Acapella devices, but a significant number of physicians are unaware of such devices. Prescribing data showed no record of prescribed airway clearance devices for COPD management. Further initiatives are needed to increase awareness in clinical practice.

Introduction: The COVID-19 pandemic has impacted healthcare workers (HCWs) worldwide, necessitating an understanding of its effects on their health and functional capacity. This study utilized the 6-Minute Walk Test (6MWT) and pulmonary function test (PFT) to evaluate post-infection recovery of HCWs, and analyzed the results in relation to comorbidities, symptoms, and healthcare admission. Methods: HCWs who previously tested positive for SARS-CoV-2 were recruited, and the 6MWT and PFT were conducted. Clinical data, including comorbidities, symptoms, hospitalization history, intensive care unit (ICU) admission, and mechanical ventilation, were collected. Binary logistic regression analysis and Fisher's exact test were employed to examine the associations between these factors and diffusing capacity of the lungs for carbon monoxide (DLCO) and the 6MWT. Results: The study comprised 80 HCWs, with various comorbidities and various presenting symptoms. On average, 167.24 days (± 63.83 days) post-SARS-CoV-2 infection, 34 (42.5 %) had a DLCO%

BackgroundGenerative artificial intelligence (Gen-AI) has emerged as a transformative tool in research and education. However, there is a mixed perception about its use. This study assessed the use, perception, prospect, and challenges of Gen-AI use in higher education.MethodsThis is a prospective, cross-sectional survey of university students in the United Kingdom (UK) distributed online between January and April 2024. Demography of participants and their perception of Gen-AI and other AI tools were collected and statistically analyzed to assess the difference in perception between various subgroups.ResultsA total of 136 students responded to the survey of which 59% (80) were male. The majority were aware of Gen-AI and other AI use in academia (61%) with 52% having personal experience of the tools. Grammar correction and idea generation were the two most common tasks of use, with 37% being regular users. Fifty-six percent of respondents agreed that AI gives an academic edge with 40% holding a positive overall perception about the use in academia. Comparatively, there was a statistically significant difference in overall perception between different age ranges (I2 = 27.39; p = 0.002) and levels of education (I2 = 20.07; p

Abstract Objective This study aims to assess the prevalence of needle phobia among Saudi and Egyptian adult populations. In addition, underlying causes and strategies that can be utilized to address needle fear were investigated. Methods A cross-sectional online survey study was conducted in Saudi Arabia and Egypt between 1 May and 30 June 2023. Participants aged 18 years and above and living in Saudi Arabia and Egypt were eligible to complete the survey. Participants were invited to participate in this study through social media platforms (Facebook, X, Snapchat, and Instagram). A convenience sampling technique was used to recruit the study participants. A 21-item questionnaire consisting of four sections including a Likert scale score was used to answer the research objectives. Numeric data were presented as mean ± SD. For categorical variables, percentages were used. Comparison between groups were made by Student’s t-test or Mann Whitney test according to data distribution. Chi squared tests for categorical values were conducted. A binary logistic regression analysis was conducted to investigate factors associated with needle phobia. Results A total of 4065 participants were involved in this study (Saudi Arabia: 2628 and Egypt: 1437). Around one-third of the study participants (36.5%) confirmed that they have needle phobia. Most of the study participants (81.1%) reported that they have had needle phobia since they were under 18 years of age. Pain, general anxiety, and fear of making a mistake during the procedure were the most commonly reported contributors for fear of needles during or before a medical procedure. Around 15.8% of the study participants reported that they have tried to get rid of phobia from needles. Non-surgical alternatives (such as oral medications and patches) and using smaller/thinner needles were the most commonly reported interventions that reduced fear of needles. Binary logistic regression analysis identified that females, those who are aged (41–50 years), widowed, those with bachelor’s degrees and higher education, and those unemployed were more likely to have needle phobia compared to others. Conclusion Our study highlighted the high prevalence of needle fear within an adult population in Egypt and Saudi Arabia. Females, those who are aged (41–50 years), those widowed, those with higher education degrees, those unemployed, those working in the health sector and people with low income were more likely to have needle phobia compared to others.

Abstract Background Nonadherence to therapies and psychological disorders are associated with poor asthma control. This study aims to assess the prevalence of anxiety and depressive symptoms, asthma control, and adherence to inhalers and to investigate whether there is an association of anxiety and depressive symptoms with adherence to inhalers and asthma control. Methods We measured anxiety and depressive symptoms using the Hospital Anxiety and Depression Scale in patients with asthma. Asthma Control Test and the 10-Item Test of Adherence to Inhalers Scale were used to assess levels of asthma control adherence to inhalers, respectively. Univariate and multivariate regression models assessed the associations of anxiety and depressive symptoms with adherence to inhalers and asthma control. Results A total of 287 patients completed the study, of whom 72% were female. The mean ± SD age and body mass index of our study population were 44 ± 13 years and 29 ± 7.2 kg/m2, respectively. Poor adherence to inhaler use was highly prevalent (49.8%; 95% CI: 43.8 to 55.7). The prevalence of anxiety, depression and poor asthma control was 27.2% (95% CI: 22.1 to 32.7), 20.9% (95% CI: 16.3 to 26.1), and 22.7% (95% CI: 17.9 to 27.9), respectively. We found a negative relationship between asthma control and anxiety, and depressive symptoms (adjusted β: -0.25; 95% CI: -0.36 to -0.14; p

Background: Acute respiratory tract infections (ARTIs) are frequently observed in infants and young children. The dynamics and transmission of ARTIs have been significantly impacted by the global COVID-19 pandemic. This study investigates the change in admission rates of Respiratory Syncytial virus (RSV) in a hospital in Saudi Arabia. Methods: The study included hospitalized pediatric patients who underwent RSV testing in three periods: pre-pandemic (2019), during the pandemic (2020–2021), and the immediate post-pandemic (2022). RSV testing was conducted using either PCR or antigen tests. Results: Between January 2019 to December 2022, out of 927 tested patients, 173 (18.7%) were positive for RSV. The yearly positivity rates were as follows: 42 (35.6%) of 118, 24 (33.3%) of 72, 15 (18.5%) of 81, and 92 (14%) of 656, yearly from 2019 to 2022, respectively (P

Abstract Background This study aimed to assess the knowledge and current practice of using the airway pressure release ventilation (APRV) mode with acute respiratory distress syndrome (ARDS) patients and identify barriers to not using this mode of ventilation among nurses who work in critical areas in Saudi Arabia. Methods Between December 2022 and April 2023, a cross-sectional online survey was disseminated to nurses working in critical care areas in Saudi Arabia. The characteristics of the respondents were analyzed using descriptive statistics. Percentages and frequencies were used to report categorical variables. Results Overall, 1,002 nurses responded to the online survey, of whom 592 (59.1%) were female. Only 248 (24.7%) nurses had ever used APRV mode, whereas only 229 (22.8%) received training on APRV mode. Moreover, 602 (60.0%) nurses did not know whether APRV was utilized in their hospital. Additionally, 658 (65.6%) nurses did not know whether APRV mode was managed using a standard protocol. Prone positioning was the highest recommended intervention by 444 (43.8%) when a conventional MV failed to improve oxygenation in patients with ARDS. 323 (32.2%) respondents stated that the P-high should be set equal to the plateau pressure on a conventional ventilator, while 400 (39.9%) said that the P-low should match PEEP from a conventional ventilator. Almost half of the respondents (446, 44.5%) stated that the T-high should be set between 4 and 6 s, while 415 (41.4%) said that the T-low should be set at 0.4 to 0.8 s. Over half of the nurses (540, 53.9%) thought that the maximum allowed tidal volume during the release phase should be 4–6 ml/kg. Moreover, 475 (47.4%) believed that the maximum allowed P-high setting should be 35 cm H2O. One-third of the responders (329, 32.8%) stated that when weaning patients with ARDS while in APRV mode, the P-high should be reduced gradually to reach a target of 10 cm H2O. However, 444 (44.3%) thought that the T-high should be gradually increased to reach a target of 10 s. Half of the responders (556, 55.5%) felt that the criteria to switch the patient to continuous positive airway pressure (CPAP) were for the patient to have an FiO2 ≤ 0.4, P-high ≤ 10 cm H2O, and T-high ≥ 10 s. Lack of training was the most common barrier to not using APRV by 615 (61.4%). Conclusion The majority of nurses who work in critical care units have not received sufficient training in APRV mode. A significant discrepancy was observed regarding the clinical application and management of APRV parameters. Inadequate training was the most frequently reported barrier to the use of APRV in patients with ARDS.

Abstract Objective To investigate the prevalence of intensive care unit (ICU) admission and its predictors among hospitalized chronic obstructive pulmonary disease (COPD) patients. Methods An observational retrospective study was conducted. All patients with a confirmed diagnosis of COPD according to the GOLD guidelines between 28 and 2020 and 1 March 2023 at Al-Noor Specialist Hospital were included in this study. Patients were excluded if a preemptive diagnosis of COPD was made clinically without spirometry evidence of fixed airflow limitation. Descriptive results were presented as frequency (percentage) for categorical variables and mean (SD) for continuous variables and to estimate prevalence of ICU admission. Predictors of ICU admission among hospitalized COPD patients were determined using logistic regression analysis. A SPSS (Statistical Package for the Social Sciences) version 25 was used to perform all statistical analysis. Results A total of 705 patients with COPD were included in this study. The mean age was 65.4 (25.3) years. Around 12.4% of the hospitalized patients were admitted to the ICD. Logistic regression analysis identified that older age (OR; 1.92, (1.41–2.62)), smoking (OR; 1.60 (1.17–2.19)), and having specific comorbidities (Hypertension (OR; 1.98 (1.45–2.71)), Diabetes mellitus (OR; 1.42 (1.04–1.93)), GERD (OR; 2.81 (1.99–3.96)), Ischemic heart disease (OR; 3.22 (2.19–4.75)), Obstructive sleep apnea syndrome (OR; 2.14 (1.38–3.33)), stroke (OR; 4.51 (2.20–9.26))) were predictors of ICU admissions among patients with COPD. Conclusions Our study found that a step-up approach to inpatient COPD management requires admission to the ICU in 12.4%, for which age, smoking status, cardiovascular, and stroke were important predictors. Further clinical research is needed to provide a validated model that can be incorporated into clinical practice to monitor this patient population during their admission and identify at-risk individuals for early transfer to higher acuity settings and intensive care units.

Background: Night shift duties are crucial in the ICU to ensure care continuity, where critically ill patients require round-the-clock care. However, cumulative night shift duties may disturb circadian rhythm, insomnia, fatigue, and depression, and require further elucidation. Objectives: This study aims to examine the negative consequences of various night shift patterns on insomnia, fatigue, and mental health of ICU Workers. Methods: A cross-sectional study examined how cumulative night shift duty affects insomnia, fatigue, and mental health in critical care providers (CCPs). Results: A total of 1006 participants completed this study between June 2022 and March 2023, including 54.5 % males. About 35 % were between 20 and 30 years of age, and Respiratory Therapists accounted for approximately 46.5 % of the entire sample. Most of our respondents (476; 47 %) reported working night shifts, with a monthly range of 8–15 nights. The prevalence rates for moderate to severe clinical insomnia, fatigue, and moderate to severe depression were 42 %, 48 %, and 32 %, respectively. CCPs working 8–15 nights had a 2-fold risk of clinical insomnia than those working fewer than eight nights with (AOR) and 95 % (CI) of 2.12 and 1.41–3.20, while those working ≥16 nights per month had a greater incidence of clinical insomnia compared to those working

BackgroundTobacco smoking is one of the most significant health-related problems worldwide. However, the prevalence and sociodemographic determinants of tobacco and nicotine use among students on healthcare courses in Saudi Arabia are limited.MethodsThis cross-sectional study used a questionnaire that was distributed via SurveyMonkey between November 2022 and June 2023 to all student universities offering healthcare courses. Data are presented as frequency and percentages. The associated sociodemographic factors with current tobacco and nicotine use were subjected to logistic regression.ResultsOverall, 5,012, of whom 3,872 (77.25%) were males, responded to the online survey. The prevalence of current tobacco and nicotine use was 3,553 (71%). The majority of users used electronic nicotine delivery systems (1,622: 32.36%). The current use of cigarettes (AOR: 1.75 (1.42 to 2.15)), e-cigarettes (AOR: 1.17 (1.01 to 1.37)), and smokeless tobacco substances (AOR: 1.35 (1.02 to 1.90)) were more pronounced among males compared to female students. However, the current use of a hookah was less among males (AOR: 0.74 (95% CI: 61 to 0.91)). Smoking cigarettes was practiced less among students living in other regions compared to the Central Region. However, smokeless tobacco substances seem to be more prevalent in the Western, Southern, and Northern Regions, (AOR: 1.57 (95% CI: 1.09 to 2.26)), (AOR: 1.43 (95% CI: 1.04 to 1.95)), and (AOR: 1.57 (95% CI: 1.09 to 2.26)), respectively.ConclusionSmoking is prevalent among students in the healthcare disciplines, with electronic nicotine delivery systems being the most used product. Several sociodemographic factors were associated with higher tobacco or nicotine use.

Introduction: COVID-19 pandemic adversely affected healthcare workers (HCWs). Here, we evaluate the occurence of long-COVID-19 symtoms among HCWs. Methods: This is a questionnaire-based study of HCWs who had COVID-19 in two medical centers in Saudi Arabia and were mostly vaccinated. Results: The study included 243 HCWs with a mean age (+ SD) of 36.1 (+ 7.6) years. Of them, 223 (91.8%) had three doses of COVID-19 vaccine, 12 (4.9%) had four doses, and 5 (2.1%) had two doses. The most common symptoms at the start of the illness were cough (180, 74.1%), shortness of breath (124, 51%), muscle ache (117, 48.1%), headache (113, 46.5%), sore throat (111, 45.7%), diarrhea (109, 44.9%) and loss of taste (108, 44.4%). Symptoms lasted for one week and 2 months and 3 months in 15 (6.2%). The main symptoms present> 3 months were hair loss (8, 3.3%), cough (5, 2.1%), and diarrhea (5, 2.1%). A binomial regression analysis showed no relationship between persistence of symptoms for> 3 months and other demographic or clinical symptoms characteristics. Conclusion: The study showed a low rate of the occurence of long-COVID> 3 months during the Omicron-wave among mostly vaccinated HCWs with no significant comorbidities. Furhter studies are needed to examine the effect of different vaccines on long-COVID-19 among HCWs.

Background: Obesity is a common cause of dyspnea. However, the impacts of obesity on spirometry parameters, particularly among individuals with unexplained dyspnea, have not been well investigated. Objectives: This study aimed to explore the prevalence of overweight and different classes of obesity and their effect on spirometry parameters among subjects with unexplained dyspnea in Saudi Arabia. Methods: We conducted a retrospective electronic medical record review of individuals with unexplained dyspnea who visited our pulmonary clinic between January 2016 and December 2022 and assessed the association of body mass index (BMI) with spirometry parameters. After we classified the subjects based on their BMI values, we determined the impacts of increased BMI on spirometry parameters. Results: The sample included a total of 978 subjects with unexplained dyspnea. The prevalence of overweight and obesity among our study sample was high (33 % and 47 %, respectively). All spirometry parameters: forced vital capacity (FVC) L, forced expiratory volume in 1 s (FEV1) L, forced expiratory flow at 25–75 % (FEF 25–75 %) L/sec and peak expiratory flow (PEF) L/sec were significantly lower in obese individuals with dyspnea compared to normal weight subjects. In addition, our findings showed a negative correlation between BMI and FVC, FEV1, FEF 25–75 %, and PEF. Conclusion: The high prevalence of obesity and overweight and the impairment of lung function because of high body weight among subjects with dyspnea point to the need for routine assessment and the evaluation of nutritional status in primary health care facilities for early intervention.

BackgroundLung cancer is currently the most fatal form of cancer worldwide, ranking as the fourth most prevalent type in Saudi Arabia, particularly among males. This trend is expected to increase with growing population, lifestyle changes, and aging population. Understanding the awareness of the Saudi population regarding the risk factors and symptoms of lung cancer is necessary to attenuate the predicted increase in cases.MethodA cross-sectional, population-based survey was performed using a previously validated questionnaire (Lung CAM). Multiple linear regression analysis was used to assess variables associated with deficiency in knowledge and awareness of risk factors and symptoms of lung cancer.ResultsMajority of the 15,099 respondents were male (65%), aged between 18 and 30 years (53%), 50% of which were educated up to a bachelor’s degree level. Overall awareness of lung cancer signs and symptoms was 53%, with painful cough and coughing up blood being the best-known symptoms. Conversely, persistent shoulder pain (44%) and clubbing fingers (47%) were the least known lung cancer symptoms. Also, 60% of the respondents showed low confidence in identifying the signs and symptoms of lung cancer. The overall awareness of the risk factors for lung cancer development was 74%, with first-hand (74%) and second-hand (68%) smoking being the most known risk factors. However, only ≤ 62% know the other non-smoking risk factors. Awareness of the risk factors and symptoms of lung cancer depended on age, gender, education, marital and employment status (p < 0.001).ConclusionPublic awareness of the risk factors and symptoms of lung cancer in Saudi Arabia is inadequate and heavily dependent on education and socio-economic status. Awareness can be improved through campaigns to raise awareness about other lesser-known lung cancer risk factors and symptoms.

Background: Limited data is available on awareness and clinical management of the airway pressure release ventilation (APRV) mode of ventilation for acute respiratory distress syndrome (ARDS) patients among physicians who work at in adult critical areas. This study aimed to assess the knowledge and current practice of using APRV mode with ARDS patients and identify barriers to not using this mode of ventilation among physicians who work in adult critical areas in Saudi Arabia. Methods: Between November 2022 and April 2023, a cross-sectional online survey was disseminated to physicians who work in adult critical areas in Saudi Arabia. The characteristics of the respondents were analyzed using descriptive statistics. Percentages and frequencies were used to report categorical variables. Results: Overall, 498 physicians responded to the online survey. All responders (498, 100 %) reported that APRV is indicated in patients with ARDS, but 260 (52.2 %) did not know if there was an institutionally approved APRV protocol. Prone positioning was the highest recommended intervention by 164 (33.0 %) when a conventional MV failed to improve oxygenation in patients with ARDS. 136 (27.3 %) responders stated that the P-high should be set equal to the plateau pressure on a conventional ventilator while 198 (39.8 %) said that P-low should be 0 cmH2O. Almost half of (229, 46.0 %) responders stated that the T-high should be set between 4 and 6 s, while 286 (57.4 %) said that the T-low should be set at 0.4–0.8 s. The maximum allowed tidal volume during the release phase should be 4–6 ml/kg. Moreover, just over half (257, 51.6 %) believed that the maximum allowed P-high setting should be 35 cmH2O. One third of the responders (171, 34.3 %) stated that when weaning patients with ARDS while in APRV mode, the P-high should be reduced gradually to reach a target of 10 cmH2O. However, 284 (36.9 %) thought that the T-high should be gradually increased to reach a target of 10 s. Most responders (331, 66.5 %) felt that the criteria to switch the patient to CPAP would be to have an FiO2 ≤ 0.4, P-high ≤10 cm H2O, and T-high ≥10 s. Lack of training has been the most common barrier to not using APRV by 388 (77.9 %). Conclusion: There is a lack of consensus on the use of APRV mode, probably due to several barriers. While there were some agreements on the management of ventilation and oxygenation, there were variations in the selection of the initial setting of APRV. Education, training, and the presence of standardized protocols may help to provide better management.

Abstract Objective To investigate the trends in hospital admissions and medication prescriptions related to asthma and chronic obstructive pulmonary disease (COPD) in England and Wales. Methods An ecological study was conducted between April 1999 and April 2020 using data extracted from the hospital episode statistics database in England and the patient episode database for Wales. The Office of National Statistics mid-year population estimates for 1999 through 2020 were collected, and medication prescription data for 2004–2020 were extracted from the prescription cost analysis database. Results The total annual number of COPD and asthma hospital admissions for various causes increased by 82.2%, from 210,525 in 1999 to 383,652 in 2020, representing a 59.1% increase in hospital admission rate (from 403.77 in 1999 to 642.42 per 100,000 persons in 2020, p

Abstract Background Multiple vaccines have been tested in clinical trials for their efficacy and safety. In Saudi Arabia, Pfizer–BioNTech or Moderna were approved for children, however, previous studies to report their safety profile are limited. This research aims to understand the side effect of children's vaccination against SARS-CoV-2 infection in Saudi Arabia. Methods This was an observational retrospective cross-sectional study was conducted using an online survey in Saudi Arabia from March to May 2022. The inclusion criteria were parents aged 18 years and above who live in Saudi Arabia and have vaccinated their children. The self-reported questionnaire was adopted from published studies to investigate the study objectives Descriptive statistics were used to describe patients’ demographic characteristics, continuous data were reported as mean ± S.D., categorical data were reported as percentages (frequencies), and logistic regression was used to identify predictors of persistent post-COVID-19 symptoms. Results This study had a total of 4,069 participants. Only 41.9% of the participants reported that their child(ren) had been infected with the coronavirus. 2.00 was the median number of children (IQR: 1.00–4.00). More than half of the study participants (64.2%) reported that a family member had been infected with the coronavirus. Both parents received COVID-19 vaccination, according to most participants (88.7%). Most participants (70.5%) stated that all children who met the vaccination criteria had received the vaccine. Most participants (83.5%) said their child or children had two doses of their vaccine, and about half (50.4%) of those who received the vaccine reported experiencing side effects. In addition, the majority (78.9%) reported that the side effects appeared within one day of receiving the vaccine, and nearly two-thirds (65.7%) reported that the side effects lasted between one and three. A total of 11,831 side effects cases were documented. Pain at the injection site, hyperthermia, and fatigue were the most reported side effects, accounting for 15.3%, 14.1%, and 13.2%, respectively. Conclusion It appears that the side effects of the COVID-19 vaccine for children are minor, tolerable, and like those described previously in clinical trials. Our data should encourage the public about the safety of receiving the COVID-19 vaccine for children.

Summary: Background: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively. Funding: The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38).

Pulmonary hypertension (PH) due to chronic obstructive pulmonary disease (COPD) is classified as Group 3 PH, with no current proven targeted therapies. Studies suggest that cigarette smoke, the most risk factor for COPD can cause vascular remodelling and eventually PH as a result of dysfunction and proliferation of pulmonary artery smooth muscle cells (PASMCs) and pulmonary artery endothelial cells (PAECs). In addition, hypoxia is a known driver of pulmonary vascular remodelling in COPD, and it is also thought that the presence of hypoxia in patients with COPD may further exaggerate cigarette smoke-induced vascular remodelling; however, the underlying cause is not fully understood. Three main pathways (prostanoids, nitric oxide and endothelin) are currently used as a therapeutic target for the treatment of patients with different groups of PH. However, drugs targeting these three pathways are not approved for patients with COPD-associated PH due to lack of evidence. Thus, this review aims to shed light on the role of impaired prostanoids, nitric oxide and endothelin pathways in cigarette smoke- and hypoxia-induced pulmonary vascular remodelling and also discusses the potential of using these pathways as therapeutic target for patients with PH secondary to COPD.

Abstract Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs a novel UAV assisted DL based image classification model (UAVDL-ICM) for Industry 4.0 environment. The proposed UAVDL-ICM technique involves an ensemble of voting based three DL models, namely Residual network (ResNet), Inception with ResNetv2, and Densely Connected Networks (DenseNet). Also, the hyperparameter tuning of these DL models takes place using a genetic programming (GP) approach. Finally, Oppositional Water Wave Optimization (OWWO) with Fully Connected Deep Neural networks (FCDNN) is employed for the classification of aerial images. A wide range of simulations takes place and the results are examined in terms of different parameters. A detailed comparative study highlighted the betterment of the UAVDL-ICM technique compared to other recent approaches.

BackgroundPulmonary hypertension due to chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) is classified as group 3 pulmonary hypertension. Inhaled treprostinil, a prostaglandin I2 analogue also known as prostacyclin, has recently been approved as a first drug for patients with pulmonary hypertension secondary to ILD. However, due to a lack of evidence, no therapies are currently approved for those with COPD-associated pulmonary hypertension. Thus, this systematic review aims to summarise the current evidence to assess the impact of inhaled prostaglandin I2 analogue use on the pulmonary hemodynamics, exercise function, lung function, and gas exchange in patients with pulmonary hypertension due to COPD.MethodsWe systematically searched the electronic databases of Medline, Embase, Scopus and Cochrane from inception to 1 February 2023. Studies of adult patients with a confirmed diagnosis of COPD-associated pulmonary hypertension who received inhaled drugs targeting the prostacyclin pathway were included in the systematic review. Case reports, systematic reviews, conference abstracts with no full text, non-full-text articles, non-English manuscripts and book chapters were excluded from this systematic review. A risk-of-bias assessment was carried out for the studies included in this review, using two different Cochrane risk-of-bias tools for randomised and non-randomised clinical trials.ResultsA total of four studies met our inclusion criteria and were included in this systematic review. The results of one prospective clinical trial showed an improvement in the pulmonary hemodynamics (e.g., cardiac index, cardiac output and mean pulmonary artery pressure) in response to inhaled prostacyclin use in patients with pulmonary hypertension secondary to COPD. However, the severity of dyspnoea, lung function, exercise capacity and gas exchange were not affected when inhaled prostacyclin was used for patients with COPD-related pulmonary hypertension.ConclusionThis systematic review demonstrated that although inhaled prostacyclin does not seem to improve COPD-related outcomes (e.g., lung function and exercise capacity), short-term use of inhaled prostacyclin has the potential to reduce mean pulmonary artery pressure and pulmonary vascular resistance without impairing ventilation-perfusion mismatch. Further studies with larger sample sizes are warranted.Systematic review registrationCRD42022372803, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=372803.

The result of the movement restrictions during the COVID-19 pandemic was an impromptu and abrupt switch from in-person to online teaching. Most focus has been on the perception and experience of students during the process. The aim of this international survey is to assess staffs' perspectives and challenges of online teaching during the COVID-19 lockdown. Cross-sectional research using a validated online survey was carried out in seven countries (Brazil, Saudi Arabia, Jordan, Indonesia, India, the United Kingdom, and Egypt) between the months of December 2021 and August 2022, to explore the status of online teaching among faculty members during the COVID-19 pandemic. Variables and response are presented as percentages while logistic regression was used to assess the factors that predict levels of satisfaction and the challenges associated with online instruction. A total of 721 response were received from mainly male (53%) staffs. Most respondents are from Brazil (59%), hold a Doctorate degree (70%) and have over 10 years of working experience (62%). Although, 67% and 79% have relevant tools and received training for online teaching respectively, 44% report that online teaching required more preparation time than face-to-face. Although 41% of respondents were uncertain about the outcome of online teaching, 49% were satisfied with the process. Also, poor internet bandwidth (51%), inability to track students' engagement (18%) and Lack of technical skills (11.5%) were the three main observed limitations. Having little or no prior experience of online teaching before the COVID-19 pandemic [OR, 1.58 (95% CI, 1.35–1.85)], and not supporting the move to online teaching mode [OR, 0.56 (95% CI,0.48–0.64)] were two main factors independently linked with dissatisfaction with online teaching. While staffs who support the move to online teaching were twice likely to report no barriers [OR, 2.15 (95% CI, 1.61–2.86)]. Although, relevant tools and training were provided to support the move to online teaching during COVID-19 lockdown, barriers such as poor internet bandwidth, inability to track students’ engagement and lack of technical skills were main limitations observed internationally by teaching staffs. Addressing these barriers should be the focus of higher education institution in preparation for future disruptions to traditional teaching modes.

Background: Simulation-based education (SBE) provides a safe, effective, and stimulating environment for training medical and healthcare students. This is especially valuable for skills that cannot be practiced on real patients due to ethical and practical reasons. We aimed to assess medical students’ attitude, perception, and experience of simulation-based medical education in Saudi Arabia. Method: A validated cross-sectional survey, using the KidSIM scale, was conducted to measure the level of perception and experience of students from different health sciences specialties toward integrating simulation as an educational tool. Participants responded to questions investigated the importance of simulation, opportunities for Inter-Professional Education (IPE), communication, roles and responsibilities, and situation awareness. Only students with previous experience of SBE were considered for participation. Result: This survey was completed by 246 participants, of whom 165 (67%) were male students and 228 (93%) were aged between the range of 18–30 years old. Of the respondents, 104 (67%) were respiratory care students, 90 (37%) were anesthesia technology students, and 45 (18%) were nursing students. Most of the participants had previous experience in IPE simulation activities (84%), and more than half of the students (54%) had a grade point average (GPA) ranging between 5.00 and 4.50. Overall, students had positive attitudes toward and beliefs about SBE, with a mean score of 129.76 ± 14.27, on the KidSIM scale, out of 150. Students’ GPA was significantly associated with a better perception to the relevance of simulation (p = 0.005), communication (p = 0.003), roles and responsibilities (p = 0.04), and situation awareness (p = 0.009). GPA is merely the sole predictor for positive attitude toward simulation with coefficient Beta value of 4.285 (p = 0.001). There were no significant correlations between other students’ characteristic variables (gender, specialty, study year, experience in IPE, and prior critical care experience). Conclusion: We found that health sciences students’ perception of SBE in Saudi Arabia is generally positive, and students’ performance is a significant determinant of the positive perception.

The ability to predict the radioactive soil radon gas concentration is important for human beings because it serves as a precursor to earthquakes. Several studies have been conducted across the globe to confirm the correlation of radon emission dynamics and earthquakes, and concluded that the soil radon gas is the witness of anomalous behaviour before the occurrences of several earthquakes. This anomalous behavior can help to construct a better prediction model for earthquake forecasting. This paper aims at employing different ensemble and individual machine learning methods on real time radon time series data with different scenarios to predict anomalies in data caused by the seismic activities.The ensemble methods include boosted tree, bagged cart and boosted linear model while standalone machine learning methods include support vector machine with linear and radial kernels and k-nearest neighbors ( ${K}$ -NN). We tested the methods on a dataset recorded on the fault line located in Muzaffarabad. Time series data was collected over a period ranging from March 1, 2017 to May 11, 2018 including nine(09) earthquakes. The methods are tested in four different settings with 10 times 10 folds cross validation procedure over the time window of 1 to 4. The repeated 10 fold cross validation is performed to reduce the noise in the model performance estimation by replicating the 10 fold cross validation procedure 10 times. Statistical performance evaluation measures viz. root mean square error (RMSE), root mean squared log error (RMSLE), mean absolute percentage error (MAPE), percentage bias (PB), and mean squared error (MSE) have been calculated for the assessment of performance. In setting 1, the support vector machine with radial kernel performs better with the minimum RMSE score of 1381.023 when compared to other prediction models. In setting 3, it can be observed through different performance metrics such as RMSE, the value in the range [1262.864, 1409.616] which is minimum when other prediction models for predicting soil radon gas concentration dataset. For setting 4, the boosted tree model yielded the minimum RMSE and MAPE scores of 1573.174 and 0.056 respectively. Findings of the study shows that boosted tree and support vector machine with radial kernel proved to be better regression models for the prediction of anomalies in soil radon gas concentration during seismic activities. An important finding of this study suggests that by employing boosted tree ensemble method make us able to accurately predict soil radon gas concentration automatically from environmental parameters.

BackgroundChronic obstructive pulmonary disease (COPD) is associated with disruption in autonomic nervous control of the heart rhythm. We present here quantitative evidence of the reduction in HRV measures as well as the challenges to clinical application of HRV in COPD clinics.MethodFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we search in June 2022 Medline and Embase databases for studies reporting HRV in COPD patients using relevant medical subject headings (MeSH) terms. The quality of included studies was assessed using the modified version of the Newcastle–Ottawa Scale (NOS). Descriptive data were extracted, while standardized mean difference was computed for changes in HRV due to COPD. Leave-one-out sensitivity test was performed to assess exaggerated effect size and funnel plots to assess publication bias.ResultsThe databases search yielded 512 studies, of which we included 27 that met the inclusion criteria. The majority of the studies (73%) had a low risk of bias and included a total of 839 COPD patients. Although there were high between-studies heterogeneity, HRV time and frequency domains were significantly reduced in COPD patients compared with controls. Sensitivity test showed no exaggerated effect sizes and the funnel plot showed general low publication bias.ConclusionCOPD is associated with autonomic nervous dysfunction as measured by HRV. Both sympathetic and parasympathetic cardiac modulation were decreased, but there is still a predominance of sympathetic activity. There is high variability in the HRV measurement methodology, which affects clinical applicability.

The particulate matter (PM) is emitted from diverse sources and affects the human health very badly. In the past, researchers applied different automated computational tools in the predication of PM. Accurate prediction of PM requires more relevant features and feature importance. In this research, we first extracted the multimodal features from time domain standard deviation average (SDAPM), standard deviation of standard deviation (SDSD), standard deviation of particulate matter (SDPM), root-mean square of standard deviation (RMSSD), and nonlinear dynamical measure wavelet entropy (WE) – Shannon, norm, threshold, multiscale entropy based on KD tree (MSEKD), and multiscale approximate entropy (MAEnt). We then applied the intelligent-based Bayesian inference approach to compute the strength of relationship among multimodal features. We also computed total incoming and outgoing forces between the features (nodes). The results reveal that there was a very highly significant correlation (p-value

Abstract Background Identifying trends of hospital admissions for respiratory diseases is crucial for public health and research to guide future clinical improvements for better outcomes. This study aims to define the trends of respiratory disease-related hospital admissions (RRHA) in England and Wales between 1999 and 2019. Methods An ecological study was conducted using hospital admission data taken from the Hospital Episode Statistics database in England and the Patient Episode Database for Wales. Hospital admissions data for respiratory diseases were extracted for the period between April 1999 and March 2019. The trend in hospital admissions was assessed using a Poisson model. Results Hospital admission rate increased by 104.7% [from 1535.05 (95% CI 1531.71–1538.38) in 1999 to 3142.83 (95% CI 3138.39–3147.26) in 2019 per 100,000 persons, trend test, p

BackgroundThe COVID-19 pandemic and associated preventative measures introduced a shock to the teaching paradigm in Saudi Arabia and the world. While many studies have documented the challenges and perceptions of students during the COVID-19 pandemic, less attention has been given to higher education staff. The aim of the present investigation is to evaluate the staff’s perception and experiences of online teaching during the COVID-19 pandemic.Materials and methodsA validated survey was conducted between December 2021 and June 2022 in Saudi Arabian Universities to assess the status of online teaching during the COVID-19 pandemic among faculty members. The collected responses were exploratively and statistically analyzed.ResultsA total of 1117 response was received. About 66% of the respondents were male and 90% of them hold postgraduate degree. Although rarely or occasionally teach online pre-COVID-19, only 33% of the respondents think the transition was difficult and 55% of them support the move. Most respondents received adequate training (68%) and tools (80%) and 88% of the respondents mentioned that they did not accrue additional workload in online study design. While the perception of online teaching was mostly positive (62%) with high satisfaction (71%). However, 25% of the respondents reported that a poor internet bandwidth was an obstacle and 20% was unable to track students’ engagement. Respondents with more years of experience, previous training, support, or perceived online transition as easy were also more likely to be satisfied with the process. Also, older respondents, those who support the transition and those with previous training were less likely to report barriers (all p < 0.001).ConclusionThe perception and experience of transition to online teaching during the COVID-19 pandemic in Saudi Arabia were positive. Low internet bandwidth and inability to track students’ limited effective online teaching. Work experience, previous training, and positive perception are the main factors that influence staff online teaching satisfaction.

Individuals with COPD are at higher risk of severe disease and mortality if they contract COVID-19. Shielding and social distancing have negatively impacted the delivery of routine care for COPD patients, which should be maintained to avoid further deterioration. We aimed to review the literature about the key toolkits of non-pharmacological treatments of COPD patients before and during the COVID-19 pandemic. In particular, we focused on smoking cessation, pulmonary rehabilitation, and telehealth delivery approaches during the COVID-19 crisis. Smoking cessation services are important to mitigate the spread of the virus, especially in people with chronic lung disease; the pandemic, in one way or another, has helped to enhance people’s motivation to quit smoking. Also, tele-rehabilitation is considered as effective as conventional pulmonary rehabilitation in controlling symptoms of disease, promoting physical activity, and enhancing self-management of COPD. Tele-rehabilitation offers flexibility and it could be the dominant mode for providing a pulmonary rehabilitation programme. Finally, the use of telehealth (TH) modes has trended during the pandemic. Consensus about the effectiveness of TH in reducing exacerbation events is still inconclusive. In the context of COPD, further clinical research must concentrate on understanding attitudes, behaviours, and motivations towards smoking cessation. Further recommendations include gauging the feasibility of a long-term tele-rehabilitation programme in large COPD populations, designing more COPD-related mobile apps, and evaluating the feasibility of tele-rehabilitation in clinical practice.

A high body mass index (BMI) is a known risk factor for coronavirus infection in hospitalized patients. Our study examined the association between BMI and complications and the survival rate among COVID-19 patients. This retrospective analysis used data from a tertiary hospital in the Eastern Region of Saudi Arabia during two waves of the COVID-19 pandemic. The study included 600 participants, with the majority being between 41 and 60 years old (41.3%) and men comprising 63.5% of the sample. Approximately 42.5% of patients were obese, and 31.3% were overweight. The results showed that BMI was significantly linked to respiratory diseases (p = 0.013); end-stage renal disease (p = 0.021); and cardiovascular disease (p = 0.003) but not diabetes mellitus (p = 0.064). Death occurred in 10.8% of patients; 33.8% were admitted to the ICU; 13.8% needed mechanical ventilation; and 60.7% had lung infiltration. Obese patients with oxygen saturation levels below 93% were 2.45 times more likely to require mechanical ventilation than those in the normal-weight group. Overweight and obese patients were also more likely to require mechanical ventilation than normal-weight patients, with odds ratios of 3.66 and 2.81, respectively. The BMI categorized was not associated with survival rate in COVID-19-hospitalized patients using Kaplan-Meier survival plots (p = 0.061). However, the BMI categorized was associated with survival rate in COVID-19 ICU patients (p < 0.001). In addition, the overweight showed a statistically significant higher hazard ratio of 2.22 (p = 0.01) compared to normal-weight patients using a Cox regression model. A high BMI was identified as an independent risk factor for reduced oxygen saturation (

Introduction: Invasive mechanical ventilation (IMV) is associated with several complications. Placement of a long-term airway (tracheostomy) is also associated with short and long-term risks for patients. Nevertheless, tracheostomies are placed to help reduce the duration of IMV, facilitate weaning and eventually undergo successful decannulation. Methods: We performed a narrative review by searching PubMed, Embase and Medline databases to identify relevant citations using the search terms (with synonyms and closely related words) “non-invasive ventilation”, “tracheostomy” and “weaning”. We identified 13 publications comprising retrospective or prospective studies in which non-invasive ventilation (NIV) was one of the strategies used during weaning from IMV and/or tracheostomy decannulation. Results: In some studies, patients with tracheostomies represented a subgroup of patients on IMV. Most of the studies involved patients with underlying cardiopulmonary comorbidities and conditions, and primarily involved specialized weaning centres. Not all studies provided data on decannulation, although those which did, report high success rates for weaning and decannulation when using NIV as an adjunct to weaning patient off ventilatory support. However, a significant percentage of patients still needed home NIV after discharge. Conclusions: The review supports a potential role for NIV in weaning patients with a tracheostomy either off the ventilator and/or with its decannulation. Additional research is needed to develop weaning protocols and better characterize the role of NIV during weaning.

Background Acute exacerbations (AEs) and disease progression in interstitial lung disease (ILD) pose important challenges to clinicians and patients. AEs of ILD are variable in presentation but may result in rapid progression of ILD, respiratory failure and death. However, in many cases AEs of ILD may go unrecognised so that their true impact and response to therapy is unknown. The potential for home monitoring to facilitate early, and accurate, identification of AE and/or ILD progression has gained interest. With increasing evidence available, there is a need for a systematic review on home monitoring of patients with ILD to summarise the existing data. The aim of this review was to systematically evaluate the evidence for use of home monitoring for early detection of exacerbations and/or progression of ILD. Method We searched Ovid-EMBASE, MEDLINE and CINAHL using Medical Subject Headings (MeSH) terms in accordance with the PRISMA guidelines (PROSPERO registration number CRD42020215166). Results 13 studies involving 968 patients have demonstrated that home monitoring is feasible and of potential benefit in patients with ILD. Nine studies reported that mean adherence to home monitoring was >75%, and where spirometry was performed there was a significant correlation (r=0.72–0.98, p

Background Forced Oscillation Technique (FOT) is an innovative tool to measure within-breath reactance at 5 Hz (ΔXrs5Hz) but its feasibility and utility in acute exacerbations of COPD (AECOPD) is understudied. Methods A prospective observational study was conducted in 82 COPD patients admitted due to AECOPD. FOT indices were measured and the association between these indices and spirometry, peak inspiratory flow rate, blood inflammatory biomarkers and patient-reported outcomes including assessment of dyspnoea, quality of life, anxiety and depression and frailty at admission and discharge were explored. Results All patients were able to perform FOT in both sitting and supine position. The prevalence of expiratory flow limitation (EFL) in the upright position was 39% (32 out of 82) and increased to 50% (41 out of 82) in the supine position. EFL (measured by ΔXrs5Hz) and resistance at 5 Hz (Rrs5Hz) negatively correlated with forced expiratory volume in 1 s (FEV1); those with EFL had lower FEV1 (0.74±0.30 versus 0.94±0.36 L, p = 0.01) and forced vital capacity (1.7±0.55 versus 2.1±0.63 L, p = 0.009) and higher body mass index (27 (21–36) versus 23 (19–26) kg·m−2, p = 0.03) compared to those without EFL. During recovery from AECOPD, changes in EFL were observed in association with improvement in breathlessness. Conclusion FOT was easily used to detect EFL during hospitalisation due to AECOPD. The prevalence of EFL increased when patients moved from a seated to a supine position and EFL was negatively correlated with airflow limitation. Improvements in EFL were associated with a reduction in breathlessness. FOT is of potential clinical value by providing a noninvasive, objective and effort-independent technique to measure lung function parameters during AECOPD requiring hospital admission.

As evidence continues to emerge, our understanding of the relationship between smoking and COVID-19 prognosis is steadily growing. An early outlook from World Health Organisation (WHO) indicates that smokers are more vulnerable to severe COVID-19 disease and are also more likely to be infected, as frequent motions from hand to mouth and sharing of tobacco products such as waterpipes increased the possibility of being infected. In this commentary, we discuss some of the latest evidence on smoking and COVID-19 and emphasise the need to promote the personal and public advantages of smoking cessation during the COVID-19 pandemic.

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s crop losses, which primarily occur because of competition with crops. Accurate and rapid weed detection in agricultural fields was a difficult task because of the presence of a wide range of weed species at various densities and growth phases. Presently, several smart agriculture tasks, such as weed detection, plant disease detection, species identification, water and soil conservation, and crop yield prediction, can be realized by using technology. In this article, we propose a Modified Barnacles Mating Optimization with Deep Learning based weed detection (MBMODL-WD) technique. The MBMODL-WD technique aims to automatically identify the weeds in the agricultural field. Primarily, the presented MBMODL-WD technique uses the Gabor filtering (GF) technique for the noise removal process. For automated weed detection, the presented MBMODL-WD technique uses the DenseNet-121 model as feature extraction with the MBMO algorithm as hyperparameter optimization. The design of the MBMO algorithm involves the integration of self-population-based initialization with the standard BMO algorithm. At last, the Elman Neural Network (ENN) method was applied for the weed classification process. To demonstrate the enhanced performance of the MBMODL-WD approach, a series of simulation analyses were performed. A comprehensive set of simulations highlighted the enhanced performance of the presented MBMODL-WD methodology over other DL models with a maximum accuracy of 98.99%.

Abstract Background Patients with chronic obstructive pulmonary disease (COPD) have elevated cardiovascular risk, and cardiovascular disease is a major cause of death in COPD. The current literature indicates that changes in cardiovascular risk during pulmonary rehabilitation (assessed using aortic stiffness) are heterogeneous suggesting that there may be sub-groups of patients who do and do not benefit. Objectives To investigate the characteristics of COPD patients who do and do not experience aortic stiffness reduction during pulmonary rehabilitation, examine how changes relate to physical activity and exercise capacity, and assess whether changes in aortic stiffness are maintained at 6 weeks following rehabilitation. Methods We prospectively measured arterial stiffness (aortic pulse-wave velocity), exercise capacity (Incremental Shuttle Walk Test) and physical activity (daily step count) in 92 COPD patients who started a six week pulmonary rehabilitation programme, 54 of whom completed rehabilitation, and 29 of whom were re-assessed six weeks later. Results Whilst on average there was no influence of pulmonary rehabilitation on aortic stiffness (pre- vs. post pulse-wave velocity 11.3 vs. 11.1 m/s p = 0.34), 56% patients responded with a significant reduction in aortic stiffness. Change in aortic stiffness (absolute and/or percentage) during rehabilitation was associated with both increased physical activity (rho = − 0.30, p = 0.042) and change in exercise capacity (rho = − 0.32, p = 0.02), but in multivariable analysis most closely with physical activity. 92% of the responders who attended maintained this response six weeks later. Conclusion Elevated aortic stiffness in COPD is potentially modifiable in a subgroup of patients during pulmonary rehabilitation and is associated with increased physical activity. Trial registration ClinicalTrials.gov Identifier: NCT03003208. Registered 26/12/ 2016.

The increasing demands of several emergent services brought new communication problems to vehicular networks (VNs). It is predicted that the transmission system assimilated with unmanned aerial vehicles (UAVs) fulfills the requirement of next-generation vehicular network. Because of its higher flexible mobility, the UAV-aided vehicular network brings transformative and far-reaching benefits with extremely high data rates; considerably improved security and reliability; massive and hyper-fast wireless access; much greener, smarter, and longer 3D communications coverage. The clustering technique in UAV-aided VN is a difficult process because of the limited energy of UAVs, higher mobility, unstable links, and dynamic topology. Therefore, this study introduced an Enhanced Artificial Gorilla Troops Optimizer–based Clustering Protocol for a UAV-Assisted Intelligent Vehicular Network (EAGTOC-UIVN). The goal of the EAGTOC-UIVN technique lies in the clustering of the nodes in UAV-based VN to achieve maximum lifetime and energy efficiency. In the presented EAGTOC-UIVN technique, the EAGTO algorithm was primarily designed by the use of the circle chaotic mapping technique. Moreover, the EAGTOC-UIVN technique computes a fitness function with the inclusion of multiple parameters. To depict the improved performance of the EAGTOC-UIVN technique, a widespread simulation analysis was performed. The comparison study demonstrated the enhancements of the EAGTOC-UIVN technique over other recent approaches.

Unmanned aerial vehicles (UAVs) have significant abilities for automatic detection and mapping of urban surface materials due to their high resolution. It requires a massive quantity of data to understand the ground material properties. In recent days, computer vision based approaches for intelligent transportation systems (ITS) have gained considerable interest among research communities and business people. Road classification using remote sensing images plays a vital role in urban planning. It remains challenging because of scene complexity, fluctuating road structures, and inappropriate illumination circumstances. The design of intelligent models and other machine learning (ML) approaches for road classification has yet to be further explored. In this aspect, this paper presents a metaheuristics optimization with deep autoencoder enabled road classification model (MODAE-RCM). The presented MODAE-RCM technique mainly focuses on the classification of roads into five types, namely wet, ice, rough, dry, and curvy roads. In order to accomplish this, the presented MODAE-RCM technique exploits modified fruit fly optimization (MFFO) with neural architectural search network (NASNet) for feature extraction. In order to classify roads, an interactive search algorithm (ISA) with a DAE model is used. The exploitation of metaheuristic hyperparameter optimizers helps to improve the classification results. The experimental validation of the MODAE-RCM technique was tested by employing a dataset comprising five road types. The simulation analysis highlighted the superior outcomes of the MODAE-RCM approach to other existing techniques.

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches.

Background Pulmonary rehabilitation is a cost-effective management strategy in chronic obstructive pulmonary disease (COPD) which improves exercise performance and health-related quality of life. Nutritional supplementation may counter malnutrition and enhance pulmonary rehabilitation outcomes but rigorous evidence is absent. We aimed to investigate the effect of high-protein supplementation (Fortisip Compact Protein (FCP)) during pulmonary rehabilitation on exercise capacity. Methods This was a double-blind randomised controlled trial comparing FCP (intervention) with PreOp (a carbohydrate control supplement) in COPD patients participating in a pulmonary rehabilitation programme. Participants consumed the supplement twice a day during pulmonary rehabilitation and attended twice-weekly pulmonary rehabilitation sessions, with pre- and post-pulmonary rehabilitation measurements, including the incremental shuttle walk test (ISWT) distance at 6 weeks as the primary outcome. Participants’ experience using supplements was assessed. Results 68 patients were recruited (intervention n=36 and control n=32). The trial was stopped early due to the COVID-19 pandemic. Although statistical significance was not reached, there was the suggestion of a clinically meaningful difference in the ISWT distance at 6 weeks favouring the intervention group (intervention 342±149 m (n=22) versus control 305±148 m (n=22); p=0.1). Individuals who achieved an improvement in the ISWT had a larger mid-thigh circumference at baseline (responders 62±4 cm versus nonresponders 55±6 cm; p=0.006). 79% of the patients were satisfied with the taste and 43% would continue taking the FCP. Conclusions Although the data did not demonstrate a statistically significant difference in the ISWT, high-protein supplementation in COPD during pulmonary rehabilitation may result in a clinically meaningful improvement in exercise capacity and was acceptable to patients. Large, adequately powered studies are justified.

The Internet of Drones (IoD) is greatly developed and promotes many civil applications. However, it can still be prone to several security problems which threaten public safety. The issue of security poses further problems upon linking the IoD to the Internet, as its data stream is exposed to attack. For secure communication between drones, an effective route planning scheme with a major intention of accomplishing security is needed. With this aim, this study develops an enhanced search-and-rescue optimization-enabled secure route planning (ESRO-SRP) scheme for the IoD environment. The presented ESRO-SRP technique mainly aims to derive a set of optimal routes to the destination. In addition, the ESRO-SRP algorithm is derived by the integration of the quasi-oppositional-based learning (QOBL) concept with the conventional SRO algorithm. Moreover, the presented ESRO-SRP technique derived a fitness function encompassing different input parameters such as residual energy, distance, and degree of trust. The experimental validation of the ESRO-SRP technique is carried out under several aspects, and the results demonstrated the enhancements of the ESRO-SRP model over recent approaches. The ESRO-SRP model has provided an increased packet delivery ratio (PDR) of 86%, whereas the BRUe-IoE, ORP-FANET, UAVe-WSN, and TR-UAV Swarm approaches have accomplished a minimal PDR of 79.60%, 73.60%, 67.60%, and 63.20%, respectively.

Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.

Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency-inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.

A smart city is a sustainable and effectual urban center which offers a maximal quality of life to its inhabitants with the optimal management of their resources. Energy management is the most difficult problem in such urban centers because of the difficulty of energy models and their important role. The recent developments of machine learning (ML) and deep learning (DL) models pave the way to design effective energy management schemes. In this respect, this study introduces an artificial jellyfish optimization with deep learning-driven decision support system (AJODL-DSSEM) model for energy management in smart cities. The proposed AJODL-DSSEM model predicts the energy in the smart city environment. To do so, the proposed AJODL-DSSEM model primarily performs data preprocessing at the initial stage to normalize the data. Besides, the AJODL-DSSEM model involves the attention-based convolutional neural network-bidirectional long short-term memory (CNN-ABLSTM) model for the prediction of energy. For the hyperparameter tuning of the CNN-ABLSTM model, the AJO algorithm was applied. The experimental validation of the proposed AJODL-DSSEM model was tested using two open-access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the improved outcomes of the AJODL-DSSEM model over recent approaches.

Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model focuses on the generation of effective captions to the input images by using two processes involving encoding unit and decoding unit. Initially, at the encoding part, the salp swarm algorithm (SSA), with a HybridNet model, is utilized to generate effectual input image representation using fixed-length vectors, showing the novelty of the work. Moreover, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model used to generate descriptive sentences. The inclusion of an SSA-based hyperparameter optimizer helps in attaining effectual performance. For inspecting the enhanced performance of the MODLE-AICT model, a series of simulations were carried out, and the results are examined under several aspects. The experimental values suggested the betterment of the MODLE-AICT model over recent approaches.