Search

Introduction: Obesity is a common comorbidity in patients with asthma and has a significant impact on health and prognoses. However, the extent to which overweight and obesity impact asthma, particularly lung function, remains unclear. This study aimed to report on the prevalence of overweight and obesity and assess their impacts on spirometry parameters in asthmatic patients. Methods: In this multicentre, retrospective study, we reviewed the demographic data and spirometry results of all adult patients with confirmed diagnoses of asthma who visited the studied hospitals’ pulmonary clinics between January 2016 and October 2022. Results: In total, 684 patients with confirmed diagnoses of asthma were included in the final analysis, of whom 74% were female, with a mean ± SD age of 47 ± 16 years. The prevalence of overweight and obesity among patients with asthma was 31.1% and 46.0%, respectively. There was a significant decline in spirometry results in obese patients with asthma compared with patients with healthy weights. Furthermore, body mass index (BMI) was negatively correlated with forced vital capacity (FVC) (L), forced expiratory volume in one second (FEV1), forced expiratory flow at 25–75% (FEF 25–75%) L/s and peak expiratory flow (PEF) L/s (r = −0.22, p < 0.001; r = −0.17, p < 0.001; r = −0.15, p < 0.001; r = −0.12, p < 0.01, respectively). Following adjustments for confounders, a higher BMI was independently associated with lower FVC (B −0.02 [95% CI −0.028, −0.01, p < 0.001] and lower FEV1 (B −0.01 [95% CI −0.01, −0.001, p < 0.05]. Conclusions: Overweight and obesity are highly prevalent in asthma patients, and more importantly, they can reduce lung function, characterised mainly by reduced FEV1 and FVC. These observations highlight the importance of implementing a nonpharmacological approach (i.e., weight loss) as part of the treatment plan for patients with asthma to improve lung function.

The human papillomavirus (HPV) is a crucial but not the predominant cause of cervical cancer. This study aimed to identify HPV-16 L1 gene expression in human papillomavirus using a pap smear. A total of 120 serum samples, 60 samples were taken from infected females with papillomavirus and another 60 as healthy control. These samples were collected after pap smears were done. These women attended Al-Emam Hospital for delivery from March 1st, 2021, to February 28th, 2022. The levels of Pap-IgM and Pap-IgG were increasing among patients attacked by papilloma. The levels of viruses were higher than in levels than control groups, which was indicated by increases in the scores of mean and standard deviation (2.01±1.17, 0.11±0.02), (14.24±7.10, 0.4±0.17), respectively. Statistically, these differences between the levels of the studied groups were highly significant. The levels of the three markers Ca19.9, Ca125, and Ca15.3 were normal in levels among papilloma patients and the control group compared to the normal value of the three markers, which equaled N.V. (>37ng/ml). Statistically, these differences between the scores of the three markers, which were measured depending on mean and standard deviation, were highly significant. There is a low positive correlation between the levels of Pap-IgM (>1) with levels of Ca19.9 (>37) with (r=0.409**, P=0.000), while there is a moderate association between the levels of Pap-IgM (>1) with Ca125 (>35ng/ml) and Ca15.3 (>37ng/ml) levels with (r=0.574**, 0.565**, P=0.000, 0.000) respectively. Also, this table documents that there is a moderate positive correlation between the levels of Pap-IgG (>1) and the levels of the three tumor markers Ca19.9 (>37), Ca125 (>35), and Ca15.3 (>37) (r=0.521**, 0.592**, 0.647**). The HPV-16 L1 gene expression was investigated in patients infected with Papillomaviruses compared to healthy controls using real-time PCR. The results showed a high Ct value for patients and controls with a high Ct value of templates, preoperational to the gene concentration.

Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than before because of extreme climatic changes. Machine learning (ML) approaches can forecast rainfall by deriving hidden patterns from historic meteorological datasets. Selecting a suitable classification method for forecasting has become a tough job. This article introduces the Fuzzy Cognitive Maps with a Metaheuristics-based Rainfall Prediction System (FCMM-RPS) technique. The intention of the FCMM-RPS technique is to predict rainfall automatically and efficiently. To accomplish this, the presented FCMM-RPS technique primarily pre-processes the rainfall data to make it compatible. In addition, the presented FCMM-RPS technique predicts rainfall using the FCM model. To enhance the rainfall prediction outcomes of the FCM model, the parameter optimization process is performed using a modified butterfly optimization algorithm (MBOA). The performance assessment of the FCMM-RPS technique is tested on a rainfall dataset. A widespread comparison study highlights the improvements of the FCMM-RPS technique in the rainfall forecasting process compared to existing techniques with a maximum accuracy of 94.22%.

Recently, artificial intelligence (AI)-enabled technologies have been widely employed for complex industrial applications. AI technologies can be utilized to improve efficiency and reduce human labor in industrial applications. At the same time, fault diagnosis (FD) and detection in rotating machinery (RM) becomes a hot research field to assure safety and product quality. Numerous studies based on statistical, machine learning (ML), and mathematical models have been available in the literature for automated fault diagnosis. From this perspective, this study presents a novel sandpiper optimization with an artificial-intelligence-enabled fault diagnosis (SPOAI-FD) technique for intelligent industrial applications. The aim is to detect the existence of faults in machineries. The proposed model involves the design of a continuous wavelet transform (CWT)-based pre-processing approach, which transforms the raw vibration signal into a useful format. In addition, a bidirectional long short-term memory (BLSTM) model is applied as a classifier, and the Faster SqueezeNet model is applied as a feature extractor. In order to modify the hyperparameter values of the BLSTM model, the sandpiper optimization algorithm (SPOA) can be utilized, showing the novelty of the work. A wide range of simulation analyses were conducted on benchmark datasets, and the results highlighted the supremacy of the SPOAI-FD algorithm over recent approaches.

Jaber S Alqahtani,1,2 Yousef S Aldabayan,3 Abdulelah M Aldhahir,4 Ahmad M Al Rajeh,3 Swapna Mandal,1,5,&ast; John R Hurst1,5,&ast; 1UCL Respiratory, University College London, London, UK; 2Department of Respiratory Care, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia; 3Respiratory Care Department, College of Applied Medical Sciences, King Faisal University, Al-Hasa, Saudi Arabia; 4Respiratory Care Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia; 5Respiratory Medicine, Royal Free London NHS Foundation Trust, London, UK&ast;These authors contributed equally to this workCorrespondence: Jaber S AlqahtaniUCL Respiratory, University College London, Rowland Hill Street, London, NW3 2PF, UKEmail Alqahtani-Jaber@hotmail.comBackground: Readmission following COPD exacerbation is a common and challenging clinical problem. New approaches to predicting readmissions are required to help mitigate risk and develop novel interventions.Methods: We conducted a prospective cohort study in 82 COPD patients admitted due to an exacerbation of COPD. Lung function measures [spirometry, forced oscillation technique (FOT) indices and peak inspiratory flow rate (PIFR)], inflammatory biomarkers and patient-reported outcomes including previous exacerbation history, breathlessness, quality of life and frailty were measured at admission and discharge. We prospectively followed patients for 30 and 90 days to identify predictors for readmission.Results: The readmission rate within 30 days was 38%, and 56% within 90 days. Previous exacerbations, higher COPD Assessment Test score at discharge, frailty, reduced PIFR and increased length of stay were significantly associated with 30-day readmission. PIFR at discharge and frailty had the highest predictive ability for 30-day readmission using area under receiver operating characteristic curves (AUC 0.86, 95% CI 0.78– 0.95, p < 0.001 and AUC 0.81, 95% CI 0.71– 0.90, p < 0.001, respectively). Ninety-day readmissions were significantly associated with previous exacerbations and hospitalisations, higher CAT score at discharge, frailty, depression, lower PIFR and greater expiratory flow limitation (EFL) in the supine position. The best predictive variable in multivariable analysis for both 30- and 90-day readmission was PIFR at discharge.Conclusion: PIFR, CAT score, frailty, and EFL were found to be associated with 30- and 90-day readmission following COPD exacerbation. These findings help identify those at highest risk and to optimise care prior to discharge.Keywords: COPD, exacerbation, admission, readmission, predictor, risk factor, PIFR, frailty, EFL