Prevalence and characteristics of resistant hypertensive patients in an Asian population

Background: Resistant hypertension is a well-recognized clinical challenge yet there are no reported data on its prevalence in Pakistan. These patients are subjected to a higher risk of developing hypertensive complications. The objective of our study was to evaluate the prevalence and determinants of resistant hypertension in an Asian cohort of hypertensive patients.Methods: This cross-sectional study was carried out among hypertensive patients visiting a tertiary care hospital in Karachi from September-December 2015. Patient data and characteristics were recorded using a pre-coded questionnaire. Morisky and Berlin questionnaires were used to assess compliance to medications and determine the risk of developing obstructive sleep apnea, respectively. Pearson\u27s chi-square test was used to analyze statistical differences between hypertensive patients and related factors.Results: A total of 515 patients were included in the study. Overall, 12% of the total patients (n=62) were resistant hypertensives and 25% (n=129) had pseudo-resistant hypertension. Resistant patients were more often females, older and had a higher body mass index (all P\u3c0.001). Use of painkillers and noncompliance to dietary recommendations were found to be significant determinants of resistant hypertension. Prevalence of comorbid conditions, including diabetes (p=0.33), hyperlipidemia (p=0.46), and chronic kidney disease (p=0.23), was not significantly higher in patients with resistant hypertension.Conclusion: Nearly one in ten hypertensive patients had true resistant hypertension, and twenty-five percent of patients had pseudo-resistance. Resistance hypertensions is significantly associated with female gender, older age, obesity, dietary noncompliance and increased use of NSAIDs

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.publishedVersio

Elucidating the Potential of Vertical Flow-Constructed Wetlands Vegetated with Different Wetland Plant Species for the Remediation of Chromium-Contaminated Water

Water scarcity is one of the key global challenges affecting food safety, food security, and human health. Constructed wetlands (CWs) provide a sustainable tool to remediate wastewater. Here we explored the potential of vertical flow-CWs (VF-CWs) vegetated with ten indigenous wetland plant species to treat chromium (Cr)-contaminated water. The wetland plants were vegetated to develop VF-CWs to treat Cr-contaminated water in a batch mode. Results revealed that the Cr removal potential of VF-CWs vegetated with different wetland plants ranged from 47% to 92% at low (15 mg L−1) Cr levels and 36% to 92% at high (30 mg L−1) Cr levels, with the maximum (92%) Cr removal exhibited by VF-CWs vegetated with Leptochloa fusca. Hexavalent Cr (Cr(VI)) was reduced to trivalent Cr (Cr(III)) in treated water (96–99 %) of all VF-CWs. All the wetland plants accumulated Cr in the shoot (1.9–34 mg kg−1 dry weight (DW)), although Cr content was higher in the roots (74–698 mg kg−1 DW) than in the shoots. Brachiaria mutica showed the highest Cr accumulation in the roots and shoots (698 and 45 mg kg−1 DW, respectively), followed by Leptochloa fusca. The high Cr level significantly (p < 0.05) decreased the stress tolerance index (STI) percentage of the plant species. Our data provide strong evidence to support the application of VF-CWs vegetated with different indigenous wetland plants as a sustainable Cr-contaminated water treatment technology such as tannery wastewater

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment

Vermicompost application upregulates morpho-physiological and antioxidant defense to conferring drought tolerance in wheat

Drought stress is an ever‐present threat to wheat growth, development, and productivity, especially in arid and semi-arid areas where rainfall is an essential aid to agriculture. The application of vermicompost has been proven to be an efficient approach to combat the drought-induced growth and developmental limitation of wheat plants and to promote cost-effective and sustainable crop production. A wire-house pot experiment was conducted to examine the effects of cow manure vermicompost on morpho-physiological and biochemical attributes of wheat seedlings under different deficit water levels. The treatments were included: three drought levels; control (70 % of field capacity (FC, D0)), mild drought (45 % of FC, D1), and severe drought (30 % of FC, D1), four vermicompost application rates; control, 4, 6 and 8 t ha−1 (designated as VT0, VT1, VT2 and VT3, respectively), and two contrasting wheat cultivars; Faisalabad-08 (drought tolerant) and Galaxy-13 (drought sensitive). Results showed that vermicompost application improved crop performance under control and drought stress conditions where VT2 depicted significantly higher values in both cultivars, particularly under drought stress. Under severe drought, VT2 increased root fresh weight by 6.13 and 10.63 %, shoot fresh weight by 15.62 % and 23.58 %, root dry weight by 40.81 % and 50 % and shoot dry weight by 20.68 and 22.22 % in Faisalabad-08 and Galaxy-13, respectively. Also, VT2-treated seedlings exhibited significantly higher gas exchange attributes, chlorophyll pigments and antioxidative enzyme activities under drought conditions. Among cultivars, Faisalabad-08 was found comparatively more drought tolerant than Galaxy-13. Our findings demonstrated that drought severely affects morphological, physiological, and biochemical attributes of wheat. However, soil applied vermicompost proved beneficial for improved morphophysiological and biochemical traits under the regime of drought stress in wheat

Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019Research in context

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)