In vivo analgesic, antipyretic and anti-inflammatory activities of ethanol extract of Pericampylus glaucus in experimental animals

Pericampylus glaucus is a common Malaysian plant used traditionally in the treatments of joint pain, abdominal pain and headache. Hence, the present research was aimed to evaluate ethanolic extract of Pericampylus glaucus for analgesic, antipyretic and anti-inflammatory activities in experimental animals. The central and peripheral analgesic activity was determined by acetic acid induced writhing and hot plate method by examining the number of writhing and paw licking or jumping time. Meanwhile, the antipyretic activity was determined by Brewer’s yeast- test that induced pyrexia and carrageenan- that induced hind paw inflammation was used for anti-inflammatory activities. The ethanolic extract of Pericampylus glaucus at doses (300 and 600 mg/kg b.wt) and Ibuprofen (100 mg/kg (b.wt) was used as a reference drug in the whole experiment. Intraperitoneal administration of Pericampylus glaucus produced significant (p<0.01) inhibition in writhing response in acetic acid induced writhing test and dose-dependent (p<0.001) prolonged paw licking in hot plate test as compared to control (normal saline treated group). Similarly, significant (p<0.001) attenuation in lowering rectal temperature was noted in animal groups that were treated with ethanolic extract of Pericampylus glaucus at different doses. The attenuation was, almost the same as produced by ibuprofen treated group. Furthermore, Pericampylus glaucus extract also produced significant reduction in hind paw edema (p<0.001), 4 h after administration of carrageenan and inhibition was 60.19% and 42.17% as compared to control (normal saline treated group). The findings of this study indicated that Pericampylus glaucus possess significant analgesic, antipyretic and anti-inflammatory activities and could possibly be used in the management of fever, pain and inflammation

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential for maternal and infant health, since maternal and infant health is vital for a healthy society. Over the last few years, researchers have delved into sensing and artificially intelligent healthcare systems for maternal and infant health. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. Since these healthcare systems deal with large amounts of data, significant development is also noted in the computing platforms. The relevant literature reports the potential impact of ICT-enabled systems for improving maternal and infant health. This article reviews wearable sensors and AI algorithms based on existing systems designed to predict the risk factors during and after pregnancy for both mothers and infants. This review covers sensors and AI algorithms used in these systems and analyzes each approach with its features, outcomes, and novel aspects in chronological order. It also includes discussion on datasets used and extends challenges as well as future work directions for researchers

Lessons from the development process of the Afghanistan integrated package of essential health services

In 2017, in the middle of the armed conflict with the Taliban, the Ministry of Public Health decided that the Afghan health system needed a well-defined priority package of health services taking into account the increasing burden of non-communicable diseases and injuries and benefiting from the latest evidence published by DCP3. This leads to a 2-year process involving data analysis, modelling and national consultations, which produce this Integrated Package of Essential health Services (IPEHS). The IPEHS was finalised just before the takeover by the Taliban and could not be implemented. The Afghanistan experience has highlighted the need to address not only the content of a more comprehensive benefit package, but also its implementation and financing. The IPEHS could be used as a basis to help professionals and the new authorities to define their priorities

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking

Protecting essential health services in low-income and middle-income countries and humanitarian settings while responding to the COVID-19 pandemic.

In health outcomes terms, the poorest countries stand to lose the most from these disruptions. In this paper, we make the case for a rational approach to public sector health spending and decision making during and in the early recovery phase of the COVID-19 pandemic. Based on ethics and equity principles, it is crucial to ensure that patients not infected by COVID-19 continue to get access to healthcare and that the services they need continue to be resourced. We present a list of 120 essential non-COVID-19 health interventions that were adapted from the model health benefit packages developed by the Disease Control Priorities project

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure &lt; 100 mmHg (n = 1127), estimated glomerular filtration rate &lt; 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570