Evaluation of the carotid artery using wavelet-based analysis of the pulse wave signal

<p>The use of pulse wave analysis may assist cardiologists in diagnosing patients with vascular diseases. However, it is not common in clinical practice to interpret and analyze pulse wave data and utilize them to detect the abnormalities of the signal. This paper presents a novel approach to the clinical application of pulse waveform analysis using the wavelet technique by decomposing the normal and pathology signal into many levels. The discrete wavelet transform (DWT) decomposes the carotid arterial pulse wave (CAPW) signal, and the continuous wavelet transform (CWT) creates images of the decomposed signal. The wavelet analysis technique in this work aims to strengthen the medical benefits of the pulse wave. The obtained results show a clear difference between the signal and the images of the arterial pathologies in comparison with normal ones. The certain distinct that were achieved are promising but further improvement may be required in the future.</p>

Clinical Trials, Potential Mechanisms, and Adverse Effects of Arnica as an Adjunct Medication for Pain Management

Arnica has traditionally been used in treating numerous medical conditions, including inflammation and pain. This review aims to summarize the results of studies testing Arnica products for pain management under different conditions, including post-operation, arthritis, low back pain, and other types of musculoskeletal pain. Based on data from clinical trials, Arnica extract or gel/cream containing Arnica extract shows promising effects for pain relief. These medical benefits of Arnica may be attributed to its chemical components, with demonstrated anti-inflammatory, antioxidant, anti-microbial, and other biological activities. In conclusion, Arnica could be an adjunct therapeutical approach for acute and chronic pain management.

The Newest Battlefield Opioid, Sublingual Sufentanil: A Proposal to Refine Opioid Usage in the U.S. Military

ABSTRACT There is an ongoing opioid epidemic in the USA, and the U.S. military is not immune to the health threat. To combat the epidemic, the Department of Defense (DoD) and Department of Veterans’ Affairs (DVA) issued new clinical practice guidelines and launched the Opioid Safety Initiative aimed at reducing opioid prescriptions. Furthermore, the DoD continually refined opioid protocols for acute pain on the battlefield, evolving from intramuscular morphine to intravenous morphine administration to oral transmucosal fentanyl citrate lollipops (Actiq) to finally sublingual sufentanil tablets (SSTs, Dsuvia). Interestingly, the newest introduction of SSTs into the military sparked great controversy, as there are concerns over the drug’s potential for misuse. However, although the opioid crisis may understandably foster an aversion to new candidate opioids, the therapeutic benefits of effective opioids in acute trauma settings should not be overlooked. SSTs may offer an improved analgesic option to meet the battlefield’s unmet needs with its non-invasive, sublingual delivery system and favorable pharmacologic properties that mitigate the risk for side effects, addiction, and adverse outcomes. Accordingly, this commentary aims to (1) review the evolution of opioid use on the battlefield and discuss the medical benefits and limitations of SSTs in acute trauma settings, (2) highlight the importance of chronic pain management post-deployment through evidence-based non-opioid modalities, and (3) explore avenues of future research. Ultimately, we propose that SSTs are an important improvement from existing battlefield opioids and that refining, not abandoning, opioid usage will be key to effectively managing pain in the military.

Me and My Baby Are One; The Mother's Experience of Continuous Kangaroo Care: A Qualitative Study

Introduction: Infant hospitalization in Neonatal Intensive Care Unit (NICU) causes the separation of mother from her infant. Kangaroo Care (KC) is a bio-care method for preterm newborns. This study was conducted with the aim of investigating the experiences of mothers that could be helpful for the further development and expansion of continuous kangaroo mother care (C-KMC). Material and Method: This is a qualitative study with a content analysis approach. In order to collect data, in-depth individual interviews were conducted with thirteen mothers who were able to care their infants in the form of C-KMC. The sampling was of purposeful type; the interviews were recorded and their contents were written accurately and word by word. Findings: Analysis of mothers' experiences led to the emergence of three main themes of mother’s positive attitude, facilitator factors and barriers to perform C-KMC. Conclusion: The results of this study showed that performing KMC caused a sense of calm, empowerment and satisfaction of the mother and had many medical benefits for the infant. On the other hand, physicians and nurses can act as facilitators or barriers based on the support they make from mothers. Also, the support of the spouse and family plays an important role in continuing to perform the continuous KMC by the mother.

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

Medicalisation and the Treatment-Enhancement Distinction in the Ethical Debate on Gene Editing

"Since the advent of CRISPR/Cas9 gene editing technology, much bioethical effort has been devoted to prescribing the appropriate potential uses of gene editing in humans. Frequently in the literature, a normative distinction is drawn between “treatment” and “enhancement”. That is, gene editing may be morally acceptable or even morally required if used to cure a disease or genetic condition. For enhancement, however, it is morally unacceptable, having too weak a justification for the risks involved. In the context of this new technology, we all thus become vulnerable to a bias: medicalisation. There are clear non-medical benefits, as I show here, of using gene editing not for treatment, but for enhancement. Many individuals and governments will wish to pursue these benefits, but if we are ethically constrained by the current perceived force of the treatment-enhancement distinction, we may be prevented from legitimately doing so. We are faced with two options: firstly, to reject the distinction presented by many ethicists, and pursue gene editing for both treatment and enhancement purposes; secondly, to expand medical definitions and the scope of health care, to include the sort of benefits that we might wish were included under “treatment”. The first option, I argue, is to be preferred, but at least currently, faces much public resistance. Instead, we risk the second option becoming the norm, with the medicalisation of scores of non-medical characteristics drawing resources, causing anxiety, and burdening health care systems, because of stubborn adherence to an arbitrary distinction in the gene editing debate. "

COVID-19 Vaccine Hesitancy in Eight European Countries: Prevalence, Determinants and Heterogeneity

Vaccine hesitancy poses a major obstacle to containing COVID-19. Previous experimental studies of communication strategies for promoting COVID-19 vaccine uptake have been conducted in a single country each, often testing strategies that have differed from those studied in other countries. On the few occasions when two or more single-country studies have tested similar treatments, they have yielded inconsistent findings. For example, highlighting pro-social benefits increased participants’ willingness to get vaccinated in the UK and the US, but had no effect in France and the UK, thus calling into question the often implied generalisability of previous findings. We experimentally assess the effectiveness of different information treatments across eight European countries and examine heterogeneity in the willingness to get vaccinated against COVID-19, as well as in the perceptions of the different vaccines available, within and across countries. We reveal striking differences in COVID-19 vaccine hesitancy across countries, ranging from 5.5% of the adult population in Spain to 50.94% in Bulgaria. The main barriers to vaccine acceptance were fears regarding the quality and safety of the vaccines, as well as mistrust in government. Receiving information emphasising (i) COVID-19 risk reduction through vaccination, (ii) non-medical benefits of a vaccination certificate, and (iii) hedonistic benefits significantly increases vaccination willingness in Germany, but only the vaccination certificate message significantly increases willingness in the UK. No information treatment has significant effects in any other country. A machine-learning technique, model-based recursive partitioning, reveals that the effectiveness of some information treatments is highly heterogeneous among subsets of the population, with adverse effects for Spanish, German and Italian participants without active employment. The heterogeneity of vaccine hesitancy and responses to different messages suggests that health authorities should avoid one-size-fits-all messages and instead tailor vaccination campaigns to their specific target populations, with special care to more disadvantaged populations.

A framework for predicting employee health risks using ensemble model

Through the phenomenon of data, big data and data analytics have provided an opportunity to collect, store, process, analyze and visualize an immense amount of information. Healthcare is recognized as one of the most information-intensive sectors. An urge to explore analytics has been sparked by the rapid growth of data within the healthcare sector. Most employers in Malaysia provide medical benefits that are included in the medical insurance plan for their employees. Data collected such as the history of medical claims are stored with the HR (Human Resource) which contributes to the potential of analyzing and recognizing trends within medical claims to better understand the use and overall health of the employee population. Patients with higher risk will generally convert into patients with high costs. Hence, early intervention of these patients will allow employers to potentially minimize costs and plan preventative steps. In predictive analysis, Decision Trees and Regression are typical techniques applied. The proposed framework combines an ensemble technique known as Stacking. As opposed to a single predictive model, an ensemble predictive model would yield better performance and accuracy. The objective of this paper is therefore to review current practices and past research within the healthcare sector while suggesting a practical framework for classification ensemble modeling. Preliminary findings indicated that an ensemble model can produce higher predictive accuracy and performance than a single model.

Evaluation of thermo-chemical conversion temperatures of cannabinoid acids in hemp (Cannabis sativa L.) biomass by pressurized liquid extraction

Background Cannabinoids are increasingly becoming compounds of medical interest. However, cannabis plants only produce carboxylated cannabinoids. In order to access the purported medical benefits of these compounds, the carboxylic acid moiety must be removed. This process is typically performed by heating the plant material or extract; however, cannabinoids being thermolabile can readily degrade, evaporate, or convert to undesired metabolites. Pressurized liquid extraction (PLE) operates using a pseudo-closed system under pressure and temperature. While pressure is maintained at 11 MPa, temperature can be varied from ambient to 200 °C. Methods Temperatures were evaluated (80 to 160 °C) using PLE for the thermo-chemical conversion of cannabinoid acids utilizing water as the solvent in the first step of extraction with subsequent extraction with ethanol. Optimum temperatures were established for the conversion of 6 cannabinoid acids to their neutral cannabinoid forms. Cannabinoid acid conversion was monitored by HPLC. Results The use of PLE for thermo-chemical decarboxylation has resulted in a rapid decarboxylation process taking merely 6 min. The temperatures established here demonstrate statistically significant maxima and minima of cannabinoids and their parent cannabinoid acids. One-way ANOVA analysis shows where individual cannabinoids are statistically different, but the combination of the maxima and minima provides temperatures for optimum thermo-chemical conversion. CBC, CBD, CBDV, and CBG have an optimum temperature of conversion of 140 °C, while THC was 120 °C for 6 min. Discussion Decarboxylation of cannabinoid acids is necessary for conversion to the bioactive neutral form. The pseudo-closed chamber of the PLE makes this an ideal system to rapidly decarboxylate the cannabinoid acids due to pressure and temperature, while minimizing loss typically associated with conventional thermal-decarboxylation. This study established the optimum temperatures for thermo-chemical conversion of the cannabinoid acids in water and provides the groundwork for further development of the technology for industrial scale application.