Endostatin concentration in plasma of healthy human volunteers

Background: Angiogenesis is involved in many cardiovascular and cancerous diseases, including atherosclerosis and is controlled by a fine balance between angiogenic and angiostatic mediators. Endostatin is one of the main angiostatic mediators, and inhibits angiogenesis and prevents progression of atherosclerosis. The available literature shows a broad range of concentrations in relatively small samples of healthy controls and is calculated by using different techniques. This study was aimed to determine the basal endostatin concentration in plasma of healthy volunteers, to fully understand its physiological role. Methods: Fifty healthy adult volunteers were recruited to the study. Participants were advised not to participate in any physical activity on the day before the blood sampling. The volunteers’ physical activity, height, weight, heart rate and blood pressure were recorded. The samples were analysed for plasma endostatin concentration, using ELISA. The participants were divided by gender and ethnic groups to calculate any difference. Results: Endostatin and other variables were normally distributed. Most of the participants had a moderate level of physical activity with no gender related difference (p=0.370). The mean value for plasma endostatin in all samples was 105±12 ng/ml with range of 81–132 ng/ml. For males, it was 107±13 ng/ml, while for females; 102±12 ng/ml. There were no significant gender or ethnicity related differences in endostatin concentration. Moreover, endostatin was not significantly related with any anthropometric and physical variable. Conclusion: This study gives endostatin levels in normal healthy people and show no gender and ethnicity related differences in endostatin levels. Endostatin was not related with any anthropometric and physical variable

Association of Type D personality with cardiovascular disease and its prognosis

Objective: To evaluate the association of Type D personality with cardiovascular disease and its prognosis. Methodology: This cross sectional study study was conducted in cardiology department of MBBS medical college Mirpur, from February 2019 to February 2020 in a period of one year. A total of 281 patients with cardiovascular diseases were included. Demographic information and risk factors were noted. Screening for metabolic syndrome was done using international diabetes federation criteria based upon central obesity. The assessment of type D personality was made through DS-14, type D scale, which is the most widely used instrument for type D personality measurement. In which all the 14 items are score on a 5-point Likert scale. Results: There were 77 (27.40%) patients having type D personality, with significantly less mean age (45.36 ± 6.2 vs. 53.45 ± 9.6) in comparison to patients without type D personality. No significant (p-value > 0.05) difference was noted in gender, education, occupation and marital status of the patients having type D personality. The rate of diabetes mellitus (44.46% vs. 37.25%), hypertension (59.74% vs. 47.06%), smoking status (62.34% vs. 53.43%) and metabolic syndrome (48.05% vs. 40.69%) were similar in both groups. The mean values of systolic (124.53 ± 12.35 vs. 116.28 ± 14.30, p-value = 0.000) and diastolic (78.44 ± 6.92 vs. 74.62 ± 7.48, p-value = 0.0001) blood pressure were significantly higher in patients having type D personality. Conclusions: A considerable number of cardiac patients in our study had type D personality trait. This trait was more common in younger age and male patients showing raised levels of blood pressure and HDL cholesterol. Key words: Cardiovascular disease, Type D personality, low HDL cholesterol, Prognosi

Propofol Versus Dexmedetomidine Sedation Reduces Delirium

OBJECTIVES Postoperative delirium (POD) is a serious complication after cardiac surgery. Use of dexmedetomidine infusion to prevent delirium is controversial. We hypothesized that dexmedetomidine sedation after cardiac surgery would reduce the incidence of POD.METHODOLOGY After the approval from institutional ethics review board and informed consent, a comparative cross sectional study was conducted in 100 patients scheduled for cardiac surgery. Patients suffering from consequential psychological issues, delirium, and grievous dementia were excluded. Delirium was evaluated by confusion assessment method for ICU (CAM-ICU). Normality and homogenity of data were analyzed using Kolmogorov-Sminorv and saphiro wilk. The factors related to delirum status were analyzed using Logistic Regression.RESULTSThe mean age among propofol group was 55.14+9.6 while among Dexmedetomidine was 55.96+12.1. POD was present in 24 of 50 (48%) and 4 of 50 (8.%) patients in propofol and dexmedetomidine groups, respectively. variables which had significance values <0.05 were patient age (0.000), associated disease (p<-0.003). In regards to other variables like patient gender (p value: 0.660), pre-operative medication (p value: -0.090), different type of surgery (p value: -0.239), had no correlation with POD.CONCLUSIONIn comparison with propofol, dexmedetomidine postoperative sedation minimized the occurrence and abbreviated the time span of POD in patients who had to undergo cardiac surgery

Depression and anxiety in glaucoma patients using mono drug therapy vs polydrug therapy

Objective: To evaluate the frequency of depression, anxiety, and stress scores among glaucoma patients and assess factors attributing to severe scores. Methodology: This Analytical, Cross-sectional study was conducted at the Armed Forces Institute of Ophthalmology from June 2020 to July 2021. Data was collected through nonprobability consecutive sampling. Individuals with diagnosed primary open-angle glaucoma were selected irrespective of age and gender. Dass-21 (self-assessment scale) was used in Urdu to document patient scores for anxiety, depression, and stress. Results: The frequency of males (n=204, 54%) and females (n=173, 45.8%) in the two groups was almost the same (p=0.164). The mean age in the mono-drug group (mean= 48.81±10.58 years) was slightly lower than the polydrug group (mean=53.67±11.16years) (p=0.000018). Overall depression score of the sample fell in the severe category (score=21-27), with individuals showing more depression scores in the poly group (n=99, 26%) than in the mono drug group (n=76, 20%) (p=0.000002). Individuals on polydrug therapy showed severe scores for depression, anxiety, and stress. Conclusion: Among the glaucoma patient, those on polydrug therapy have higher incidences of anxiety among young patients and depression among older patients. This not only causes poor compliance to treatment but also increases the risk of progression of glaucoma hence augmenting the crippling effects of the disease. Keywords: Depression, anxiety, DASS-21, Glaucom

Current state of knowledge of basic life support in health professionals of the largest city in Pakistan: A cross-sectional study

Background: Basic Life Support (BLS) is the recognition of sudden cardiac arrest and activation of the emergency response system, followed by resuscitation, and rapid defibrillation. According to WHO, Pakistan has one of the highest mortality rates from accidental deaths therefore assessment and comparison of BLS knowledge in health professionals is crucial. We thereby aim to assess and compare the knowledge of BLS in doctors, dentists and nurses. Methods: A multi-centric cross-sectional survey was conducted in Karachi at different institutions belonging to the private as well as government sector from January to March 2018. We used a structured questionnaire which was adapted from pretested questionnaires that have been used previously in similar studies. Descriptive statistics were analyzed using SPSS v22.0, where adequate knowledge was taken as a score of at least 50%. P \u3c 0.05 was considered as significant. Logistic regression was used to identify the factors affecting the knowledge regarding BLS in health care professionals. Results: The responders consisted of 140 doctors, nurses and dentists each. Only one individual (dentist) received a full score of 100%. In total, 58.3% of the population had inadequate knowledge. Average scores of doctors, dentists and nurses were 53.5, 43.3 and 38.4% respectively. Doctors, participants with prior training in BLS and those with 6 to 10 years after graduation were found to be a significant predictor of adequate knowledge, on multivariate analysis. Conclusion: Even though knowledge of BLS in doctors is better than that of dentists and nurses, overall knowledge of health care professionals is extremely poor. Present study highlights the need for a structured training of BLS for health care workers

Quality of life in individuals surgically treated for congenital hydrocephalus during infancy: A single-institution experience

Background: Congenital hydrocephalus (CH) is a frequently encountered birth anomaly that can hinder long-term neurologic maturity and social well-being of affected children. This study was undertaken to assess quality of life (QOL) 10-15 years after surgical treatment for primary CH during infancy at a tertiary care hospital in a developing country.Methods: This retrospective cohort study included individuals who presented to Aga Khan University Hospital, Karachi, Pakistan, between 1995 and 2005 at QOL.Results: Of 118 patients, 90 patients participated in the study. Mean age at first admission was 6.2 months. Mean length of follow-up was 5.4 years. Of these, 28 patients had died after surgery. Shunt infection (P = 0.012) and delayed milestones (P = 0.003) were found to be statistically significant factors affecting mortality in the patients who died. The mean overall health score was 0.67 ± 0.30. Age (P = 0.039).Conclusions: In our analysis, we assessed the QOL associated with CH. We hope that these results will provide insight for future prospective work with the ultimate goal of improving long-term QOL in children with CH

Kaempferol as a dietary anti-inflammatory agent: current therapeutic standing

Inflammation is a physiological response to different pathological, cellular or vascular damages due to physical, chemical or mechanical trauma. It is characterized by pain, redness, heat and swelling. Current natural drugs are carefully chosen as a novel therapeutic strategy for the management of inflammatory diseases. Different phytochemical constituents are present in natural products. These phytochemicals have high efficacy both in vivo and in vitro. Among them, flavonoids occur in many foods, vegetables and herbal medicines and are considered as the most active constituent, having the ability to attenuate inflammation. Kaempferol is a polyphenol that is richly found in fruits, vegetables and herbal medicines. It is also found in plant-derived beverages. Kaempferol is used in the management of various ailments but there is no available review article that can summarize all the natural sources and biological activities specifically focusing on the anti-inflammatory effect of kaempferol. Therefore, this article is aimed at providing a brief updated review of the literature regarding the anti-inflammatory effect of kaempferol and its possible molecular mechanisms of action. Furthermore, the review provides the available updated literature regarding the natural sources, chemistry, biosynthesis, oral absorption, metabolism, bioavailability and therapeutic effect of kaempferol

Compatibility and challenges in machine learning approach for structural crack assessment

Structural health monitoring and assessment (SHMA) is exceptionally essential for preserving and sustaining any mechanical structure’s service life. A successful assessment should provide reliable and resolute information to maintain the continuous performance of the structure. This information can effectively determine crack progression and its overall impact on the structural operation. However, the available sensing techniques and methods for performing SHMA generate raw measurements that require significant data processing before making any valuable predictions. Machine learning (ML) algorithms (supervised and unsupervised learning) have been extensively used for such data processing. These algorithms extract damage-sensitive features from the raw data to identify structural conditions and performance. As per the available published literature, the extraction of these features has been quite random and used by academic researchers without a suitability justification. In this paper, a comprehensive literature review is performed to emphasise the influence of damage-sensitive features on ML algorithms. The selection and suitability of these features are critically reviewed while processing raw data obtained from different materials (metals, composites and polymers). It has been found that an accurate crack prediction is only possible if the selection of damage-sensitive features and ML algorithms is performed based on available raw data and structure material type. This paper also highlights the current challenges and limitations during the mentioned sections

Suitability analysis of machine learning algorithms for crack growth prediction based on dynamic response data

Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model’s predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions