A population-base survey on knowledge, attitude and awareness of the general public on antibiotic use and resistance

Objectives: This study was designed to assess the awareness and knowledge of antibiotic usage and antibiotic resistance among the general public in the Cape Coast metropolis of Ghana. It also tries to decipher whether the level of education and the professional status of an individual has a positive association with the level of knowledge on antibiotic resistance. Methods: A population-base survey involving members of the public was conducted from August to November 2019. A structured questionnaire was developed to collect data from 632 respondents. Data were analyzed through SPSS v.21 using Chi square statistics and multivariate regression. Differences in knowledge were evaluated using ANOVA and the assumption of equal variance was tested with Levene statistics. Results: The response rate was 74.3%. Lower educational status group had a greater knowledge level (39.7%) on antibiotic resistance. Despite the high score, the lowest educational status group, (M = 1.82, SD = 0.769), middle educational status group (M = 1.98, SD = 0.748), and the high educational status group (M = 1.88, SD = 0.773) were not significantly different from each other with regard to their general knowledge level on antibiotic resistance (P < 0.05). The study revealed that, working in the healthcare sector is a major contributor to the level of knowledge on antibiotic resistance. Conclusion: Given the scale of the issue on antibiotic resistance and the fact that attempts to resolve it will involve efforts on the part of all, it is important that the public is aware of the importance of the issue of antibiotic resistance, its implications and what they can do to address it. The level of knowledge among respondents with lower educational status should be enough evidence to introduce more educational campaigns on antibiotic resistance

Machine learning-assisted prediction of pneumonia based on non-invasive measures

BackgroundPneumonia is an infection of the lungs that is characterized by high morbidity and mortality. The use of machine learning systems to detect respiratory diseases via non-invasive measures such as physical and laboratory parameters is gaining momentum and has been proposed to decrease diagnostic uncertainty associated with bacterial pneumonia. Herein, this study conducted several experiments using eight machine learning models to predict pneumonia based on biomarkers, laboratory parameters, and physical features.MethodsWe perform machine-learning analysis on 535 different patients, each with 45 features. Data normalization to rescale all real-valued features was performed. Since it is a binary problem, we categorized each patient into one class at a time. We designed three experiments to evaluate the models: (1) feature selection techniques to select appropriate features for the models, (2) experiments on the imbalanced original dataset, and (3) experiments on the SMOTE data. We then compared eight machine learning models to evaluate their effectiveness in predicting pneumoniaResultsBiomarkers such as C-reactive protein and procalcitonin demonstrated the most significant discriminating power. Ensemble machine learning models such as RF (accuracy = 92.0%, precision = 91.3%, recall = 96.0%, f1-Score = 93.6%) and XGBoost (accuracy = 90.8%, precision = 92.6%, recall = 92.3%, f1-score = 92.4%) achieved the highest performance accuracy on the original dataset with AUCs of 0.96 and 0.97, respectively. On the SMOTE dataset, RF and XGBoost achieved the highest prediction results with f1-scores of 92.0 and 91.2%, respectively. Also, AUC of 0.97 was achieved for both RF and XGBoost models.ConclusionsOur models showed that in the diagnosis of pneumonia, individual clinical history, laboratory indicators, and symptoms do not have adequate discriminatory power. We can also conclude that the ensemble ML models performed better in this study

Microbial infections as potential risk factors for lung cancer: Investigating the role of human papillomavirus and chlamydia pneumoniae

Background: Lung cancer is the leading cause of cancer morbidity and mortality worldwide. Apart from tobacco smoke and dietary factors, microbial infections have been reported as the third leading cause of cancers globally. Deciphering the association between microbiome and lung cancer will provide potential biomarkers and novel insight in lung cancer progression. In this current study, we performed a meta-analysis to decipher the possible association between C. pneumoniae and human papillomavirus (HPV) and the risk of lung cancer. Methods: Literature search was conducted in most English and Chinese databases. Data were analyzed using CMA v.3.0 and RevMan v.5.3 software (Cochrane-Mantel-Haenszel method) by random-effects (DerSimonian and Laird) model. Results: The overall pooled estimates for HPV studies revealed that HPV infections in patients with lung cancer were significantly higher than those in the control group (OR = 2.33, 95% CI = 1.57–3.37, p < 0.001). Base on subgroup analysis, HPV infection rate was significantly higher in Asians (OR = 6.38, 95% CI = 2.33–17.46, p < 0.001), in tissues (OR = 5.04, 95% CI = 2.27–11.19, p < 0.001) and blood samples (OR = 1.40, 95% CI = 1.02–1.93, p = 0.04) of lung cancer patients but non-significantly lower in males (OR = 0.84, 95% CI = 0.57–1.22, p =0.35) and among lung cancer patients at clinical stage I-II (OR = 0.95, 95% CI = 0.61–1.49, p = 0.82). The overall pooled estimates from C. pneumoniae studies revealed that C. pneumoniae infection is a risk factor among lung cancer patients who are IgA seropositive (OR = 1.88, 95% CI = 1.30–2.70, p < 0.001) and IgG seropositive (OR = 1.50, 95% CI = 1.10–2.04, p = 0.010). All seronegative IgA (OR = 0.69, 95% CI = 0.42–1.16, p = 0.16) and IgG (OR = 0.66, 95% CI = 0.42–105, p = 0.08) titers are not associative risk factors to lung cancer. Conclusions: Immunoglobulin (IgA) and IgG seropositive titers of C. pneumoniae and lungs infected with HPV types 16 and 18 are potential risk factors associated with lung cancer

LSD1 as a Biomarker and the Outcome of Its Inhibitors in the Clinical Trial: The Therapy Opportunity in Tumor

Tumors are the foremost cause of death worldwide. As a result of that, there has been a significant enhancement in the investigation, treatment methods, and good maintenance practices on cancer. However, the sensitivity and specificity of a lot of tumor biomarkers are not adequate. Hence, it is of inordinate significance to ascertain novel biomarkers to forecast the prognosis and therapy targets for tumors. This review characterized LSD1 as a biomarker in different tumors. LSD1 inhibitors in clinical trials were also discussed. The recent pattern advocates that LSD1 is engaged at sauce chromatin zones linking with complexes of multi-protein having an exact DNA-binding transcription factor, establishing LSD1 as a favorable epigenetic target, and also gives a large selection of therapeutic targets to treat different tumors. This review sturdily backing the oncogenic probable of LSD1 in different tumors indicated that LSD1 levels can be used to monitor and identify different tumors and can be a useful biomarker of progression and fair diagnosis in tumor patients. The clinical trials showed that inhibitors of LSD1 have growing evidence of clinical efficacy which is very encouraging and promising. However, for some of the inhibitors such as GSK2879552, though selective, potent, and effective, its disease control was poor as the rate of adverse events (AEs) was high in tumor patients causing clinical trial termination, and continuation could not be supported by the risk-benefit profile. Therefore, we propose that, to attain excellent clinical results of inhibitors of LSD1, much attention is required in designing appropriate dosing regimens, developing in-depth in vitro/in vivo mechanistic works of LSD1 inhibitors, and developing inhibitors of LSD1 that are reversible, safe, potent, and selective which may offer safer profiles

Comprehensive effects of lifestyle reform, adherence, and related factors on hypertension control: A review

Abstract Despite the effectiveness of currently available antihypertensive medications, there is still a need for new treatment strategies that are more effective in certain groups of hypertensive and for additional resources to combat hypertension. However, medication non‐adherence was previously recognized as a major problem in the treatment of hypertension. The mechanisms behind the positive impacts of lifestyle changes might occur in different ways. In comparison with other studies, the efficacy and effectiveness of lifestyle modifications and antihypertensive pharmaceutical treatment for the prevention and control of hypertension and concomitant cardiovascular disease have been demonstrated in randomized controlled trials. However, in this review, the attitudinal lifestyle modifications and barriers to blood pressure control were elaborated on. An effective method for reducing blood pressure (BP) and preventing cardiovascular events with antihypertensive medications has been outlined. Maintaining healthy lifestyle factors (body mass index, diet, smoking, alcohol consumption, sodium excretion, and sedentary behavior) could lower systolic blood pressure BP by 3.5 mm Hg and reduce the risk of cardiovascular disease (CVD) by about 30%, regardless of genetic susceptibility to hypertension. Conducting a lifestyle intervention using health education could improve lifestyle factors, such as reducing salt, sodium, and fat intake, changing eating habits to include more fruits and vegetables, not smoking, consuming less alcohol, exercising regularly, maintaining healthy body weight, and minimizing stressful conditions. Each behavior could affect BP by modulating visceral fat accumulation, insulin resistance, the renin‐angiotensin‐aldosterone system, vascular endothelial function, oxidative stress, inflammation, and autonomic function. Evidence of the joint effect of antihypertensive medications and lifestyle reforms suggests a pathway to reduce hypertension