Bacteria-derived extracellular vesicles: endogenous roles, therapeutic potentials and their biomimetics for the treatment and prevention of sepsis

Sepsis is one of the medical conditions with a high mortality rate and lacks specific treatment despite several years of extensive research. Bacterial extracellular vesicles (bEVs) are emerging as a focal target in the pathophysiology and treatment of sepsis. Extracellular vesicles (EVs) derived from pathogenic microorganisms carry pathogenic factors such as carbohydrates, proteins, lipids, nucleic acids, and virulence factors and are regarded as “long-range weapons” to trigger an inflammatory response. In particular, the small size of bEVs can cross the blood-brain and placental barriers that are difficult for pathogens to cross, deliver pathogenic agents to host cells, activate the host immune system, and possibly accelerate the bacterial infection process and subsequent sepsis. Over the years, research into host-derived EVs has increased, leading to breakthroughs in cancer and sepsis treatments. However, related approaches to the role and use of bacterial-derived EVs are still rare in the treatment of sepsis. Herein, this review looked at the dual nature of bEVs in sepsis by highlighting their inherent functions and emphasizing their therapeutic characteristics and potential. Various biomimetics of bEVs for the treatment and prevention of sepsis have also been reviewed. Finally, the latest progress and various obstacles in the clinical application of bEVs have been highlighted

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

The effect of breakfast on childhood obesity: a systematic review and meta-analysis

ObjectivePrevious cohort trials have shown that skipping breakfast increases the risk of obesity or overweight in children. However, this finding remains controversial. Through a meta-analysis, this study systematically evaluated the effect of skipping breakfast on the prevalence of obesity or overweight in children.MethodsWe performed a literature search for studies published until March 19, 2023. using the Cochrane, PubMed, and Embase databases. Based on the inclusion and exclusion criteria, observational studies on the relationship between skipping breakfast and overweight/obesity in children and adolescents were analyzed. Three investigators independently screened the relevant literature, extracted the data, and assessed the risk of bias. The quality of the included studies was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects model was used. The odds ratio (OR) with its 95% confidence interval (CI) was used to indicate the effect size.ResultsA total of 40 retrospective studies with 323,244 children ranging in age from 2 to 20 years were included in this study. The results of this meta-analysis showed that children and adolescents who skipped breakfast had a significantly higher prevalence of obesity or overweight than those who ate breakfast (OR, 1.59; 95% CI, 1.33–1.90; P &lt; 0.001). Skipping breakfast was positively associated with overweight in children and adolescents (OR, 1.37; 95% CI, 1.23–1.54; P &lt; 0.001). Similarly, skipping breakfast was positively associated with obesity in children and adolescents (OR, 1.51; 95% CI, 1.30–1.76; P &lt; 0.001). The effect was also different by sex, with girls being the most affected (OR, 1.47; 95% CI, 1.23–1.76; P &lt; 0.001). There was also a correlation between skipping breakfast and abdominal obesity in children (OR, 0.65; 95% CI, 0.55–0.77; P &lt; 0.001).ConclusionThis meta-analysis suggested that skipping breakfast is associated with an increased risk of overweight/obesity in children and adolescents. The findings provide support for a possible protective role of breakfast against excessive weight gain in children and adolescents. However, more rigorous study designs with validated and standardized measures of relevant variables are needed

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

Adaptation of the Wound Healing Questionnaire universal-reporter outcome measure for use in global surgery trials (TALON-1 study): mixed-methods study and Rasch analysis

BackgroundThe Bluebelle Wound Healing Questionnaire (WHQ) is a universal-reporter outcome measure developed in the UK for remote detection of surgical-site infection after abdominal surgery. This study aimed to explore cross-cultural equivalence, acceptability, and content validity of the WHQ for use across low- and middle-income countries, and to make recommendations for its adaptation.MethodsThis was a mixed-methods study within a trial (SWAT) embedded in an international randomized trial, conducted according to best practice guidelines, and co-produced with community and patient partners (TALON-1). Structured interviews and focus groups were used to gather data regarding cross-cultural, cross-contextual equivalence of the individual items and scale, and conduct a translatability assessment. Translation was completed into five languages in accordance with Mapi recommendations. Next, data from a prospective cohort (SWAT) were interpreted using Rasch analysis to explore scaling and measurement properties of the WHQ. Finally, qualitative and quantitative data were triangulated using a modified, exploratory, instrumental design model.ResultsIn the qualitative phase, 10 structured interviews and six focus groups took place with a total of 47 investigators across six countries. Themes related to comprehension, response mapping, retrieval, and judgement were identified with rich cross-cultural insights. In the quantitative phase, an exploratory Rasch model was fitted to data from 537 patients (369 excluding extremes). Owing to the number of extreme (floor) values, the overall level of power was low. The single WHQ scale satisfied tests of unidimensionality, indicating validity of the ordinal total WHQ score. There was significant overall model misfit of five items (5, 9, 14, 15, 16) and local dependency in 11 item pairs. The person separation index was estimated as 0.48 suggesting weak discrimination between classes, whereas Cronbach's α was high at 0.86. Triangulation of qualitative data with the Rasch analysis supported recommendations for cross-cultural adaptation of the WHQ items 1 (redness), 3 (clear fluid), 7 (deep wound opening), 10 (pain), 11 (fever), 15 (antibiotics), 16 (debridement), 18 (drainage), and 19 (reoperation). Changes to three item response categories (1, not at all; 2, a little; 3, a lot) were adopted for symptom items 1 to 10, and two categories (0, no; 1, yes) for item 11 (fever).ConclusionThis study made recommendations for cross-cultural adaptation of the WHQ for use in global surgical research and practice, using co-produced mixed-methods data from three continents. Translations are now available for implementation into remote wound assessment pathways