From fear to empowerment: the impact of employees AI awareness on workplace well-being – a new insight from the JD–R model

PurposeThe primary purpose of the study was to explore the impact of health workers’ awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD–R) model, the study aimed to provide insights into how health workers’ perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study’s findings could inform strategies for supporting healthcare workers during technological transformations.Design/methodology/approachThe study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling.FindingsThe study findings revealed that health workers’ AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers’ workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers’ AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour.Research limitations/implicationsWhile the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables.Practical implicationsThe study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers’ AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives.Originality/valueThe study contributes to the existing literature by addressing a relatively unexplored area – the impact of AI awareness on healthcare workers’ well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers’ perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD–R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations

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

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

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.</jats:p

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

Building a predictive model to assist in the diagnosis of cervical cancer

Aim: Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier detection is the most effective technique for decreasing the worldwide incidence of the illness. Materials and methods: This research presents a novel ensemble technique for predicting cervical cancer risk. Specifically, the authors introduce a voting classifier that aggregates prediction probabilities from multiple machine-learning models: logistic regression, K-nearest neighbor, decision tree, XGBoost and multilayer perceptron. Results: The average accuracy, precision, recall and f1-score of the voting classifier were 96.6, 97.4, 95.9 and 96.6, respectively. Furthermore, the voting algorithm gains average high values for all evaluation metrics (accuracy, precision, recall and f1-score). The f1-score of the algorithm is 96%, which demonstrates the robustness of the model. Conclusion: The findings suggest that the probability of having cervical cancer can be accurately predicted utilizing the voting technique. </jats:p

Polymorphism in the Androgen Biosynthesis Gene (CYP17), a Risk for Prostate Cancer: A Meta-Analysis

Gene polymorphism is one of the few factors that increases the risk of prostate cancer. T to C substitution in the 5’ promoter region of the CYP17 gene is hypothesized to increase the rate of gene transcription, increase androgen production, and thereby increase the risk of prostate cancer. Nevertheless, the inconsistencies originating from studies on CYP17 polymorphism and prostate cancer prompted this meta-analysis, to decipher the association between CYP17 polymorphism and prostate cancer. Most case-control studies addressing CYP17 polymorphism and prostate cancer were exhaustively searched from Web of Science, Google Scholar, and PubMed. The various genotype distributions as well as the minor allele distributions were retrieved. Pooled odds ratios ( ORs) with their 95% CI and estimates of the Hardy–Weinberg Equilibrium were calculated. Analyses were performed using the RevMan v.5.3 software and SPSS v.21. There was high-pooled heterogeneity ( I2 = 87.0%, OR = .42, CI [.39, .45], and p &lt; .001) among the A2 versus A1 allele. With the per-allele model (A2 versus A1), ethnicity was a major risk factor to prostate cancer, with Asians recording the highest risk ( OR = 12.61, 95% CI [8.77, 18.12]). From the genotype models, A1/A1 versus A2/A2 ( OR = 3.02, 95% CI [2.65, 3.44]) and A1/A2 versus A2/A2 ( OR = 4.39, 95% CI [3.86, 5.00]) were all significantly associated with prostate cancer. Although some genotype models were associated with the risk of prostate cancer, we should be mindful when interpreting the results of this study because of the limited number of studies and the small sample size used. </jats:p