Bacillus amyloliquefaciens, a Gram-positive bacterium, has emerged as a versatile microorganism with significant applications in various fields, including industry, medicine, and agriculture. This comprehensive review aims to provide an in-depth understanding of the characteristics, genetic tools, and metabolic capabilities of B. amyloliquefaciens, while highlighting its potential as a chassis cell for synthetic biology, metabolic engineering, and protein expression. We discuss the bacterium’s role in the production of chemicals, enzymes, and other industrial bioproducts, as well as its applications in medicine, such as combating infectious diseases and promoting gut health. In agriculture, B. amyloliquefaciens has demonstrated potential as a biofertilizer, biocontrol agent, and stress tolerance enhancer for various crops. Despite its numerous promising applications, B. amyloliquefaciens remains less studied than its Gram-negative counterpart, Escherichia coli. This review emphasizes the need for further research and development of advanced engineering techniques and genetic editing technologies tailored for B. amyloliquefaciens, ultimately unlocking its full potential in scientific and industrial contexts.

Inflammation is a crucial defense mechanism that protects the body from the devastating effects of invading pathogens. However, an unrestrained inflammatory reaction may result in systemic manifestations with dire consequences to the host. The extent of activation of the inflammatory response is tightly regulated through immunological and neural pathways. Previously, we demonstrated that cholinergic stimulation confers enhanced protection in experimental animals orally infected with virulent Salmonella enterica serovar Typhimurium. In this study, we investigated the mechanism by which this enhanced protection takes place. Cholinergic stimulation was induced by a 3-week pretreatment with paraoxon, a highly specific acetylcholinesterase (AChE) inhibitor. This treatment enhanced host survival following oral-route infection and this correlated with significantly reduced bacterial load in systemic target organs. Enhanced protection was not due to increased gut motility or rapid bacterial clearance from the gastrointestinal tract. Moreover, protection against bacterial infection was not evident when the animals were infected systemically, suggesting that acetylcholine-mediated protective effect was mostly confined to the gut mucosal tissue. In vivo imaging demonstrated a more localized infection and delay in bacterial dissemination into systemic organs in mice pretreated with paraoxon. Morphological analysis of the small intestine (ileum) showed that AChE inhibition induced the degranulation of goblet cells and Paneth cells, two specialized secretory cells involved in innate immunity. Our findings demonstrate a crucial pathway between neural and immune systems that acts at the mucosal interface to protect the host against oral pathogens.

Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a feedforward neural network (FNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM), across four datasets: (i) clinical laboratory biomarkers for diabetes and cardiovascular disease, (ii) patient history and lifestyle factors for diabetes risk, (iii) symptom and ECG data for heart disease, and (iv) syndromic surveillance data for communicable disease. The AUC (area under the receiver operating characteristic curve) was used to assess model performance. The findings show that prediction accuracy is highly context-dependent: laboratory data produced modest accuracy (AUC = 0.62), patient history-based models performed well for diabetes (AUC = 0.84), heart disease prediction from symptoms and ECG performed best (AUC = 0.87), and communicable disease prediction from broad symptom data was poor (AUC = 0.49). This work is unique in that it conducts a systematic comparison analysis of numerous ML models across a variety of clinical data scenarios, offering vital insights into how different data modalities and modelling approaches affect diagnostic accuracy. These findings highlight the significance of dataset selection, context-aware feature design, and rigorous model tuning for deploying effective clinical ML models, especially in resource-constrained healthcare settings. Overall, the study found that prediction success is highly influenced by data type, modelling approach, and clinical context, underlining the importance of tailored, data-specific solutions in a variety of healthcare situations.