Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer's disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Background Mercury, as a global heavy metal pollutant, poses a serious threat to human health. The toxicity of mercury depends on its chemical form. Distinguishing the forms of mercury in the environment is of great significance for mercury management and reducing human mercury exposure risks. Objective To establish a non-targeted metallomics method based on synchrotron radiation X-ray fluorescence (SRXRF) spectroscopy combined with machine learning to screen inorganic mercury (IHg) or methylmercury (MeHg) exposed rice plants. Methods Rice seeds were exposed to ultra-pure water (control group), 0.1 mg·L⁻¹ IHg (IHg group) or MeHg (MeHg group) solutions, respectively. After germination, the seedlings were cultured for 21 d, and rice leaves were collected, dried, weighed, and pressed. The content of metallome in rice leaves was determined by SRXRF. Machine learning models including soft independent modeling cluster analysis (SIMCA), partial least squares discriminant analysis (PLS-DA), and logistic regression (LR) were used to classify the SRXRF full spectra of different groups and find the best model to distinguish rice exposed to IHg or MeHg. Besides, characteristic elements were selected as input parameters to optimize the model by improving computing speed and reducing model calculation. Results The SRXRF spectral intensities of the control group, IHg group, and MeHg group were different, indicating that exposure to IHg and MeHg can interfere the homeostasis of metallome in rice leaves. The results of principal component analysis (PCA) of SRXRF spectra showed that the control group could be well distinguished from the mercury exposed groups, but the IHg group and the MeHg group were mostly overlapped. The accuracy rates of the three models (PLS-DA, SIMCA, and LR) were higher than 98% for the training set, higher than 95% for the validation set, and higher than 94% for the cross-validation set. Besides, the accuracy of the LR model was higher than that of the PLS-DA model and the SIMCA model. Furthermore, the accuracy was 92.05% when using characteristic elements K, Ca, Mn, Fe, and Zn selected by LR to distinguish the IHg group and the MeHg group. Compared with the full spectra model, although the prediction accuracy of the characteristic spectral model decreased, the input parameters of the model decreased by 99.51%, and precision, recall, and F1 score were above 84.48%, indicating that the model could distinguish rice exposed to different mercury forms. Conclusion Non-targeted metallomics method based on SRXRF and machine learning can be applied for high-throughput screening of rice exposed to different forms of mercury and thus decrease the risks of people being exposed to mercury.

Objective:To investigate the distribution of pathogens and resistance to commonly used antimicrobial drugs in patients with skin and soft tissue infection.Methods:It was a cross-sectional study from April 2020 to April 2021, 120 patients with skin and soft tissue infection, including 65 males and 55 females, aged 21 to 64 (44.4±3.2) years. The wound pus or secretion samples were collected for inspection and bacterial isolation and identification and drug susceptibility test to observe the distribution of pathogens and resistance to commonly used antimicrobial drugs.Results:A total of 140 pathogens were isolated from 120 patients with skin and soft tissue infection, including 76 Gram (54.29%) positive, 51 Gram (36.43%) negative bacteria and 13 fungus (9.29%). In terms of bacterial resistance, the three main Gram-positive bacteria, Staphylococcus aureus, Staphylococcus epidermidis and Staphylococcus haemolyticus had high rates of drug resistance: 71.05% Staphylococcus aureus were resistance to ampicillin/sulbactam; 63.16 to erythromycin, and 42.11 to ampicillin/sulbactam; S. epidermidis showed 100% resistant to penicillin, 57.89% to erythromycin, and 47.37% to ampicillin/sulbactam; the resistance of Staphylococcus haemolyticus was 70% to penicillin, 50% to erythromycin, 50% to ampicillin/sulbactam; Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus haemolyticus showed low resistance to nitrofurantoin, linezolid, and vancomycin. S. aureus showed 0% resistance to nitrofurantoin, 0% to linecolamine, and 0% to vancomycin. Staphylococcus epidermidis was 0% resistant to nitrofurantoin and 15.79% resistant to linezolid; S. haemolyticus showed 0% resistance to nitrofurantoin, linecolamine and vancomycin. Three main Gram-negative bacteria, Escherichia coli, Pseudomonas aeruginosa, and K. pneumoniae, showed high resistance to erythromycin, ciprofloxacin, ofloxacin, and gentamicin. E. coli showed 80% resistance to erythromycin, 64% to ciprofloxacin, 60% to ofloxacin, and 52% to gentamicin. P. aeruginosa showed 46.15% resistance to erythromycin, 46.15 to ciprofloxacin, 38.46% to ofloxacin, and 38.46% to gentamicin. Klebsiella pneumoniae showed resistance to erythromycin (57.14%), ciprofloxacin (42.86%), ofloxacin (42.86%), and gentamicin (48.86%); 16% Escherichia coli were resistant to amikacin and 4% to meropenem. P. aeruginosa showed 7.69% were resistant to amikacin and 0% to Amikacin; Amikacin resistance in K. pneumoniae was 14.29% and 0 for meropenem.Conclusions:The pathogenic bacteria in patients with skin and soft tissue infection are often Gram-positive, and there are different resistance phenomena to various antimicrobial drugs. In clinical use, antibiotics should be rationally selected combined with the results of drug sensitivity test to reduce the bacterial drug resistance.