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: Cardiovascular disease (CVD) is the predominant cause of mortality in China. However, the mechanisms linking sarcopenia to CVD remain poorly understood, particularly in normal-weight populations. Individuals with the absence of overweight or obesity may tend to experience missed opportunities for timely intervention. This study aimed to investigate the longitudinal association between sarcopenia and incidence of new-onset CVD in a normal-weight population, and to examine the mediating effect of functional limitation in this relationship. METHODS: We conducted a closed-cohort analysis using a nationwide sample of 4,147 middle-aged and older adults with normal weight in China. We performed Cox proportional hazards regression analysis to explore the associations of baseline sarcopenia with incident CVD. The difference method was applied to estimate the mediation proportion of functional limitation in this association. RESULTS: Over a mean follow-up period of 7.62 years, CVD occurred in 835 participants. In the multivariable-adjusted Cox model, individuals with sarcopenia exhibited a significantly higher likelihood of developing incident CVD compared to those without sarcopenia (adjusted hazard ratio [aHR] = 1.45, 95% confidence interval [CI]: 1.21-1.73, P < 0.001). Similar associations were observed for the incidence of heart disease and stroke. Functional limitation accounted for approximately 15.0% of the total effect of sarcopenia on incident CVD (P < 0.001). CONCLUSIONS Sarcopenia exerts both direct and indirect effects on incident CVD among middle-aged and older adults who are normal weight, with functional limitation serving as a significant mediator. Interventions targeting both sarcopenia and functional limitation may offer a promising strategy for enhancing cardiovascular health in this population.
Humans ; Sarcopenia/complications* ; China/epidemiology* ; Male ; Female ; Middle Aged ; Cardiovascular Diseases/etiology* ; Aged ; Incidence ; Cohort Studies ; Proportional Hazards Models ; Risk Factors ; Aged, 80 and over ; Longitudinal Studies

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Learning-associated functional plasticity at hippocampal synapses remains largely unexplored. Here, in a single session of reward-based trace conditioning, we examine learning-induced synaptic plasticity in the dorsal CA1 hippocampus (dCA1). Local field-potential recording combined with selective optogenetic inhibition first revealed an increase of dCA1 synaptic responses to the conditioned stimulus (CS) induced during conditioning at both Schaffer collaterals to the stratum radiatum (Rad) and temporoammonic input to the lacunosum moleculare (LMol). At these dCA1 inputs, synaptic potentiation of CS-responding excitatory synapses was further demonstrated by locally blocking NMDA receptors during conditioning and whole-cell recording sensory-evoked synaptic responses in dCA1 neurons from naive animals. An overall similar time course of the induction of synaptic potentiation was found in the Rad and LMol by multiple-site recording; this emerged later and saturated earlier than conditioned behavioral responses. Our experiments demonstrate a cued memory-associated dCA1 synaptic plasticity induced at both Schaffer collaterals and temporoammonic pathways.
Animals ; CA1 Region, Hippocampal/physiology* ; Male ; Association Learning/physiology* ; Neuronal Plasticity/physiology* ; Cues ; Memory/physiology* ; Synapses/physiology* ; Conditioning, Classical/physiology* ; Excitatory Postsynaptic Potentials/physiology* ; Receptors, N-Methyl-D-Aspartate/antagonists & inhibitors* ; Rats ; Optogenetics

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
Child ; Humans ; Cognitive Training ; Magnetic Resonance Imaging ; Brain ; Brain Mapping ; Motor Cortex

Humans ; Lung Transplantation/adverse effects* ; Risk Factors

Chemotherapy resistance plays a pivotal role in the prognosis and therapeutic failure of patients with colorectal cancer(CRC).Cisplatin(DDP)-resistant cells exhibit an inherent ability to evade the toxic chemotherapeutic drug effects which are characterized by the activation of slow-cycle programs and DNA repair.Among the elements that lead to DDP resistance,O6-methylguanine(O6-MG)-DNA-meth-yltransferase(MGMT),a DNA-repair enzyme,performs a quintessential role.In this study,we clarify the significant involvement of MGMT in conferring DDP resistance in CRC,elucidating the underlying mechanism of the regulatory actions of MGMT.A notable upregulation of MGMT in DDP-resistant cancer cells was found in our study,and MGMT repression amplifies the sensitivity of these cells to DDP treatment in vitro and in vivo.Conversely,in cancer cells,MGMT overexpression abolishes their sensi-tivity to DDP treatment.Mechanistically,the interaction between MGMT and cyclin dependent kinase 1(CDK1)inducing slow-cycling cells is attainted via the promotion of ubiquitination degradation of CDK1.Meanwhile,to achieve nonhomologous end joining,MGMT interacts with XRCC6 to resist chemotherapy drugs.Our transcriptome data from samples of 88 patients with CRC suggest that MGMT expression is co-related with the Wnt signaling pathway activation,and several Wnt inhibitors can repress drug-resistant cells.In summary,our results point out that MGMT is a potential therapeutic target and predictive marker of chemoresistance in CRC.

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

OBJECTIVES: To investigate the level of neuropsychological development in large for gestational age (LGA) infants at the age of 12 months. METHODS: The infants, aged 12 to <13 months, who attended the Outpatient Service of Child Care in the First Affiliated Hospital of Shandong First Medical University from December 2021 to June 2023, were enrolled as subjects. According to the gestational age and birth weight, they were divided into preterm appropriate for gestational age (AGA) group, preterm LGA group, early term AGA group, early term LGA group, full-term AGA group, and full-term LGA group. A modified Poisson regression analysis was used to investigate the association between LGA and neuropsychological development outcome at 12 months of age. RESULTS: After adjustment for confounding factors, compared with the full-term AGA group at the age of 12 months, the full-term LGA group had a significant increase in the risk of language deficit (RR=1.364, 95%CI: 1.063-1.750), the early term LGA group had significant increases in the risk of abnormal gross motor, fine motor, language, and the preterm LGA group had significant increases in the risk of abnormal language, social behavior, and total developmental quotient (P<0.05); also, the early term AGA group had higher risks of developmental delay across all five attributes and in total developmental quotient at the age of 12 months (P<0.05); except for the language attribute, the preterm AGA group had higher risks of developmental delay in the other 4 attributes (P<0.05). CONCLUSIONS The neuropsychological development of LGA infants with different gestational ages lags behind that of full-term AGA infants at 12 months of age, and follow-up and early intervention of such infants should be taken seriously in clinical practice.
Infant, Newborn ; Infant ; Child ; Humans ; Birth Weight ; Infant, Large for Gestational Age ; Infant, Small for Gestational Age ; Gestational Age ; Child Health