Herbal medicines have gained recognition among physicians and patients due to their lower adverse effects compared to modern medicines. They are extensively used to treat various diseases, including cancer, cardiovascular issues, chronic inflammation, microbial contamination, diabetes, obesity, and hepatic disorders, among others. Unfortunately, the clinical application of herbal medicines is limited by their low solubility and inadequate bioavailability. Utilizing herbal medicines in the form of nanocrystals (herbal medicine nanocrystals) has shown potential in enhancing solubility and bioavailability by reducing the particle size, increasing the specific surface area, and modifying the absorption mechanisms. Multiple studies have demonstrated that these nanocrystals significantly improve drug efficacy by reducing toxicity and increasing bioavailability. This review comprehensively examines therapeutic approaches based on herbal medicine nanocrystals. It covers the preparation principles, key factors influencing nucleation and polymorphism control, applications, and limitations. The review underscores the importance of optimizing delivery systems for successful herbal medicine nanocrystal therapeutics. Furthermore, it discusses the main challenges and opportunities in developing herbal medicine nanocrystals for the purpose of treating conditions such as cancer, inflammatory diseases, cardiovascular disorders, mental and nervous diseases, and antimicrobial infections. In conclusion, we have deliberated regarding the hurdles and forthcoming outlook in the realm of nanotoxicity, in vivo kinetics, herbal ingredients as stabilizers of nanocrystals, and the potential for surmounting drug resistance through the utilization of nanocrystalline formulations in herbal medicine. We anticipate that this review will offer innovative insights into the development of herbal medicine nanocrystals as a promising and novel therapeutic strategy.

Messenger RNA (mRNA) therapeutics have garnered considerable attention due to their remarkable efficacy in the treatment of various diseases. The COVID‐19 mRNA vaccine and RSV mRNA vaccine have been approved on the market. Due to the inherent nuclease‐instability and negative charge of mRNA, delivery systems are developed to protect the mRNA from degradation and facilitate its crossing cell membrane to express functional proteins or peptides in the cytoplasm. However, the deficiency in transfection efficiency and targeted biological distribution are still the major challenges for the mRNA delivery systems. In this review, we first described the physiological barriers in the process of mRNA delivery and then discussed the design approach and recent advances in mRNA delivery systems with an emphasis on their tissue/cell‐targeted abilities. Finally, we pointed out the existing challenges and future directions with deep insights into the design of efficient mRNA delivery systems. We believe that a high‐precision targeted delivery system can greatly improve the therapeutic effects and bio‐safety of mRNA therapeutics and accelerate their clinical transformations. This review may provide a new direction for the design of mRNA delivery systems and serve as a useful guide for researchers who are looking for a suitable mRNA delivery system.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by insidious onset and progressive symptom deterioration. It extends beyond a simple aging process, involving irreversible and progressive neurological degeneration that impairs brain function through multiple etiologies. Iron dysregulation is implicated in the pathophysiology of AD; however, the precise mechanisms remain unclear. Additionally, vitamin E and selenium are key in regulating ferroptosis through their antioxidant properties. The present review examined the mechanistic pathways by which ferroptosis contributes to AD, the regulatory roles of vitamin E, selenium, ferrostatin‑1, N‑acetylcysteine and curcumin, and their potential as therapeutic agents to mitigate neurodegeneration.

Due to multiple comorbid illnesses, polypharmacy, and age-related changes in pharmacokinetics and pharmacodynamics in older adults, the prevalence of potentially inappropriate medications (PIMs) is high, which affects the quality of life of older adults. Building an effective warning model is necessary for the early identification of PIMs to prevent harm caused by medication in geriatric patients. The purpose of this study was to develop a machine learning-based model for the warning of PIMs in older Chinese outpatients. This retrospective study was conducted among geriatric outpatients in nine tertiary hospitals in Chengdu from January 2018 to December 2018. The Beers criteria 2019 were used to assess PIMs in geriatric outpatients. Three problem transformation methods were used to tackle the multilabel classification problem in prescriptions. After the division of patient prescriptions into the training and test sets (8:2), we adopted six widely used classification algorithms to conduct the classification task and assessed the discriminative performance by the accuracy, precision, recall, F1 scores, subset accuracy (ss Acc), and Hamming loss (hm) of each model. The results showed that among 11,741 older patient prescriptions, 5816 PIMs were identified in 4038 (34.39%) patient prescriptions. A total of 41 types of PIMs were identified in these prescriptions. The three-problem transformation methods included label power set (LP), classifier chains (CC), and binary relevance (BR). Six classification algorithms were used to establish the warning models, including Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), CatBoost, Deep Forest (DF), and TabNet. The CC + CatBoost model had the highest accuracy value (97.83%), recall value (89.34%), F1 value (90.69%), and ss Acc value (97.79%) with a good precision value (92.18%) and the lowest hm value (0.0006). Therefore, the CC + CatBoost model was selected to predict the occurrence of PIM in geriatric Chinese patients. This study’s novelty establishes a warning model for PIMs in geriatric patients by using machine learning. With the popularity of electronic patient record systems, sophisticated computer algorithms can be implemented at the bedside to improve medication use safety in geriatric patients in the future.

Background: Older adults with dementia often face the risk of potentially inappropriate medication (PIM) use. The quality of PIM evaluation is hindered by researchers' unfamiliarity with evaluation criteria for inappropriate drug use. While traditional machine learning algorithms can enhance evaluation quality, they struggle with the multilabel nature of prescription data. Aim: This study aimed to combine six machine learning algorithms and three multilabel classification models to identify correlations in prescription information and develop an optimal model to identify PIMs in older adults with dementia. Method: This study was conducted from January 1, 2020, to December 31, 2020. We used cluster sampling to obtain prescription data from patients 65 years and older with dementia. We assessed PIMs using the 2019 Beers criteria, the most authoritative and widely recognized standard for PIM detection. Our modeling process used three problem transformation methods (binary relevance, label powerset, and classifier chain) and six classification algorithms. Results: We identified 18,338 older dementia patients and 36 PIMs types. The classifier chain + categorical boosting (CatBoost) model demonstrated superior performance, with the highest accuracy (97.93%), precision (95.39%), recall (94.07%), F1 score (95.69%), and subset accuracy values (97.41%), along with the lowest Hamming loss value (0.0011) and an acceptable duration of the operation (371s). Conclusion: This research introduces a pioneering CC + CatBoost warning model for PIMs in older dementia patients, utilizing machine-learning techniques. This model enables a quick and precise identification of PIMs, simplifying the manual evaluation process.

There has been some evidence that dietary fiber may be associated with diabetic nephropathy (DN), but the relationship is still unclear. The purpose of this study was to examine the association between dietary fiber intake and DN. This cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data collected between 2007 and 2020. Weighted multivariate logistic regression was used to examine the relation between dietary fiber intake and DN. In addition, fitted smoothed curves were used to explore potential non-linear relationships. If non-linearity was observed, inflection points were further calculated by a recursive algorithm. The study finally included 5964 subjects ≥20 years of age. The mean age was 60.8 ± 13.4 years with males (52.4 %), and non-Hispanic Whites (62.4 %), and the weighted prevalence of DN was 36.7 %. Dietary fiber was negatively associated with the risk of DN after controlling for all confounding variables (OR = 0.89, 95%CI: 0.80, 0.99). Smoothed curve fit plots of the dose relationship showed that dietary fiber intake was linearly related to DN, whereas males (inflection point of 8.0 g/d) and non-Hispanic Blacks (inflection point of 14.9 g/d) followed a non-linear inverted U-shaped curve relationship. In United States adults aged 20 and older, dietary fiber intake may be associated with a reduced risk of DN. Appropriate increases in dietary fiber intake may offer potential benefits for DN. In conclusion, it appears that increasing dietary fiber intake may be one of the most effective strategies for the prevention and management of DN.