Research progress on perioperative blood-brain barrier damage and its potential mechanism

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Comprehensive review of potential drugs with anti-pulmonary fibrosis properties

Pulmonary fibrosis is a chronic and progressive lung disease characterized by the accumulation of scar tissue in the lungs, which leads to impaired lung function and reduced quality of life. The prognosis for idiopathic pulmonary fibrosis (IPF), which is the most common form of pulmonary fibrosis, is generally poor. The median survival for patients with IPF is estimated to be around 3-5 years from the time of diagnosis. Currently, there are two approved drugs (Pirfenidone and Nintedanib) for the treatment of IPF. However, Pirfenidone and Nintedanib are not able to reverse or cure pulmonary fibrosis. There is a need for new pharmacological interventions that can slow or halt disease progression and cure pulmonary fibrosis. This review aims to provide an updated overview of current and future drug interventions for idiopathic pulmonary fibrosis, and to summarize possible targets of potential anti-pulmonary fibrosis drugs, providing theoretical support for further clinical combination therapy or the development of new drugs.

Alterations in microbiota of patients with COVID-19: implications for therapeutic interventions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently caused a global pandemic, resulting in more than 702 million people being infected and over 6.9 million deaths. Patients with coronavirus disease (COVID-19) may suffer from diarrhea, sleep disorders, depression, and even cognitive impairment, which is associated with long COVID during recovery. However, there remains no consensus on effective treatment methods. Studies have found that patients with COVID-19 have alterations in microbiota and their metabolites, particularly in the gut, which may be involved in the regulation of immune responses. Consumption of probiotics may alleviate the discomfort caused by inflammation and oxidative stress. However, the pathophysiological process underlying the alleviation of COVID-19-related symptoms and complications by targeting the microbiota remains unclear. In the current study, we summarize the latest research and evidence on the COVID-19 pandemic, together with symptoms of SARS-CoV-2 and vaccine use, with a focus on the relationship between microbiota alterations and COVID-19-related symptoms and vaccine use. This work provides evidence that probiotic-based interventions may improve COVID-19 symptoms by regulating gut microbiota and systemic immunity. Probiotics may also be used as adjuvants to improve vaccine efficacy.

Vascular dementia: From pathobiology to emerging perspectives

Vascular dementia (VaD) is the second most common type of dementia. VaD is synonymous with ageing, and its symptoms place a significant burden on the health and wellbeing of older people. Despite the identification of a substantial number of risk factors for VaD, the pathological mechanisms underpinning this disease remain to be fully elucidated. Consequently, a biogerontological imperative exists to highlight the modifiable lifestyle factors which can mitigate against the risk of developing VaD. This review will critically examine some of the factors which have been revealed to modulate VaD risk. The survey commences by providing an overview of the putative mechanisms which are associated with the pathobiology of VaD. Next, the factors which influence the risk of developing VaD are examined. Finally, emerging treatment avenues including epigenetics, the gut microbiome, and pro-longevity pharmaceuticals are discussed. By drawing this key evidence together, it is our hope that it can be used to inform future experimental investigations in this field.

Perioperative stroke

Ischaemic or haemorrhagic perioperative stroke (that is, stroke occurring during or within 30 days following surgery) can be a devastating complication following surgery. Incidence is reported in the 0.1-0.7% range in adults undergoing non-cardiac and non-neurological surgery, in the 1-5% range in patients undergoing cardiac surgery and in the 1-10% range following neurological surgery. However, higher rates have been reported when patients are actively assessed and in high-risk populations. Prognosis is significantly worse than stroke occurring in the community, with double the 30-day mortality, greater disability and diminished quality of life among survivors. Considering the annual volume of surgeries performed worldwide, perioperative stroke represents a substantial burden. Despite notable differences in aetiology, patient populations and clinical settings, existing clinical recommendations for perioperative stroke are extrapolated mainly from stroke in the community. Perioperative in-hospital stroke is unique with respect to the stroke occurring in other settings, and it is essential to apply evidence from other settings with caution and to identify existing knowledge gaps in order to effectively guide patient care and future research.

Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.

Posterity of nanoscience as lipid nanosystems for Alzheimer's disease regression

Alzheimer's disease (AD) is a type of dementia that affects a vast number of people around the world, causing a great deal of misery and death. Evidence reveals a relationship between the presence of soluble Aβ peptide aggregates and the severity of dementia in Alzheimer's patients. The BBB (Blood Brain Barrier) is a key problem in Alzheimer's disease because it prevents therapeutics from reaching the desired places. To address the issue, lipid nanosystems have been employed to deliver therapeutic chemicals for anti-AD therapy in a precise and targeted manner. The applicability and clinical significance of lipid nanosystems to deliver therapeutic chemicals (Galantamine, Nicotinamide, Quercetin, Resveratrol, Curcumin, HUPA, Rapamycin, and Ibuprofen) for anti-AD therapy will be discussed in this review. Furthermore, the clinical implications of the aforementioned therapeutic compounds for anti-AD treatment have been examined. Thus, this review will pave the way for researchers to fashion therodiagnostics approaches based on nanomedicine to overcome the problems of delivering therapeutic molecules across the blood brain barrier (BBB).