AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.

BACKGROUND AND AIMS: Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS: We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS: About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION: Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets.

This article provides an in-depth review of computational methods for predicting transcriptional regulators (TRs) with query gene sets. Identification of TRs is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement.

Co-delivery of Siape1 and Melatonin by 125I-loaded PSMA-targeted Nanoparticles for the Treatment of Prostate Cancer.

BACKGROUND: Both apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) inhibition and melatonin suppress prostate cancer (PCa) growth. OBJECTIVE: This study evaluated the therapeutic efficiency of self-assembled and prostate-specific membrane antigen (PSMA)-targeted nanocarrier loading 125I radioactive particles and encapsulating siRNA targeting APE1 (siAPE1) and melatonin for PCa. METHODS: The linear polyarginine R12 polypeptide was prepared using Fmoc-Arg-Pbf-OH. The PSMA-targeted polymer was synthesized by conjugating azide-modified R12 peptide to PSMA monoclonal antibody (mAb). Before experiments, the PSMA-R12 nanocarrier was installed with melatonin and siAPE1, which were subsequently labeled by 125I radioactive particles. In vitro biocompatibility and cytotoxicity of nanocomposites were examined in LNCaP cells and in vivo biodistribution and pharmacokinetics were determined using PCa tumor-bearing mice. RESULTS: PSMA-R12 nanocarrier was ~120 nm in size and was increased to ~150 nm by melatonin encapsulation. PSMA-R12 nanoparticles had efficient loading capacities of siAPE1, melatonin, and 125I particles. The co-delivery of melatonin and siAPE1 by PSMA-R12-125I showed synergistic effects on suppressing LNCaP cell proliferation and Bcl-2 expression and promoting cell apoptosis and caspase-3 expression. Pharmacokinetics analysis showed that Mel@PSMA-R12-125I particles had high uptake activity in the liver, spleen, kidney, intestine, and tumor, and were accumulated in the tumor sites within the first 8 h p.i., but was rapidly cleared from all the tested organs at 24 h p.i. Administration of nanoparticles to PCa tumors in vivo showed that Mel@PSMA-R12- 125I/siAPE1 had high efficiency in suppressing PCa tumor growth. CONCLUSION: The PSMA-targeted nanocarrier encapsulating siAPE1 and melatonin is a promising therapeutic strategy for PCa and can provide a theoretical basis for patent applications.

Unraveling the molecular landscape of breast muscle development in domestic Yuzhong pigeons and European meat pigeon: Insights from Iso-seq and RNA-seq analysis.

The mechanisms governing gene regulation in domestic Yuzhong pigeon breast muscle development remain largely elusive. Here, we conducted a comparative analysis using Iso-seq and RNA-seq data from domestic Yuzhong pigeons and European meat pigeons to uncover signaling pathways and genes involved in breast muscle development. The Iso-seq data from domestic Yuzhong pigeons yielded 131,377,075 subreads, resulting in 16,587 non-redundant high-quality full-length transcripts post-correction. Furthermore, utilizing pfam, CPC, PLEK, and CPAT, we predicted 5575, 4973, 2333, and 4336 lncRNAs, respectively. Notably, several genes potentially implicated in breast muscle development were identified, including tropomyosin beta chain, myosin regulatory light chain 2, and myosin binding protein C. KEGG enrichment analysis revealed critical signaling pathways in breast muscle development, spanning carbon metabolism, biosynthesis of amino acids, glycolysis/gluconeogenesis, estrogen signaling, PI3K-AKT signaling, protein processing in the endoplasmic reticulum, oxidative phosphorylation, pentose phosphate pathway, fructose and mannose metabolism, and tight junctions. These findings offer insights into the biological processes driving breast muscle development in domestic Yuzhong pigeon, contributing to our understanding of this complex phenomenon.

A-to-I edited miR-154-p13-5p inhibited cell proliferation and migration and induced apoptosis by targeting LIX1L in the bladder cancer.

With the advancement of RNA sequencing technology, there has been a drive to uncover and elucidate the pivotal role of A-to-I RNA editing events in tumorigenesis. However, A-to-I miRNA editing events have been clearly identified in bladder cancer, the molecular mechanisms underlying their role in bladder cancer remain unclear. In our investigation, we observed a notable under-expression of edited miR-154-p13-5p in bladder cancer (BC) tissues, in contrast to normal counterparts. Remarkably, heightened expression levels of edited miR-154-p13-5p correlated with improved survival outcomes. To assess the impact of modified miR-154-p13-5p, we conducted a string of cell phenotype assays through transfection of the corresponding miRNAs or siRNAs. The results unequivocally demonstrate that edited miR-154-p13-5p exerts a substantial inhibitory influence on proliferation, migration, and induces apoptosis by specifically targeting LIX1L in bladder cancer. Moreover, we observed that the editing of miR-154-p13-5p or LIX1L-siRNAs inhibits the expression of LIX1L, thereby suppressing EMT-related proteins and cell cycle protein CDK2. Simultaneously, an upregulation in the expression levels of Caspase-3 and Cleaved Caspase-3 were also detected. Our research findings suggest that the upregulation of edited miR-154-p13-5p could potentially enhance the prognosis of bladder cancer, thereby presenting molecular biology-based therapeutic strategies.

BIT: Bayesian Identification of Transcriptional Regulators.

BIT is a novel Bayesian hierarchical model capable of predicting transcriptional regulators (TRs) from the input of user-provided epigenomic regions. TRs are critical molecules in transcriptional regulation. Many diseases and cancers are linked to the dysfunction of TRs. Knowing TRs in certain biological process can help find new biomarkers or therapeutic targets. Thus, BIT formulates a novel Bayesian hierarchical model with the Pólya-gamma data augmentation strategy. Based on collected ChIP-seq datasets, BIT can identify TRs responsible for the genome-wide binding pattern within the user-provided epigenomic regions. BIT has been validated by using a simulation study and three applications.

Rhoptry proteins affect the placental barrier in the context of Toxoplasma gondii infection: Signaling pathways and functions.

Toxoplasma gondii is an opportunistic and pathogenic obligate intracellular parasitic protozoan that is widespread worldwide and can infect most warm-blooded animals, seriously endangering human health and affecting livestock production. Toxoplasmosis caused by T. gondii infection has different clinical manifestations, which are mainly determined by the virulence of T. gondii and host differences. Among the manifestations of this condition, abortion, stillbirth, and fetal malformation can occur if a woman is infected with T. gondii in early pregnancy. Here, we discuss how the T. gondii rhoptry protein affects host pregnancy outcomes and speculate on the related signaling pathways involved. The effects of rhoptry proteins of T. gondii on the placental barrier are complex. Rhoptry proteins not only regulate interferon-regulated genes (IRGs) to ensure the survival of parasites in activated cells but also promote the spread of worms in tissues and the invasive ability of the parasites. The functions of these rhoptry proteins and the associated signaling pathways highlight relevant mechanisms by which Toxoplasma crosses the placental barrier and influences fetal development and will guide future studies to uncover the complexity of the host-pathogen interactions.

Depression and the Risk of Dementia and All-Cause Mortality Among Japanese Older Adults: A 9-Year Longitudinal Study From JAGES.

BACKGROUND: This study aims to investigate the association and dose-response relationship between depression, dementia, and all-cause mortality based on a national cohort study of older adults in Japan. METHODS: We conducted a longitudinal study of 44,546 participants ≥65 years from 2010-2019 Japanese Gerontological Evaluation Study. The Geriatric Depression Scale-15 was used to assess depressive symptoms and the long-term care insurance was used to assess dementia. Fine-Gray models and Cox proportional hazard models were used to explore the effect of depression severity on the incidence of dementia and all-cause mortality, respectively. Causal mediation analysis were used to explore the extent of association between dementia-mediated depression and all-cause mortality. RESULTS: We found that both minor and major depressive symptoms were associated with the increased cumulative incidence of dementia and all-cause mortality, especially major depressive symptoms (p < .001). The multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for dementia were 1.25 (1.19-1.32) for minor depressive symptoms and 1.42 (1.30-1.54) for major depressive symptoms in comparison to non-depression; p for trend < .001. The multivariable-adjusted HRs and 95% CIs for all-cause mortality were 1.27 (1.21-1.33) for minor depressive symptoms and 1.51 (1.41-1.62) for major depressive symptoms in comparison to non-depression; p for trend < .001. Depression has a stronger impact on dementia and all-cause mortality among the younger group. In addition, dementia significantly mediated the association between depression and all-cause mortality. DISCUSSION: Interventions targeting major depression may be an effective strategy for preventing dementia and premature death.

Predictive value of TCCD and regional cerebral oxygen saturation for detecting early postoperative brain injury.

OBJECTIVE: This study aims to analyze the risk factors for early postoperative brain injury in patients undergoing cardiovascular surgery and explore the predictive value of transcranial color Doppler (TCCD) and regional cerebral oxygen saturation (rSO2) for detecting early postoperative brain injury in cardiovascular surgery patients. METHODS: A total of 55 patients undergoing cardiovascular surgery with cardiopulmonary bypass in Changzhou No.2 The People's Hospital of Nanjing Medical University were included in this study. Neuron-specific enolase (NSE) concentration was measured 24 h after operation. Patients were divided into brain injury (NSE ≥ 16.3 ng/mL) and normal (0 < NSE < 16.3 ng/mL) groups according to the measured NSE concentration. The clinical outcomes between the two groups were compared, including decreased rSO2 and cerebral blood flow (as measured by TCCD) levels. The risk factors of early postoperative brain injury were analyzed by multivariate logistic regression analysis, and the significant variables were analyzed by receiver operating characteristic (ROC) analysis. RESULTS: A total of 50 patients were included in this study, with 20 patients in the brain injury group and 30 patients in the normal group. Cardiopulmonary bypass time (min) (107 ± 29 vs. 90 ± 28, P = 0.047) and aortic occlusion time (min) (111 (IQR 81-127) vs. 87 (IQR 72-116), P = 0.010) were significantly longer in the brain injury group than in the normal group. Patients in the brain injury group had greater decreased rSO2 (%) (27.0 ± 7.3 vs. 17.5 ± 6.1, P < 0.001) and cerebral blood flow (%) (44.9 (IQR 37.8-69.2) vs. 29.1 (IQR 12.0-48.2), P = 0.004) levels. Multivariate logistic regression analysis suggested that decreased rSO2 and cerebral blood flow levels, aortic occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury (P < 0.05). ROC analysis reported that the best cutoff values for predicting early postoperative brain injury were 21.4% and 37.4% for decreased rSO2 and cerebral blood flow levels, respectively (P < 0.05). CONCLUSION: The decreased rSO2 and cerebral blood flow levels, aorta occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury. TCCD and rSO2 could effectively monitor brain metabolism and cerebral blood flow and predict early postoperative brain injury.

Clinical characteristics and molecular mechanisms underlying bladder cancer in individuals with spinal cord injury: a systematic review.

BACKGROUND: Patients with spinal cord injury have a relatively high risk for bladder cancer and often complicated with bladder cancer in advanced stages, and the degree of aggressiveness of malignancy is high. Most of the literature is based on disease clinical features while, our study reviews the clinical characteristics and molecular mechanisms of spinal cord injury patients with bladder cancer, so that it might help clinicians better recognize and manage these patients. METHOD: We searched PubMed, Web of Science and Embase, using retrieval type like ("Neurogenic Lower Urinary Tract Dysfunction" OR "Spinal cord injury" OR "Spinal Cord Trauma") AND ("bladder cancer" OR "bladder neoplasm" OR "bladder carcinoma" OR "Urinary Bladder Neoplasms" OR "Bladder Tumor"). In Web of Science, the retrieval type was searched as "Topic", and in PubMed and Embase, as "All Field". The methodological quality of eligible studies and their risk of bias were assessed using the Newcastle-Ottawa scale. This article is registered in PROSPERO with the CBD number: CRD42024508514. RESULT: In WOS, we searched 219 related papers, in PubMed, 122 and in Embase, 363. Thus, a total of 254 articles were included after passing the screening, within a time range between 1960 and 2023. A comprehensive analysis of the data showed that the mortality and incidence rates of bladder cancer in spinal cord injury patients were higher than that of the general population, and the most frequent pathological type was squamous cell carcinoma. In parallel to long-term urinary tract infection and indwelling catheterization, the role of molecules such as NO, MiR 1949 and Rb 1. was found to be crucial pathogenetically. CONCLUSION: This review highlights the risk of bladder cancer in SCI patients, comprehensively addressing the clinical characteristics and related molecular mechanisms. However, given that there are few studies on the molecular mechanisms of bladder cancer in spinal cord injury, further research is needed to expand the understanding of the disease.

Detection of uridine diphosphate glucuronosyltransferase 1A1 for pancreatic cancer imaging and treatment via a "turn-on" fluorescent probe.

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.

Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Assessing NGS-based computational methods for predicting transcriptional regulators with query gene sets.

This article provides an in-depth review of computational methods for predicting transcriptional regulators with query gene sets. Identification of transcriptional regulators is of utmost importance in many biological applications, including but not limited to elucidating biological development mechanisms, identifying key disease genes, and predicting therapeutic targets. Various computational methods based on next-generation sequencing (NGS) data have been developed in the past decade, yet no systematic evaluation of NGS-based methods has been offered. We classified these methods into two categories based on shared characteristics, namely library-based and region-based methods. We further conducted benchmark studies to evaluate the accuracy, sensitivity, coverage, and usability of NGS-based methods with molecular experimental datasets. Results show that BART, ChIP-Atlas, and Lisa have relatively better performance. Besides, we point out the limitations of NGS-based methods and explore potential directions for further improvement. Key points: An introduction to available computational methods for predicting functional TRs from a query gene set.A detailed walk-through along with practical concerns and limitations.A systematic benchmark of NGS-based methods in terms of accuracy, sensitivity, coverage, and usability, using 570 TR perturbation-derived gene sets.NGS-based methods outperform motif-based methods. Among NGS methods, those utilizing larger databases and adopting region-centric approaches demonstrate favorable performance. BART, ChIP-Atlas, and Lisa are recommended as these methods have overall better performance in evaluated scenarios.

Rapid screening of the novel bioactive peptides with notable α-glucosidase inhibitory activity by UF-LC-MS/MS combined with three-AI-tool from black beans.

This work aimed to propose a rapid method to screen the bioactive peptides with anti-α-glucosidase activity instead of traditional multiple laborious purification and identification procedures. 242 peptides binding to α-glycosidase were quickly screened and identified by bio-affinity ultrafiltration combined with LC-MS/MS from the double enzymatic hydrolysate of black beans. Top three peptides with notable anti-α-glucosidase activity, NNNPFKF, RADLPGVK and FLKEAFGV were further rapidly screened and ranked by the three artificial intelligence tools (three-AI-tool) BIOPEP database, PeptideRanker and molecular docking from the 242 peptides. Their IC50 values were in order as 4.20 ± 0.11 mg/mL, 2.83 ± 0.03 mg/mL, 1.32 ± 0.09 mg/mL, which was opposite to AI ranking, for the hydrophobicity index of the peptides was not included in the screening criteria. According to the kinetics, FT-IR, CD and ITC analyses, the binding of the three peptides to α-glucosidase is a spontaneous and irreversible endothermic reaction that results from hydrogen bonds and hydrophobic interactions, which mainly changes the α-helix structure of α-glucosidase. The peptide-activity can be evaluated vividly by AFM in vitro. In vivo, the screened FLKEAFGV and RADLPGVK can lower blood sugar levels as effectively as acarbose, they are expected to be an alternative to synthetic drugs for the treatment of Type 2 diabetes.

Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

Self-standing perylene diimide covalent organic framework membranes for trace TMA sensing at room temperature.

The unprecedented demand for highly selective, real-time monitoring and low-power gas sensors used in food quality control has been driven by the increasing popularity of the Internet of Things (IoT). Herein, the self-standing perylene diimide based covalent organic framework membranes (COFMPDI-THSTZ) were prepared via liquid-liquid interfacial synthesis method. By incorporating the perylene diimide monomer into the COFM through molecular engineering, COFMPDI-THSTZ based sensor demonstrated an outstanding trimethylamine (TMA)-sensing performance at room temperature. Benefited from the TMA-accessible self-standing membrane morphology, π-electron delocalization effect, and extensive surface area with continuous nanochannels, the specific and highly sensitive TMA measurement has been achieved within the range of 0.03-400 ppm, with an exceptional theoretical detection limit as low as 10 ppb. Moreover, the primary internal mechanism of COFMPDI-THSTZ for this efficient TMA detection was investigated through in-situ FT-IR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers on sensing material surface, resulting in alterations in sensor resistance upon exposure to the target gas. For practical usage, COFMPDI-THSTZ based sensor exhibited exceptional real-time in-situ sensing capabilities, further confirmed their potential for application in dynamic prediction evaluation of marine fish products and quality monitoring in IoT.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

Ozone-mediated cerebral protection: Unraveling the mechanism through ferroptosis and the NRF2/SLC7A11/GPX4 signaling pathway.

BACKGROUND: The pathogenesis of brain ischemic/reperfusion (I/R) insult is characterized by neuronal loss due to excessive oxidative stress responses. Ferroptosis, a form of oxidative cell death, can be triggered when the balance between antioxidants and pro-oxidants in cells is disrupted. Ozone, a natural bioactive molecule with antioxidant/anti-apoptotic and pro-autophagic properties, has been shown to enhance the antioxidant system's capacity and ameliorate oxidative stress. However, its role in neuronal ferroptosis remains unclear. Therefore, we investigated the functions and possible mechanisms of ozone in cerebral I/R-induced ferroptotic neuronal death. METHODS: A cerebral ischemia-reperfusion injury model was induced in Sprague-Dawley (SD) rats pre-treated with ozone. Intraperitoneal administration of the NRF2 inhibitor ML385, the SLC7A11 inhibitor Erastin, and the GPX4 inhibitor RSL3 was performed one hour prior to model establishment. RESULTS: Our results showed that ozone preconditioning mitigated neuronal damage caused by cerebral I/R, reduced the severity of neurological deficits, lowered cerebral infarct volume in middle cerebral artery occlusion (MCAO) rats, and decreased the volume of cerebral infarcts. Transmission electron microscopy, immunofluorescence, and Western blotting indicated ferroptosis following MCAO-induced brain damage. MCAO resulted in morphological damage to neuronal mitochondria, increased lipid peroxidation accumulation, and elevated malondialdehyde (MDA) production. Furthermore, MCAO decreased levels of FTH1 and GPX4 (negative regulators of ferroptosis) and increased ACSL4 levels (a positive regulator of ferroptosis). Ozone preconditioning demonstrated a neuroprotective effect by increasing NRF2 nuclear translocation and the expression of SLC7A11 and GPX4. Treatment with ML385, Erastin, and RSL3 significantly reversed ozone preconditioning's protective effect on neuronal ferroptosis. CONCLUSION: Our findings demonstrated that ozone treatment attenuates ferroptosis in a cerebral ischemia/reperfusion injury rat model via the NRF2/SLC7A11/GPX4 pathway, providing a theoretical basis for ozone's potential use as a therapy to prevent ischemic stroke.

MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.

MOTIVATION: Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. RESULTS: We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. AVAILABILITY AND IMPLEMENTATION: R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.