Gene therapy and gene editing strategies in inherited blood disorders.

Gene therapy has shown significant potential in treating various diseases, particularly inherited blood disorders such as hemophilia, sickle cell disease, and thalassemia. Advances in understanding the regulatory network of disease-associated genes have led to the identification of additional therapeutic targets for treatment, especially for ß-hemoglobinopathies. Erythroid regulatory factor BCL11A offers the most promising therapeutic target for ß-hemoglobinopathies and reduction of its expression using the commercialized gene therapy product Casgevy was approved for use in the UK and USA in 2023. Notably, the emergence of innovative gene editing technologies has further broadened the gene therapy landscape, presenting new possibilities for treatment. Intensive studies indicate that base editing and prime editing, built upon CRISPR technology, enable precise single-base modification in hematopoietic stem cells for addressing inherited blood disorders ex vivo and in vivo. In this review, we present an overview of the current landscape of gene therapies, focusing on clinical research and gene therapy products for inherited blood disorders, evaluation of potential gene targets, and the gene editing tools employed in current gene therapy practices, which provides an insight for the establishment of safer and more effective gene therapy methods for a wider range of diseases in the future.

Evaluating current and future pedestrian mid-block crossing safety treatments using virtual reality simulation.

Virtual reality (VR) simulation offers a proactive, cost effective, immersive, and low risk platform for studying pedestrian safety. Within immersive virtual environments (IVEs), existing and alternative design conditions and intelligent transportation systems (ITS) technologies can be directly compared, prior to real-world implementation, to assess the impacts alternatives may have on pedestrian safety, perception, and behavior. Environmental factors can be controlled within IVEs so that test trials are replicable and directly comparable. Coupled with stated preference feedback, participants' observed preferences and behavior provide a comprehensive understanding of the impacts of proposed design alternatives. This research presents a case study of pedestrian behavior with three different mid-block crossing safety treatments modeled within a one-to-one scale IVE replication of a real-world location in Charlottesville, Virginia. The three safety treatments consider both passive and active collision avoidance designs and technologies, including (1) the existing painted crosswalk, (2) the addition of rectangular rapid flashing beacons (RRFBs), and (3) a pedestrian to everything (P2X) ITS phone application. Additionally, this paper demonstrates a VR simulation experimental design and framework for testing pedestrian safety treatments within naturalistic and replicable IVEs to assess both stated and observed preferences and behaviors of pedestrians. Repeated measures ANOVA indicated changes in both accepted gap size (p = 0.001) and crossing speed (p < 0.001) with alternative safety treatments. Generalized mixed models showed that pedestrians waited for statistically larger gap sizes (p = 0.02) without the assistance of alternative safety technologies (RRFBs and P2X application) and pedestrians crossed the street significantly faster (p = 0.001) without the alternative safety technologies, leading to unsafe dashing behavior. Through post-experiment surveys, it was found that participants perceived the As Built environment to be the least safe of the three treatments and that their sense of risk within the IVE was realistic. Considering both the observed crossing behavior and stated feedback, pedestrians exhibited intentionally unsafe darting behavior without assistive safety technology. This study demonstrates how VR simulation may be leveraged to study both stated preferences and observed behavior for understanding the safety implications of alternative roadway designs, providing a proactive approach for assessing and designing for pedestrian safety.

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

INTRODUCTION: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C2h), as an indicator of safety and efficacy, are important for optimizing therapy. OBJECTIVE: The objective of this study was to establish machine learning (ML) models to predict the C2h, that can be used for establishing an individualized dosing regimen in clinical practice. METHODS: Published population pharmacokinetic (PopPK) models for adults were searched based on PubMed and ultimately four reliable models were selected for simulating individual C2h datasets under different conditions (demographics, genotype, ethnicity, etc.). Machine learning models were trained on simulated C2h obtained from the four PopPK models. Five different algorithms were used for ML model building to predict C2h. Real-world data were used for predictive performance evaluations. Virtual trials were used to compare ML-optimized doses with PopPK model-optimized doses. RESULTS: Categorical boosting (CatBoost) exhibited the highest prediction ability. Target C2h can be predicted using the ML model combined with the dosing regimen and three covariates (N-acetyltransferase 2 [NAT2] genotypes, weight and race [Asians and Africans]). Real-world data validation results showed that the ML model can achieve an overall prediction accuracy of 93.4%. Using the final ML model, the mean absolute prediction error value decreased by 45.7% relative to the average of PopPK models. Using the ML-optimized dosing regimen, the probability of target attainment increased by 43.7% relative to the PopPK model-optimized dosing regimens. CONCLUSION: Machine learning models were developed with great predictive performance, which can be used to determine the individualized initial dose of isoniazid in adult patients.

Parental willingness to pay and preference for human papillomavirus vaccine for girls aged 9-14 in subsidy scenarios in Shanghai, China.

Multiple studies have documented low human papillomavirus (HPV) vaccine uptake among Chinese girls. It remains crucial to determine the parental willingness to pay (WTP) HPV vaccine for girls. We conducted a cross-sectional study recruiting 3904 parents with girls aged 9-14 in Shanghai, China, employing an online questionnaire with a convenience sampling strategy. Parental WTP, both range of payment and estimated point value, were determined for themselves (or wives) and daughters. HPV vaccine uptake was 22.44% in mothers and 3.21% in daughters. Respondents favored WTP ≤ 1000 CNY/138 USD for themselves (or wives), whereas showed increasing WTP along with valency of HPV vaccine for daughters (2-valent: 68.62% ≤1000 CNY/138 USD; 4-valent: 56.27% 1001-2000 CNY/138-277 USD; 9-valent: 65.37% ≥2001 CNY/277 USD). Overall, respondents showed higher WTP for daughters (median 2000 CNY/277 USD; IQR 1000-3600 CNY/138-498 USD) than for themselves (2000 CNY/277 USD; 1000-3500 CNY/138-483 USD) or wives (2000 CNY/277 USD; 800-3000 CNY/110-414 USD) (each p < .05). Furthermore, parental WTP was higher for international vaccine and 9-valent vaccine (each p < 0.05). Between two assumed government subsidy scenarios, parental preference for 9-valent vaccine remained consistently high for daughters (approximately 24% in each scenario), whereas preference for themselves (or wives) was sensitive to payment change between the subsidy scenarios. Using a discrete choice experiment, we found domestic vaccine was commonly preferred; however, certain sociodemographic groups preferred multivalent HPV vaccines. In conclusion, the valency of HPV vaccine may influence parental decision-making for daughters, in addition to vaccine price. Our findings would facilitate tailoring the HPV immunization program in China.

FABP3 Induces Mitochondrial Autophagy to Promote Neuronal Cell Apoptosis in Brain Ischemia-Reperfusion Injury.

This study elucidates the molecular mechanisms by which FABP3 regulates neuronal apoptosis via mitochondrial autophagy in the context of cerebral ischemia-reperfusion (I/R). Employing a transient mouse model of middle cerebral artery occlusion (MCAO) established using the filament method, brain tissue samples were procured from I/R mice. High-throughput transcriptome sequencing on the Illumina CN500 platform was performed to identify differentially expressed mRNAs. Critical genes were selected by intersecting I/R-related genes from the GeneCards database with the differentially expressed mRNAs. The in vivo mechanism was explored by infecting I/R mice with lentivirus. Brain tissue injury, infarct volume ratio in the ischemic penumbra, neurologic deficits, behavioral abilities, neuronal apoptosis, apoptotic factors, inflammatory factors, and lipid peroxidation markers were assessed using H&E staining, TTC staining, Longa scoring, rotation experiments, immunofluorescence staining, and Western blot. For in vitro validation, an OGD/R model was established using primary neuron cells. Cell viability, apoptosis rate, mitochondrial oxidative stress, morphology, autophagosome formation, membrane potential, LC3 protein levels, and colocalization of autophagosomes and mitochondria were evaluated using MTT assay, LDH release assay, flow cytometry, ROS/MDA/GSH-Px measurement, transmission electron microscopy, MitoTracker staining, JC-1 method, Western blot, and immunofluorescence staining. FABP3 was identified as a critical gene in I/R through integrated transcriptome sequencing and bioinformatics analysis. In vivo experiments revealed that FABP3 silencing mitigated brain tissue damage, reduced infarct volume ratio, improved neurologic deficits, restored behavioral abilities, and attenuated neuronal apoptosis, inflammation, and mitochondrial oxidative stress in I/R mice. In vitro experiments demonstrated that FABP3 silencing restored OGD/R cell viability, reduced neuronal apoptosis, and decreased mitochondrial oxidative stress. Moreover, FABP3 induced mitochondrial autophagy through ROS, which was inhibited by the free radical scavenger NAC. Blocking mitochondrial autophagy with sh-ATG5 lentivirus confirmed that FABP3 induces mitochondrial dysfunction and neuronal apoptosis by activating mitochondrial autophagy. In conclusion, FABP3 activates mitochondrial autophagy through ROS, leading to mitochondrial dysfunction and neuronal apoptosis, thereby promoting cerebral ischemia-reperfusion injury.

Deciphering the molecular mechanism of long non-coding RNA HIF1A-AS1 regulating pancreatic cancer cells.

Background: HIF1A-AS1, an antisense transcript of HIF1α gene, is a 652-bp LncRNA that is globally expressed in multiple tissues of animals. Recent evidence indicated that HIF1A-AS1 was involved in tumorigenesis of several types of cancer. However, the role of lncRNA in PC has not been reported, and the molecular mechanism remains elusive. Results: In order to investigate the role of HIF1A-AS1 in PC, it was overexpressed in some PC cell lines (PANC-1, PATU8988 and SW1990), and a series of experiments including cell viability detection, flow cytometry, transwell migration, clone formation and wound healing were performed. Functionally, the results indicated that overexpression of HIF1A-AS1 could greatly inhibit proliferation and migration and promote apoptosis of PC cells. Moreover, the isobaric tags for relative and absolute quantification (iTRAQ) quantitative proteomics analysis was implemented to explore the underlying mechanism and the results indicated that OE of HIF1A-AS1 globally affected the expression levels of multiple proteins associated with metabolism of cancer. At last, the network analysis revealed that most of these differentially expressed proteins (DEPs) were integrated and severed essential roles in regulatory function. In view of this, we guessed HIF1A-AS1 overexpression induced the dysfunction of metabolism and disordered proteins' translation, which may account for its excellent tumour suppressor effect. Conclusions: HIF1A-AS1 altered the cell function of PC cell lines via affecting the expression of numerous proteins. In summary, HIF1A-AS1 may exhibit a potential therapeutic effect on PC, and our study provided useful information in this filed.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework.

BACKGROUND: The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. METHODS: These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. RESULTS: Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. CONCLUSIONS: This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.

The two-step strategy for enhancing the specific activity and thermostability of alginate lyase AlyG2 with mechanism for improved thermostability.

To overcome the trade-off challenge encountered in the engineering of alginate lyase AlyG2 from Seonamhaeicola algicola Gy8T and to expand its potential industrial applications, we devised a two-step strategy encompassing activity enhancement followed by thermal stability engineering. To enhance the specific activity of efficient AlyG2, we strategically substituted residues with bulky steric hindrance proximal to the active pocket with glycine or alanine. This led to the generation of three promising positive mutants, with particular emphasis on the T91S mutant, exhibiting a 1.91-fold specific activity compared to the wild type. To mitigate the poor thermal stability of T91S, mutants with negative ΔΔG values in the thermal flexibility region were screened out. Notably, the S72Ya mutant not only displayed 17.96 % further increase in specific activity but also exhibited improved stability compared to T91S, manifesting as a remarkable 30.97 % increase in relative activity following a 1-hour incubation at 42 °C. Furthermore, enhanced kinetic stability was observed. To gain deeper insights into the mechanism underlying the enhanced thermostability of the S72Ya mutant, we conducted molecular dynamics simulations, principal component analysis (PCA), dynamic cross-correlation map (DCCM), and free energy landscape (FEL) analysis. The results unveiled a reduction in the flexibility of the surface loop, a stronger correlation dynamic and a narrower motion subspace in S72Ya system, along with the formation of more stable hydrogen bonds. Collectively, our findings suggest amino acids substitutions resulting in smaller side chains proximate to the active site can positively impact enzyme activity, while reducing the flexibility of surface loops emerges as a pivotal factor in conferring thermal stability. These insights offer valuable guidance and a framework for the engineering of other enzyme types.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. METHODS: Five ß-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. FINDINGS: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. INTERPRETATION: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal ß-lactam antibiotic doses. FUNDING: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

The antiangiogenic effect of total saponins of Panax japonicus C.A. Meyer in rheumatoid arthritis is mediated by targeting the HIF-1α/VEGF/ANG-1 axis.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese herbal medicine Panax japonicus C.A. Meyer has a long history in clinical treatment of rheumatoid arthritis (RA). Total saponins of Panax japonicus C.A. Meyer (TSPJs) were extracted from the root of Panax japonicus C.A. Meyer, and its anti-rheumatism mechanism is still unclear. AIM OF THE STUDY: To investigate whether TSPJs attenuated synovial angiogenesis in RA and explore the potential mechanisms. MATERIALS AND METHODS: Potential TSPJs targets involving gene function were predicted by network pharmacology related databases. Bioinformatics analysis and molecular docking technology were used to predict the mechanism of TSPJs in the treatment of RA. The predicted results were validated by cell experiments and a collagen-induced arthritis (CIA) mouse model. RESULTS: Bioinformatics analysis results showed that TSPJs may inhibit RA-related angiogenesis through the hypoxia-inducible factor-1 (HIF-1) and vascular endothelial growth factor (VEGF) pathways. In vitro, different doses of TSPJs showed a good inhibitory effect on the tube formation of EA.hy926 cells. The results of the cellular thermal shift assay indicated that TSPJs can bind to the HIF-1α, VEGFA, and angiopoietin-1 (ANG-1) proteins. In vivo, the administration of TSPJs alleviated the symptoms of CIA mice, including the arthritis index, hind paw thickness, and swollen joint count. The histological results demonstrated that TSPJs inhibited inflammation, angiogenesis, bone damage, and cartilage destruction. Furthermore, TSPJs decreased the number of vessels and the expression level of CD31. The mechanistic results revealed that TSPJs decreased the expression of HIF-1α, VEGFA, and ANG-1 in the serum or synovial tissues of CIA mice. CONCLUSION: These results suggest that TSPJs effectively inhibit angiogenesis in RA, and the mechanism may be related to inhibiting the HIF-1α/VEGF/ANG-1 axis.

Potential for Recycling Metakaolin/Slag-Based Geopolymer Concrete of Various Strength Levels in Freeze-Thaw Conditions.

Geopolymer concrete (GPC) represents an innovative green and low-carbon construction material, offering a viable alternative to ordinary Portland cement concrete (OPC) in building applications. However, existing studies tend to overlook the recyclability aspect of GPC for future use. Various structural applications necessitate the use of concrete with distinct strength characteristics. The recyclability of the parent concrete is influenced by these varying strengths. This study examined the recycling potential of GPC across a spectrum of strength grades (40, 60, 80, and 100 MPa, marked as C40, C60, C80, and C100) when subjected to freeze-thaw conditions. Recycling 5-16 mm recycled geopolymer coarse aggregate (RGAs) from GPC prepared from 5 to 16 mm natural coarse aggregates (NAs). The cementitious material comprised 60% metakaolin and 40% slag, with natural gravel serving as the NAs, and the alkali activator consisting of sodium hydroxide solution and sodium silicate solution. The strength of the GPC was modulated by altering the Na/Al ratio. After 350 freeze-thaw cycles, the GPC specimens underwent crushing, washing, and sieving to produce RGAs. Subsequently, their physical properties (apparent density, water absorption, crushing index, and attached mortar content and microstructure (microhardness, SEM, and XRD) were thoroughly examined. The findings indicated that GPC with strength grades of C100, C80, and C60 were capable of enduring 350 freeze-thaw cycles, in contrast to C40, which did not withstand these conditions. RGAs derived from GPC of strength grades C100 and C80 complied with the criteria for Class II recycled aggregates, whereas RGAs produced from GPC of strength grade C60 aligned with the Class III level. A higher-strength grade in the parent concrete correlated with enhanced performance characteristics in the resulting recycled aggregates.

Nomogram Based on High-Resolution Vessel Wall MRI Features to Differentiate Moyamoya Disease From Atherosclerosis-Associated Moyamoya Vasculopathy.

BACKGROUND: The angiographic features of moyamoya disease (MMD) and atherosclerosis-associated moyamoya vasculopathy (AS-MMV) are similar, but the etiology and clinical treatment strategies are different. Differentiating MMD from AS-MMV helps to choose the appropriate treatment. PURPOSE: To investigate the feasibility of a nomogram based on high-resolution vessel wall (HR-VWI) MRI features to differentiate MMD from AS-MMV. STUDY TYPE: Retrospective. SUBJECTS: One hundred and two patients with MMD (N = 52) or AS-MMV (N = 50) in the training cohort (9-72 years; 54 females) and 70 patients with MMD (N = 42) or AS-MMV (N = 28) in the validation cohort (7-69 years; 33 females). FIELD STRENGTH/SEQUENCE: 3-T, three-dimensional time-of-flight MR angiography (3D-TOF-MRA), spin echo high-resolution 3D T1-weighted imaging (3D-T1WI), 3D T2-weighted imaging (3D-T2WI), and contrast-enhanced 3D-T1WI. ASSESSMENT: Image assessment was performed by three neuroradiologists (with 10, 15, and 18 years of experience). Demographic characteristic and image features were evaluated and compared. Independent factors of MMD were screened to construct a nomogram model in the training cohort. The validation cohort was used to validated its generality. STATISTICAL TESTS: Interclass correlation coefficient (ICC), kappa, t-test, χ2 test, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve and concordance index (C-index). A P-value <0.05 was considered statistically significant. RESULTS: Significant differences were observed between MMD and AS-MMV in terms of age, vessel outer diameter, vessel wall thickening pattern, maximum thickness, dot sign, and anterior cerebral artery (ACA) involved. Age, outer diameter, dot sign, and ACA involved were independent factors. The C-index was 0.886 in the training cohort and 0.859 in the validation cohort. The ROC demonstrated high diagnostic efficacy with an AUC of 0.884 in the training cohort and 0.857 in the validation cohort. DATA CONCLUSION: A nomogram model based on age, vessel outer diameter, dot sign and ACA involved may effectively distinguish MMD from AS-MMV with good reliability and accuracy. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Identification and Verification of Endoplasmic Reticulum Stress-Related Genes as Novel Signatures for Osteoarthritis Diagnosis and Therapy: A Bioinformatics Analysis-Oriented Pilot Study.

BACKGROUND AND PURPOSE: Endoplasmic reticulum stress (ERS) has been reported to be closely associated with the development of osteoarthritis (OA), but the underlying mechanisms are not fully delineated. The present study was designed to investigate the involvement of ERS-related genes in regulating OA progression. METHODS: The expression profiles of OA patients and normal people were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) in datasets GSE55457 and GSE55235 were screened and identified by R software with the construction of the protein-protein interaction (PPI) networks. Through the STRING and Venn diagram analysis, hub ERS-related genes were obtained. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed. Biomarkers with high diagnostic values of osteoarthritis (OA) were studied. The hematoxylin and eosin (H&E) staining and micro-CT were applied to evaluate the establishment of the OA model. The expression levels of biomarkers were validated with the use of reverse transcription­quantitative polymerase chain reaction (RT-qPCR) and western blot. Finally, we evaluated the correlations of hub ERS-related genes with the immune infiltration cells via the CIBERSORT algorithm. RESULTS: A total of 60 downregulated and 52 upregulated DEGs were identified, and the following GO and KEGG pathway analyses verified that those DEGs were mainly enriched in biological process (BP), cellular component (CC), molecular function (MF), and inflammation-associated signal pathways. Interestingly, among all the DEGs, six ER stress-associated genes, including activating transcription factor 3 (ATF3), DEAD-Box Helicase 3 X-Linked (DDX3X), AP-1 transcription factor subunit (JUN), eukaryotic initiation factor 4 (EIF4A1), KDEL endoplasmic reticulum protein retention receptor 3 (KDELR3), and vascular endothelial growth factor A (VEGFA), were found to be closely associated with OA progression, and the following RT-qPCR and Western Blot analysis confirmed that DDX3X, JUN, and VEGFA were upregulated, whereas KDELR3, EIF4A1, and ATF3 were downregulated in OA rats tissues compared to the normal tissues, which were in accordance with our bioinformatics findings. Furthermore, our receiver operating characteristic (ROC) curve analysis verified that the above six ER stress-associated genes could be used as ideal biomarkers for OA diagnosis and those genes also potentially regulated immune responses by influencing the biological functions of mast cells and macrophages. CONCLUSION: Collectively, the present study firstly identified six ER stress-associated genes (ATF3, DDX3X, JUN, EIF4A1, KDELR3, and VEGFA) that may play critical role in regulating the progression of OA.

Silver Nanoparticles Encapped by Dihydromyricetin: Optimization of Green Synthesis, Characterization, Toxicity, and Anti-MRSA Infection Activities for Zebrafish (Danio rerio).

To achieve the environmentally friendly and rapid green synthesis of efficient and stable AgNPs for drug-resistant bacterial infection, this study optimized the green synthesis process of silver nanoparticles (AgNPs) using Dihydromyricetin (DMY). Then, we assessed the impact of AgNPs on zebrafish embryo development, as well as their therapeutic efficacy on zebrafish infected with Methicillin-resistant Staphylococcus aureus (MRSA). Transmission electron microscopy (TEM) and dynamic light-scattering (DLS) analyses revealed that AgNPs possessed an average size of 23.6 nm, a polymer dispersity index (PDI) of 0.197 ± 0.0196, and a zeta potential of -18.1 ± 1.18 mV. Compared to other published green synthesis products, the optimized DMY-AgNPs exhibited smaller sizes, narrower size distributions, and enhanced stability. Furthermore, the minimum concentration of DMY-AgNPs required to affect zebrafish hatching and survival was determined to be 25.0 µg/mL, indicating the low toxicity of DMY-AgNPs. Following a 5-day feeding regimen with DMY-AgNP-containing food, significant improvements were observed in the recovery of the gills, intestines, and livers in MRSA-infected zebrafish. These results suggested that optimized DMY-AgNPs hold promise for application in aquacultures and offer potential for further clinical use against drug-resistant bacteria.

Anode-Free Aqueous Aluminum Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) possess the advantages of high safety, cost-effectiveness, eco-friendliness and high theoretical capacity. However, the Al2O3 film on the Al anode surface, a natural physical barrier to the plating of hydrated aluminum ions, is a key factor in the decomposition of the aqueous electrolyte and the severe hydrogen precipitation reaction. To circumvent the obnoxious Al anode, a proof-of-concept of an anode-free AAIB is first proposed, in which Al2TiO5, as a cathode pre-aluminum additive (Al source), can replenish Al loss by over cycling. The Al-Cu alloy layer, formed by plating Al on the Cu foil surface during the charge process, possesses a reversible electrochemical property and is paired with a polyaniline (cathode) to stimulate the battery to exhibit high initial discharge capacity (175 mAh g-1), high power density (≈410 Wh L-1) and ultra-long cycle life (4000 cycles) with the capacity retention of ≈60% after 1000 cycles. This work will act as a primer to ignite the enormous prospective researches on the anode-free aqueous Al ion batteries.

Artificial intelligence-based prognostic model accurately predicts the survival of patients with diffuse large B-cell lymphomas: analysis of a large cohort in China.

BACKGROUND: Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include molecular data. Therefore, we developed a widely applicable novel scoring system with molecular indicators screened by artificial intelligence (AI) that achieves accurate prognostic stratification and promotes individualized treatments. METHODS: We retrospectively enrolled a cohort of 401 patients with DLBCL from our hospital, covering the period from January 2015 to January 2019. We included 22 variables in our analysis and assigned them weights using the random survival forest method to establish a new predictive model combining bidirectional long-short term memory (Bi-LSTM) and logistic hazard techniques. We compared the predictive performance of our "molecular-contained prognostic model" (McPM) and the IPI. In addition, we developed a simplified version of the McPM (sMcPM) to enhance its practical applicability in clinical settings. We also demonstrated the improved risk stratification capabilities of the sMcPM. RESULTS: Our McPM showed superior predictive accuracy, as indicated by its high C-index and low integrated Brier score (IBS), for both overall survival (OS) and progression-free survival (PFS). The overall performance of the McPM was also better than that of the IPI based on receiver operating characteristic (ROC) curve fitting. We selected five key indicators, including extranodal involvement sites, lactate dehydrogenase (LDH), MYC gene status, absolute monocyte count (AMC), and platelet count (PLT) to establish the sMcPM, which is more suitable for clinical applications. The sMcPM showed similar OS results (P < 0.0001 for both) to the IPI and significantly better PFS stratification results (P < 0.0001 for sMcPM vs. P = 0.44 for IPI). CONCLUSIONS: Our new McPM, including both clinical and molecular variables, showed superior overall stratification performance to the IPI, rendering it more suitable for the molecular era. Moreover, our sMcPM may become a widely used and effective stratification tool to guide individual precision treatments and drive new drug development.