Ameliorative effects of mesenchymal stromal cells on senescence associated phenotypes in naturally aged rats.

BACKGROUND: Aging is a multifaceted process that affects all organ systems. With the increasing trend of population aging, aging-related diseases have resulted in significant medical challenges and socioeconomic burdens. Mesenchymal stromal cells (MSCs), due to their antioxidative stress, immunoregulatory, and tissue repair capabilities, hold promise as a potential anti-aging intervention. METHODS: In this study, we transplanted MSCs into naturally aged rats at 24 months, and subsequently examined levels of aging-related factors such as ß-galactosidase, superoxide dismutase, p16, p21 and malondialdehyde in multiple organs. Additionally, we assessed various aging-related phenotypes in these aged rats, including immune senescence, lipid deposition, myocardial fibrosis, and tissue damage. We also conducted a 16 S ribosomal ribonucleic acid (rRNA) analysis to study the composition of gut microbiota. RESULTS: The results indicated that MSCs significantly reduced the levels of aging-associated and oxidative stress-related factors in multiple organs such as the heart, liver, and lungs of naturally aging rats. Furthermore, they mitigated chronic tissue damage and inflammation caused by aging, reduced levels of liver lipid deposition and myocardial fibrosis, alleviated aging-associated immunodeficiency and immune cell apoptosis, and positively influenced the gut microbiota composition towards a more youthful state. This research underscores the diverse anti-aging effects of MSCs, including oxidative stress reduction, tissue repair, metabolic regulation, and improvement of immune functions, shedding light on the underlying anti-aging mechanisms associated with MSCs. CONCLUSIONS: The study confirms that MSCs hold great promise as a potential anti-aging approach, offering the possibility of extending lifespan and improving the quality of life in the elderly population.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay. METHODS: Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets. RESULTS: The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay. CONCLUSIONS: The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.

BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. METHODS: This study involved 961 patients, with prospective analysis of data from 244 patients with major trauma at our hospital and retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine learning algorithms used to train models included logistic regression (LR), decision tree (DT), extreme gradient boosting machine (eXGBM), neural network (NN), random forest (RF), and light gradient boosting machine (LightGBM). RESULTS: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917-0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883-0.930) and 0.912 (95% CI: 0.880-0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866-0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860-0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751-0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM was emerged as the optimal model, and it was deployed online as an AI application. CONCLUSIONS: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.

Immunotherapy in the context of sepsis-induced immunological dysregulation.

Sepsis is a clinical syndrome caused by uncontrollable immune dysregulation triggered by pathogen infection, characterized by high incidence, mortality rates, and disease burden. Current treatments primarily focus on symptomatic relief, lacking specific therapeutic interventions. The core mechanism of sepsis is believed to be an imbalance in the host's immune response, characterized by early excessive inflammation followed by late immune suppression, triggered by pathogen invasion. This suggests that we can develop immunotherapeutic treatment strategies by targeting and modulating the components and immunological functions of the host's innate and adaptive immune systems. Therefore, this paper reviews the mechanisms of immune dysregulation in sepsis and, based on this foundation, discusses the current state of immunotherapy applications in sepsis animal models and clinical trials.

Advances in the Application of AI Robots in Critical Care: Scoping Review.

BACKGROUND: In recent epochs, the field of critical medicine has experienced significant advancements due to the integration of artificial intelligence (AI). Specifically, AI robots have evolved from theoretical concepts to being actively implemented in clinical trials and applications. The intensive care unit (ICU), known for its reliance on a vast amount of medical information, presents a promising avenue for the deployment of robotic AI, anticipated to bring substantial improvements to patient care. OBJECTIVE: This review aims to comprehensively summarize the current state of AI robots in the field of critical care by searching for previous studies, developments, and applications of AI robots related to ICU wards. In addition, it seeks to address the ethical challenges arising from their use, including concerns related to safety, patient privacy, responsibility delineation, and cost-benefit analysis. METHODS: Following the scoping review framework proposed by Arksey and O'Malley and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a scoping review to delineate the breadth of research in this field of AI robots in ICU and reported the findings. The literature search was carried out on May 1, 2023, across 3 databases: PubMed, Embase, and the IEEE Xplore Digital Library. Eligible publications were initially screened based on their titles and abstracts. Publications that passed the preliminary screening underwent a comprehensive review. Various research characteristics were extracted, summarized, and analyzed from the final publications. RESULTS: Of the 5908 publications screened, 77 (1.3%) underwent a full review. These studies collectively spanned 21 ICU robotics projects, encompassing their system development and testing, clinical trials, and approval processes. Upon an expert-reviewed classification framework, these were categorized into 5 main types: therapeutic assistance robots, nursing assistance robots, rehabilitation assistance robots, telepresence robots, and logistics and disinfection robots. Most of these are already widely deployed and commercialized in ICUs, although a select few remain under testing. All robotic systems and tools are engineered to deliver more personalized, convenient, and intelligent medical services to patients in the ICU, concurrently aiming to reduce the substantial workload on ICU medical staff and promote therapeutic and care procedures. This review further explored the prevailing challenges, particularly focusing on ethical and safety concerns, proposing viable solutions or methodologies, and illustrating the prospective capabilities and potential of AI-driven robotic technologies in the ICU environment. Ultimately, we foresee a pivotal role for robots in a future scenario of a fully automated continuum from admission to discharge within the ICU. CONCLUSIONS: This review highlights the potential of AI robots to transform ICU care by improving patient treatment, support, and rehabilitation processes. However, it also recognizes the ethical complexities and operational challenges that come with their implementation, offering possible solutions for future development and optimization.

Predicting hypovitaminosis C with LASSO algorithm in adult critically ill patients in surgical intensive care units: a bi-center prospective cohort study.

Vitamin C played pleiotropic roles in critical illness and vitamin C insufficiency was predictive of the development of multiple organ failure. Currently, the prevalence of vitamin C insufficiency in Chinese critically ill patients is rarely determined and there are no established bedside tools to predict hypovitaminosis C. To develop a nomogram to identify patients with high risk of hypovitaminosis C, we performed a bi-center prospective cohort study at two ICUs of the first and sixth medical center in PLA General Hospital, Beijing, China from May 6th to July 31st, 2021 We identified 322 eligible patients. 62.4% patients were hypovitaminosis C. 7 features, including source of infection, the level of serum albumin, age, male gender, sepsis, vascular disease, and wasting of vitamin C by the kidney, were selected using LASSO algorithm and therefore included in the nomogram. In the testing set, our model showed moderate discrimination ability with areas under the curve of 0.75 [0.64-0.84]. Variable importance evaluated by SHAP value highlighted two novel important predictors, i.e., abdominal infection and the level of serum albumin. In conclusion, we first reported a high burden of vitamin C insufficiency in Chinese adult patient in the ICU. We also constructed a prediction model to timely identify patients with high risk of hypovitaminosis C, which allows the clinicians to choose appropriate candidates for Vitamin C repletion in clinical practice or clinical trials.

Modulating Electronic Structure of Iridium Single-Atom Anchored on 3D Fe-Doped ß-Ni(OH)2 Catalyst with Nanopyramid Array Structure for Enhanced Oxygen Evolution Reaction.

Developing high-performance electrocatalysts for oxygen evolution reaction (OER) is crucial in the pursuit of clean and sustainable hydrogen energy, yet still challenging. Herein, a spontaneous redox strategy is reported to achieve iridium single-atoms anchored on hierarchical nanosheet-based porous Fe doped ß-Ni(OH)2 pyramid array electrodes (SAs Ir/Fe-ß-Ni(OH)2), which exhibits high OER performance with a low overpotential of 175 mV at 10 mA cm-2 and a remarkable OER current density in alkaline electrolyte, surpassing Fe-ß-Ni(OH)2/NF and IrO2 by 31 and 38 times at 1.43 V versus RHE, respectively. OER catalytic mechanism demonstrates that the conversion of *OH→*O and the active lattice O content can be significantly improved due to the modulation effect of the Ir single atoms on the local electronic structure and the redox behavior of FeNi (oxy) hydroxide true active species. This work provides a promising insight into understanding the OER enhancement mechanism for Ir single-atoms modified FeNi-hydroxide systems.

INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.

ABSTRACT: The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. Retrospective data was extracted from adult patients in the MIMIC-IV database who spent a minimum of 48 h in the ICU. Feature selection was performed using LASSO regression, and the dataset was balanced using the BL-SMOTE approach. Predictive models were built using six machine learning algorithms. The Shapley additive explanation algorithm was used to assess the impact of various clinical features in the optimal model, enhancing interpretability. The study included 26,346 ICU patients, of whom 379 (1.44%) were diagnosed with invasive fungal infection. The predictive model was developed using 20 risk factors, and the dataset was balanced using the borderline-SMOTE (BL-SMOTE) algorithm. The BL-SMOTE random forest model demonstrated the highest predictive performance (area under curve = 0.88, 95% CI = 0.84-0.91). Shapley additive explanation analysis revealed that the three most influential clinical features in the BL-SMOTE random forest model were dialysis treatment, APSIII scores, and liver disease. The machine learning model provides a reliable tool for predicting the occurrence of IFI in ICU patients. The BL-SMOTE random forest model, based on 20 risk factors, exhibited superior predictive performance and can assist clinicians in early assessment of IFI occurrence in ICU patients. Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.

Coupling Ferricyanide/Ferrocyanide Redox Mediated Recycling Spent LiFePO4 with Hydrogen Production.

Replacing the oxygen evolution reaction with thermodynamically more favorable alternative oxidation reactions offers a promising alternative to reduce the energy consumption of hydrogen production. However, questions remain regarding the economic viability of alternative oxidation reactions for industrial-scale hydrogen production. Here, we propose an innovative cost-effective, environment-friendly and energy-efficient strategy for simultaneous recycling of spent LiFePO4 (LFP) batteries and hydrogen production by coupling the spent LFP-assisted ferricyanide/ferrocyanide ([Fe(CN)6 ]4- /[Fe(CN)6 ]3- ) redox reaction. The onset potential for the electrooxidation of [Fe(CN)6 ]4- to [Fe(CN)6 ]3- is low at 0.87 V. Operando Raman and UV/Visible spectroscopy confirm that the presence of LFP in the electrolyte allows for the rapid reduction of [Fe(CN)6 ]3- to [Fe(CN)6 ]4- , thereby completing the [Fe(CN)6 ]4- /[Fe(CN)6 ]3- redox cycle as well as facilitating the conversion of spent LiFePO4 into LiOH ⋅ H2 O and FePO4 . The electrolyzer consumes 3.6 kWh of electricity per cubic meter of H2 produced at 300 mA cm-2 , which is 43 % less than conventional water electrolysis. Additionally, this recycling pathway for spent LFP batteries not only minimizes chemical consumption and prevents secondary pollution but also presents significant economic benefits.

Effect of early hemostasis strategy on secondary post-traumatic sepsis in trauma hemorrhagic patients.

INTRODUCTION: Fibrinogen and platelet, as the two main components of hemostatic resuscitation, are frequently administered in traumatic massive hemorrhage patients. It is reasonable to infer that they may have an impact on post-traumatic sepsis as more and more recognition of their roles in inflammation and immunity. This study aims to determine the association between the fibrinogen/platelet transfusion ratio during the first 24 h after trauma and the risk of the post- traumatic sepsis. METHODS: We analyzed the data from the National Trauma Data Bank (NTDB). Subjects included the critically injured adult patients admitted to Level I/II trauma center from 2013 to 2017 who received fibrinogen and platelet supplementation and more than 10 units (about 4000 ml) packed red blood cells (pRBCs) during the first 24 h after trauma. Two parts of analyses were performed: (1) multivariable stepwise regression was used to determine the variables that influence the risk of post-traumatic sepsis; (2) propensity score matching (PSM), to compare the influences of different transfusion ratio between fibrinogen and platelet on the risk of sepsis and other outcomes after trauma. RESULTS: 8 features were screened out by bi-directional multivariable stepwise logistic regression to predict the post-traumatic sepsis. They are age, sex, BMI, ISSabdomen, current smoker, COPD, Fib4h/24h and Fib/PLT24h. Fib/PLT24h was negatively related to sepsis (p < 0.05). A total of 1601 patients were included in the PSM cohort and grouped by Fib/PLT24h = 0.025 according to the fitting generalized additive model (GAM) model curve. The incidence of sepsis was significantly decreased in the high Fib/PLT group [3.3 % vs 9.4 %, OR = 0.33, 95 %CI (0.17-0.60)]; the length of stay in ICU and mechanical ventilation were both shortened as well [8 (IQR 2.00,17.00) vs 9 (IQR 3.00,19.25), p = 0.006 and 4 (IQR 2.00,10.00) vs 5 (IQR 2.00,14.00), p = 0.003, respectively. CONCLUSIONS: Early and sufficient supplementation of fibrinogen was a convenient way contribute to reduce the risk of sepsis after trauma.

Acute lung injury caused by sepsis: how does it happen?

Sepsis is a systemic inflammatory disease caused by severe infections that involves multiple systemic organs, among which the lung is the most susceptible, leaving patients highly vulnerable to acute lung injury (ALI). Refractory hypoxemia and respiratory distress are classic clinical symptoms of ALI caused by sepsis, which has a mortality rate of 40%. Despite the extensive research on the mechanisms of ALI caused by sepsis, the exact pathological process is not fully understood. This article reviews the research advances in the pathogenesis of ALI caused by sepsis by focusing on the treatment regimens adopted in clinical practice for the corresponding molecular mechanisms. This review can not only contribute to theories on the pathogenesis of ALI caused by sepsis, but also recommend new treatment strategies for related injuries.

Protective and immunomodulatory effects of mesenchymal stem cells on multiorgan injury in male rats with heatstroke.

Heatstroke (HS) causes multiple organ dysfunction syndrome (MODS) with a mortality rate of 60% after hospitalization. Currently, there is no effective and targeted approach for the treatment of HS. Despite growing evidence that mesenchymal stem cells (MSCs) may reduce multiorgan damage and improve survival through immunomodulatory effects in several diseases, no one has tested whether MSCs have immunomodulatory effects in heatstroke. The present study focused on pathological changes and levels of the cytokines and immunoglobulins to investigate the mechanisms underlying the protective effect and the anti-inflammatory effects of MSCs. We found that MSCs treatment significantly reduced the 28-day mortality rate (P < 0.05), the levels of hepatic and renal function markers on day 1 (P < 0.01) and the pathological lesion scores of multiple organs in HS rats. The levels of IgG1, IgM, and IgA of the HS + MSC group was significantly higher than that in HS group on days 3 and 28(P < 0.05). In conclusion, MSCs contribute to protecting against multiorgan injury, reducing pro-inflammatory cytokines, stabilizing immunoglobulins, and reducing the mortality rate of HS rats.

[Mechanism of regulatory T cells in heat stroke-induced acute kidney injury].

OBJECTIVE: To investigate the mechanism of regulatory T cells (Treg) in heat stroke (HS)-induced acute kidney injury (AKI). METHODS: Male SPF Balb/c mice were randomly divided into control group, HS group (HS+Rat IgG), HS+PC61 group, and HS+Treg group (n = 6). The HS mice model was established by making the body temperature of the mice reach 42.7 centigrade at room temperature 39.5 centigrade with relative humidity 60% for 1 hour. In HS+PC61 group, 100 µg PC61 antibody (anti-CD25) was injected through the tail vein in consecutive 2 days before the model was established to eliminate Tregs. Mice in HS+Treg group was injected with 1×106 Treg via tail vein immediately after successful modeling. The proportion of Treg infiltrated in the kidney, serum creatinine (SCr) and histopathology, levels of interferon-γ (IFN-γ) and tumor necrosis factor-α (TNF-α) both in the serum and kidney tissue, as well as proportion of neutrophils and macrophages located in the kidney were observed at 24 hours after HS. RESULTS: HS dampened renal function and exaggerated kidney injury, up-regulated levels of inflammatory cytokines both in local kidney and circulation, and increased infiltration of neutrophils and macrophages to the injured kidneys. The proportion of Treg (Treg/CD4+) infiltrated in kidney was significantly decreased in HS group, compared with control group [(3.40±0.46)% vs. (7.67±0.82)%, P < 0.01]. Compared with HS group, local Tregs in kidney were almost completely depleted via PC61 antibody [(0.77±0.12)% vs. (3.40±0.46)%, P < 0.01]. Depletion of Tregs could exacerbate HS-AKI, indicating by increased serum creatinine [SCr (mmol/L): 348.22±35.36 vs. 254.42±27.40, P < 0.01] and pathological injury (Paller score: 4.70±0.20 vs. 3.60±0.20, P < 0.01), incremental levels of IFN-γand TNF-α both in injured kidney and serum [serum IFN-γ (ng/L): 747.70±64.52 vs. 508.46±44.79, serum TNF-α (ng/L): 647.41±26.62 vs. 464.53±41.80, both P < 0.01], and more infiltrated neutrophils and macrophages in the injured kidney [neutrophil proportion: (6.63±0.67)% vs. (4.37±0.43)%, macrophage proportion: (38.70±1.66)% vs. (33.19±1.55)%, both P < 0.01]. On the contrast, adoptive transfer of Tregs could reverse the aforementioned effects of Treg depletion, indicating by incremental proportion of Tregs in the injured kidney [(10.58±1.19)% vs. (3.40±0.46)%, P < 0.01], decreased serum creatinine [SCr (mmol/L): 168.24±40.56 vs. 254.42±27.40, P < 0.01] and pathological injury (Paller score: 2.73±0.11 vs. 3.60±0.20, P < 0.01), reduced levels of IFN-γ and TNF-α both in injured kidney and serum [serum IFN-γ (ng/L): 262.62±22.68 vs. 508.46±44.79, serum TNF-α (ng/L): 206.41±22.58 vs. 464.53±41.80, both P < 0.01], and less infiltrated neutrophils and macrophages in the injured kidney [neutrophil proportion: (3.04±0.33)% vs. (4.37±0.43)%, macrophage proportion: (25.68±1.93)% vs. (33.19±1.55)%, both P < 0.01]. CONCLUSIONS: Treg might be involved in HS-AKI, possibly via down-regulation of pro-inflammatory cytokines and infiltration of inflammatory cells.

Single-cell transcriptome profiling of sepsis identifies HLA-DRlowS100Ahigh monocytes with immunosuppressive function.

BACKGROUND: Sustained yet intractable immunosuppression is commonly observed in septic patients, resulting in aggravated clinical outcomes. However, due to the substantial heterogeneity within septic patients, precise indicators in deciphering clinical trajectories and immunological alterations for septic patients remain largely lacking. METHODS: We adopted cross-species, single-cell RNA sequencing (scRNA-seq) analysis based on two published datasets containing circulating immune cell profile of septic patients as well as immune cell atlas of murine model of sepsis. Flow cytometry, laser scanning confocal microscopy (LSCM) imaging and Western blotting were applied to identify the presence of S100A9+ monocytes at protein level. To interrogate the immunosuppressive function of this subset, splenic monocytes isolated from septic wild-type or S100a9-/- mice were co-cultured with naïve CD4+ T cells, followed by proliferative assay. Pharmacological inhibition of S100A9 was implemented using Paquinimod via oral gavage. RESULTS: ScRNA-seq analysis of human sepsis revealed substantial heterogeneity in monocyte compartments following the onset of sepsis, for which distinct monocyte subsets were enriched in disparate subclusters of septic patients. We identified a unique monocyte subset characterized by high expression of S100A family genes and low expression of human leukocyte antigen DR (HLA-DR), which were prominently enriched in septic patients and might exert immunosuppressive function. By combining single-cell transcriptomics of murine model of sepsis with in vivo experiments, we uncovered a similar subtype of monocyte significantly associated with late sepsis and immunocompromised status of septic mice, corresponding to HLA-DRlowS100Ahigh monocytes in human sepsis. Moreover, we found that S100A9+ monocytes exhibited profound immunosuppressive function on CD4+ T cell immune response and blockade of S100A9 using Paquinimod could partially reverse sepsis-induced immunosuppression. CONCLUSIONS: This study identifies HLA-DRlowS100Ahigh monocytes correlated with immunosuppressive state upon septic challenge, inhibition of which can markedly mitigate sepsis-induced immune depression, thereby providing a novel therapeutic strategy for the management of sepsis.

Synthesis and biological evaluation of N-(3-fluorobenzyl)-4-(1-(methyl-d3)-1H-indazol-5-yl)-5-(6-methylpyridin-2-yl)-1H-imidazol-2-amine as a novel, potent ALK5 receptor inhibitor.

Specific inhibition of ALK5 provides a novel method for controlling the development of cancers and fibrotic diseases. In this work, a novel series of N-(3-fluorobenzyl)-4-(1-(methyl-d3)-1H-indazol-5-yl)-5-(6-methylpyridin-2-yl)-1H-imidazol-2-amine (11), a potential clinical candidate, was synthesized by strategic incorporation of deuterium at potential metabolic soft spots and identified as ALK5 inhibitors. This compound has a low potential for CYP-mediated drug-drug interactions as a CYP450 inhibitor (IC50 = >10 µM) and showed potent inhibitory effects in cellular assay (IC50 = 3.5 ± 0.4 nM). The pharmacokinetic evaluation of 11 in mice demonstrated moderate clearance (29.0 mL/min/kg) and also revealed high oral bioavailability in mice (F = 67.6%).

Bi-Functional Co/Al Modified 1T-MoS2 /rGO Catalyst for Enhanced Uranium Extraction and Hydrogen Evolution Reaction in Seawater.

Uranium is a key element in the preparation of nuclear fuel. An electrochemical uranium extraction technique is proposed to achieve high efficiency uranium extraction performance through HER catalyst. However, it is still a challenge to design and develop a high-performance hydrogen evolution reaction (HER) catalyst for rapid extraction and recovery of uranium from seawater. Herein, a bi-functional Co, Al modified 1T-MoS2 /reduced graphene oxide (CA-1T-MoS2 /rGO) catalyst, showing a good HER performance with a HER overpotential of 466 mV at 10 mA cm-2 in simulated seawater, is first developed. Benefiting from the high HER performance of CA-1T-MoS2 /rGO, efficient uranium extraction is achieved with a uranium extraction capacity of 1990 mg g-1 in simulated seawater without post-treatment, exhibiting a good reusability. The results of experiments and density functional theory (DFT) show that a high uranium extraction and recovery capability is attributed to the synergy effect of the improved HER performance and the strong adsorption capacity between U and OH*. This work provides a new strategy for the design and preparation of bi-functional catalysts with high HER performance and uranium extraction and recovery capabilities in seawater.

Mesenchymal stem cell-derived exosomes regulate microglia phenotypes: a promising treatment for acute central nervous system injury.

There is growing evidence that long-term central nervous system (CNS) inflammation exacerbates secondary deterioration of brain structures and functions and is one of the major determinants of disease outcome and progression. In acute CNS injury, brain microglia are among the first cells to respond and play a critical role in neural repair and regeneration. However, microglial activation can also impede CNS repair and amplify tissue damage, and phenotypic transformation may be responsible for this dual role. Mesenchymal stem cell (MSC)-derived exosomes (Exos) are promising therapeutic agents for the treatment of acute CNS injuries due to their immunomodulatory and regenerative properties. MSC-Exos are nanoscale membrane vesicles that are actively released by cells and are used clinically as circulating biomarkers for disease diagnosis and prognosis. MSC-Exos can be neuroprotective in several acute CNS models, including for stroke and traumatic brain injury, showing great clinical potential. This review summarized the classification of acute CNS injury disorders and discussed the prominent role of microglial activation in acute CNS inflammation and the specific role of MSC-Exos in regulating pro-inflammatory microglia in neuroinflammatory repair following acute CNS injury. Finally, this review explored the potential mechanisms and factors associated with MSC-Exos in modulating the phenotypic balance of microglia, focusing on the interplay between CNS inflammation, the brain, and injury aspects, with an emphasis on potential strategies and therapeutic interventions for improving functional recovery from early CNS inflammation caused by acute CNS injury.

Protective Effects of Mesenchymal Stem Cells Against Central Nervous System Injury in Heat Stroke.

BACKGROUND: Heatstroke (HS) is a serious disease caused by central nervous system (CNS) injuries, such as delirium, convulsion, and coma. Currently, mesenchymal stem cells (MSCs) have demonstrated novel neuroprotective effects; therefore, this research explores the neuroprotective effects and mechanisms of MSCs against HS injury. METHODS: HS rat models were induced in a 40°C and 65% humidity environment until the rectal temperature reached 42°C. The verified HS injury model rats were divided into the HS and MSCs-treated groups. Each rat in the treated group was infused with 1x106 MSCs suspended in 0.3 ml physiological saline via the tail vein. The HS- or MSCs-treated rats were further divided into early-stage (3d) and late-stage (28d). HS rat models were induced by a high-temperature and high-humidity environment at a specific time, the mortality was analyzed, and an automatic biochemical analyzer measured levels of liver and kidney function indicators in the blood. The neurons' morphologic changes were observed through Nissl staining, and neurological deficit scores were performed. Moreover, the levels of inflammatory factors in brain tissue were measured using a multi-cytokine detection platform, and the expression of BDNF, phosphorylated TrkB and P38 were detected by the Western Bolt. RESULTS: MSCs injection significantly reduced mortality and alleviated liver and kidney function. Moreover, the neurological deficit and neuronic edema of the hippocampus caused by HS at 3d and 28d were significantly ameliorated by MSCs administration. Specifically, the injection of MSCs inhibited high levels of interleukin (IL)-1ß, IL-6, tumor necrosis factor-α (TNF-α), and IL-17A caused by HS but elevated the levels of IL-10 and IL-13 in the early period (3d); while in the later period (28d), MSCs significantly increased the levels of IL-10 and IL-13 continuously and inhibited the high level of IL-17A. Furthermore, MSCs injection increased the expressions of BDNF and phosphorylated TrkB (BDNF receptor), meanwhile inhibiting the expression of phosphorylated P38 (inflammatory factor) in the brains of HS rats in the early period (3d) but had no significant influence on the later period (28d). CONCLUSION: These results suggested that MSCs injection may provide therapeutic effects for HS in rats by improving liver and kidney function and reducing CNS damage. Moreover, MSCs injection inhibited the brain inflammatory response of HS rats, and the BDNF-TrkB and P38/MAPK signal pathways may be involved, providing a potential mechanism for HS therapy by MSCs administration.

Desmoplastic small round cell tumor cancer stem cell-like cells resist chemotherapy but remain dependent on the EWSR1-WT1 oncoprotein.

Desmoplastic Small Round Cell Tumor (DSRCT) is a rare and aggressive pediatric cancer driven by the EWSR1-WT1 fusion oncogene. Combinations of chemotherapy, radiation and surgery are not curative, and the 5-years survival rate is less than 25%. One potential explanation for refractoriness is the existence of a cancer stem cell (CSC) subpopulation able escape current treatment modalities. However, no study to-date has examined the role of CSCs in DSRCT or established in vitro culture conditions to model this subpopulation. In this study, we investigated the role of stemness markers in DSRCT survival and metastasis, finding that elevated levels of SOX2 and NANOG are associated with worse survival in sarcoma patients and are elevated in metastatic DSRCT tumors. We further develop the first in vitro DSRCT CSC model which forms tumorspheres, expresses increased levels of stemness markers (SOX2, NANOG, KLF4, and OCT4), and resists doxorubicin chemotherapy treatment. This model is an important addition to the DSRCT tool kit and will enable investigation of this critical DSRCT subpopulation. Despite lower sensitivity to chemotherapy, the DSRCT CSC model remained sensitive to knockdown of the EWSR1-WT1 fusion protein, suggesting that future therapies directed against this oncogenic driver have the potential to treat both DSRCT bulk tumor and CSCs.

Geriatric Nutritional Risk Index is Associated with Hospital Death in Elderly Patients with Multiple Organ Dysfunction Syndrome: A Retrospective Study Based on the MIMIC-III Database.

Purpose: Elderly patients with multiple organ dysfunction syndrome (MODS) have a higher mortality during hospitalization in the intensive care unit (ICU). Elderly patients often suffer from malnutrition. On the basis of the MIMIC-III database, this study analyzed the effect of the baseline nutritional status on the death of elderly patients with MODS during hospitalization. Materials and Methods: Elderly patients with MODS were screened out from MIMIC-III 1.4 database. The geriatric nutritional risk index (GNRI) was calculated and used to group patients into: normal nutrition (GNRI > 98) and malnutrition (GNRI ≤ 98) groups. The malnutrition group was divided into mild (92-98), moderate (82-91), and severe (≤81) groups. The differences in the baseline data and the incidence of adverse events between groups were compared. The GAM model was used to determine whether a curve relationship was present between the hospital death of elderly patients with MODS and GNRI and analyze the threshold saturation effect. The multivariate logistic regression was used to calculate the odds ratio (OR) of in-hospital deaths in different GNRI groups. The interaction test was performed to find subgroups with differences. Results: A total of 2456 elderly patients with MODS were enrolled. A total of 1,273 (51.8%) and 1183 (48.2%) patients were in the normal nutrition and malnutrition groups, respectively. The mortality rate of patients in the normal nutrition group during hospitalization was lower than that in the malnutrition group (206/1273 vs. 292/1183, X2 = 27.410, P < 0.001; OR = 0.59, 95% CI: 0.48-0.72). The GAM model fitting analysis showed a threshold saturation effect at GNRI = 92. Adjusted OR values with GNRI ≥ 92 began to change to 1, and GNRI and death had no association. At GNRI < 92, high GNRI related to low risk of death. Subgroup analysis of patients with GNRI < 92 showed that the risk of death in elderly male patients was lower than that of female patients. Conclusion: GNRI is related to the severity of illness in elderly patients with MODS. At GNRI < 92, moderate to severe malnutrition increases the risk of death in elderly patients with MODS during hospitalization.