Study on Preclinical Safety and Toxic Mechanism of Human Umbilical Cord Mesenchymal Stem Cells in F344RG Rats.

Mesenchymal stem cells have made remarkable progress in recent years. Many studies have reported that human umbilical cord mesenchymal stem cells (hUC-MSCs) have no toxicity, but thromboembolism appeared in patients treated with hUC-MSCs. Therefore, people are still worried about the safety of clinical application. The study aims to determine the safety, potential toxic mechanism and biodistribution of hUC-MSCs. F344RG rats were given 5 or 50 million cells/kg of hUC-MSCs by single administration in compliance with Good Laboratory Practice standards. Standard toxicity was performed. RNA sequencing was then performed to explore the potential toxic mechanisms. In parallel, the biodistribution of hUC-MSCs was examined. The dose of 5 million cells/kg hUC-MSCs had no obvious toxicity on symptom, weight, food intake, hematology, serum biochemistry, urine biochemistry, cytokines, and histopathology. However, blood-tinged secretions in the urethral orifice and 20% mortality occurred at 50 million cells/kg. Disseminated intravascular coagulopathy (DIC) is the leading cause of death. hUC-MSCs significantly upregulated complement and coagulation cascade pathways gene expression, resulting in DIC. Besides, hUC-MSCs upregulated fibrinolytic system suppressor genes A2m, Serping1 and Serpinf2. hUC-MSCs survived in rats for less than 28 days, no hUC-MSC was detected in tissues outside the lungs. There was no toxicity in F344RG rats at 5 million cells/kg, but some toxicities were detected at 50 million cells/kg. hUC-MSCs significantly upregulated complement and coagulation cascade pathways, upregulated the expression of fibrinolytic system suppressor genes A2m, Serping1 and Serpinf2, to inhibit fibrinolytic system, caused DIC, which provided a new insight into the toxic mechanism of hUC-MSCs.

Effects of Low-Pressure Systems on Temperature, Humidity, Egg Production, and Feed Utilization Efficiency in Large-Scale Poultry Houses during Summer.

Low-pressure systems (LPSs) are among the most critical weather systems, producing excessive precipitation that causes air temperatures to drop and rise considerably. Acute temperature changes directly affect poultry feed intake (FI) and laying performance. To explore the effects of LPSs on hens, the parameters of air temperature, relative humidity, egg production, and feed utilization efficiency were evaluated during different LPSs in three houses. Results indicated that about 2.8 ± 0.7 d, 2.4 ± 0.5 d, and 2.4 ± 0.5 d before the LPS landfall in houses 1, 2, and 3, respectively, the indoor air temperature started to decrease, with the average decreases being 1.7 °C ± 0.4 °C, 2.4 °C ± 0.6 °C, and 1.8 °C ± 0.4 °C, respectively. Significant differences were observed between different LPSs for reducing indoor air temperature (p < 0.05) in the three houses. In house 1, the egg production rates (EPRs) were decreased by 6.6% and 1.1% when LPSs 1 and 2 landed. The average egg weight (AEW) and FI during the LPS landfall were significantly higher than those before the LPS landfall (p < 0.01). Under successive LPSs landfall in the three houses, the EPRs initially reduced by 3.9%, 4.0%, and 0.5%, respectively, but the second LPS event increased the EPRs by 1.8%, 5.3%, and 1.0%, respectively. Furthermore, the LPS landfall increased the feed conversion ratio (FCRe) in the three houses, all above 2.00. In conclusion, LPSs can reduce heat stress, lower the EPRs, and lead to higher FI, FCRe, and AEW.

G4LDB 3.0: a database for discovering and studying G-quadruplex and i-motif ligands.

Non-canonical nucleic acid structures, such as G-quadruplex (G4) and i-Motif (iM), have garnered significant research interest because of their unique structural properties and biological activities. Thousands of small molecules targeting G4/iM structures have been developed for various chemical and biological applications. In response to the growing interest in G4-targeting ligands, we launched the first G4 Ligand Database (G4LDB) in 2013. Here, we introduce G4LDB 3.0 (http://www.g4ldb.com), an upgraded version featuring extensive enhancements in content and functionality. The new version includes over 4800 G4/iM ligands and approximately 51 000 activity entries. Key upgrades include advanced search capabilities, dynamic knowledge graphs, enhanced data visualization, along with a new dynamic analysis function that automatically displays ligand structure clustering results and chemical space distribution. With these updates, G4LDB 3.0 further evolves into a comprehensive resource and valuable research tool. The significant improvements address the increasing demand for efficient data handling and user experience, highlighting the critical role of G4LDB in advancing research on G-quadruplexes and i-motifs.

[Retracted] Fundamental research on the action mechanism of the 800 nm semiconductor laser on skin blackheads and coarse pores.

[This retracts the article DOI: 10.3892/etm.2016.3937.].

Automated ECL Aptasensing Platform from an Intrarticular Radical Annihilation Route for Distinguishing Glioma Stages.

Nowadays, continuous efforts have been devoted to designing stable and high-efficiency electrochemiluminescence (ECL) emitters as alternatives for tris(2,2'-bipyridine)-ruthenium(II) (Ru(bpy)32+) in medical research. Herein, a novel ECL emitter was obtained by coordinating crystalline covalent triazinyl frameworks (cCTFs) with Ru2+ (termed Ru-cCTFs), which exhibited strong ECL emission by the ligand to metal charge transfer (LMCT) route. After its integration with 4-mercaptopyridine (SH-Py), the resultant SH-Py-Ru-cCTFs achieved 2.3-fold enhancement in the ECL efficiency by employing Ru(bpy)32+ as a standard, which involved a dynamic "intrarticular radical annihilation" ECL pathway. On such foundation, an automated ECL (A-ECL) aptasensor was constructed with an "on-off-on" model and magnetic separation upon linkage of the SH-Py-Ru-cCTFs with streptavidin (SA) magnetic beads (MBs). This automatic assay of miRNA-182 showed a wider linear range from 1.0 to 100.0 fM with a correlation coefficient (R2) of 0.994, a lower limit of detection (LOD) down to 0.28 fM, and faster operation within 41 min. Impressively, this bioassay facilely distinguished the stages of glioma disease from clinical blood samples with high accuracy. Hence, this research sheds light on how to develop advanced ECL luminophores and an automatic method, showing substantial insights into pathogenesis research of gliomas.

Cardiovascular death risk in patients with solid tumors: a population-based study in the United States.

BACKGROUND: Previous studies have focused on the risk of cardiovascular disease (CVD)-specific death in hematological cancers and in some single anatomical tumor sites, there remains a paucity of data on systematic analyses in solid tumors. OBJECTIVE: The objective of this study is to evaluate the distribution, risk, and trends of CVD-specific death in patients with solid tumors. METHODS: A total of 2 679 293 patients with solid tumors diagnosed between 1975 and 2019 were screened from the Surveillance, Epidemiology and End Results (SEER) program across 15 anatomical sites. Standardized mortality ratios (SMRs) and absolute excess risks (AERs) were used to describe the intensity of CVD-specific death, competing risk regression models were used to assess the risk of CVD-specific death, and restricted cubic spline analyses were employed to investigate the potential linear or nonlinear relationship between age and CVD death. RESULTS: CVD-specific death in patients with solid tumors accounted for 48.95% of non-cancer deaths. Compared with the general population, patients with solid tumors had higher SMR and AER of CVD death (SMR: 1.15; AER: 21.12), heart disease-related death (SMR: 1.13; AER: 13.96), and cerebrovascular disease-related death (SMR: 1.20; AER: 4.85). Additionally, the SMR exhibited a decreasing trend with increasing time to diagnosis. Furthermore, a nonlinear relationship was observed between age and CVD-specific death in patients with solid tumors of different systems. CONCLUSION: CVD-specific death accounted for a large proportion of the cause of non-cancer deaths. Patients with solid tumors exhibit an elevated risk of CVD-specific death. Screening for CVD death and optimizing risk management in patients with solid tumors throughout anticancer treatment may be beneficial in preventing CVD death.

Left atrial appendage thrombus is associated with a higher fractal dimension in patients with atrial fibrillation.

PURPOSE: To assess the anatomical complexity of the left atrial appendage (LAA) using fractal dimension (FD) based on cardiac computed tomography angiography (CTA) and the association between LAA FD and LAA thrombosis. MATERIALS AND METHODS: Patients with atrial fibrillation (AF) who underwent both cardiac CTA and transesophageal echocardiography (TEE) between December 2018 and December 2022 were retrospectively analyzed. Patients were categorized into normal (n = 925), circulatory stasis (n = 82), and thrombus groups (n = 76) based on TEE results and propensity score matching (PSM) was performed for subsequent analysis. FD was calculated to quantify the morphological heterogeneity of LAA. Independent risk factors for thrombus were screened using logistic regression. The diagnostic performance of FD and CHA2DS2-VaSc score for predicting thrombus was evaluated using the area under the receiver operating characteristics curve (AUC). RESULTS: LAA FD was higher in the thrombus group (1.61 [1.49, 1.70], P < 0.001) than in the circulatory stasis (1.33 [1.18, 1.47]) and normal groups (1.30 [1.18, 1.42]) both before and after PSM. LAA FD was also an independent risk factor in the thrombus (OR [odds ratio] = 570,861.15 compared to normal, 41,122.87 compared to circulatory stasis; all P < 0.001) and circulatory stasis group (OR = 98.87, P = 0.001) after PSM. The diagnostic performance of LAA FD was significantly better than the CHA2DS2-VaSc score in identifying thrombus. CONCLUSIONS: Patients with high LAA FD are more likely to develop LAA thrombus, and the use of FD provides an effective method for assessing the risk of thrombosis in AF patients, thereby guiding individualized clinical treatment.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

MOTIVATION: A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule. RESULTS: We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule. AVAILABILITY AND IMPLEMENTATION: The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

Linking industrial emissions and dietary exposure to human burdens of polychlorinated naphthalenes.

Relationships between toxic pollutant emissions during industrial processes and toxic pollutant dietary intakes and adverse health burdens have not yet been quantitatively clarified. Polychlorinated naphthalenes (PCNs) are typical industrial pollutants that are carcinogenic and of increasing concern. In this study, we established an interpretable machine learning model for quantifying the contributions of industrial emissions and dietary intakes of PCNs to health effects. We used the SHapley Additive exPlanations model to achieve individualized interpretability, enabling us to evaluate the specific contributions of individual feature values towards PCNs concentration levels. A strong relationship between PCN dietary intake and body burden was found using a robust large-scale PCN diet survey database for China containing the results of the analyses of 17,280 dietary samples and 4480 breast milk samples. Industrial emissions and dietary intake contributed 12 % and 52 %, respectively, of the PCN burden in breast milk. The model quantified the contributions of food consumption and industrial emissions to PCN exposure, which will be useful for performing accurate health risk assessments and developing reduction strategies of PCNs.

LIFR regulates cholesterol-driven bidirectional hepatocyte-neutrophil cross-talk to promote liver regeneration.

Liver regeneration is under metabolic and immune regulation. Despite increasing recognition of the involvement of neutrophils in regeneration, it is unclear how the liver signals to the bone marrow to release neutrophils after injury and how reparative neutrophils signal to hepatocytes to reenter the cell cycle. Here we report that loss of the liver tumour suppressor Lifr in mouse hepatocytes impairs, whereas overexpression of leukaemia inhibitory factor receptor (LIFR) promotes liver repair and regeneration after partial hepatectomy or toxic injury. In response to physical or chemical damage to the liver, LIFR from hepatocytes promotes the secretion of cholesterol and CXCL1 in a STAT3-dependent manner, leading to the efflux of bone marrow neutrophils to the circulation and damaged liver. Cholesterol, via its receptor ERRα, stimulates neutrophils to secrete hepatocyte growth factor to accelerate hepatocyte proliferation. Altogether, our findings reveal a LIFR-STAT3-CXCL1-CXCR2 axis and a LIFR-STAT3-cholesterol-ERRα-hepatocyte growth factor axis that form bidirectional hepatocyte-neutrophil cross-talk to repair and regenerate the liver.

Efficacy and challenges of anti-PD1 in MSI-H mCRC: a case report on concurrent infections and ir-AIHA.

Anti-programmed cell death protein 1 (PD-1) therapy has demonstrated notable efficacy in treating patients with deficient mismatch repair/high microsatellite instability (dMMR/MSI-H) metastatic colorectal cancer (mCRC). However, its clinical application is fraught with challenges and can lead to significant immune-related adverse events (ir-AEs). In this report, we present a complicated case of an mCRC patient with MSI-H and mutations in ß2M and LRP1B proteins, complicated by concurrent bacteremia and liver fluke infection, who received first-line anti-PD1 therapy. The patient exhibited a positive response to anti-PD1 treatment, even in the presence of concomitant antibiotic and anti-parasitic interventions. Additionally, the patient experienced immunotherapy-related autoimmune hemolytic anemia (ir-AIHA), a rare hematological ir-AE, which was effectively treated later on. Immunotherapy represents a pivotal and highly effective approach to tumor treatment. Baseline assessment of the MMR and MSI status is a crucial step before initiating immunotherapy, and regular ongoing assessments during the treatment course can facilitate early recognition of any secondary complications, enabling prompt intervention and ensuring optimal therapeutic outcomes. Overall, a multidisciplinary diagnostic and therapeutic algorithm can help maximize the therapeutic benefits of immunotherapy.

An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.

Modern nanotechnology has generated numerous datasets from in vitro and in vivo studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, we report a nanoinformatics platform that accurately annotates nanostructures into machine-readable data files and provides modeling toolkits. This platform, accessible to the public at https://vinas-toolbox.com/, has annotated nanostructures of 14 material types. The associated nanodescriptor data and assay test results are appropriate for modeling purposes. The modeling toolkits enable data standardization, data visualization, and machine learning model development to predict properties and bioactivities of new nanomaterials. Moreover, a library of virtual nanostructures with their predicted properties and bioactivities is available, directing the synthesis of new nanomaterials. This platform provides a data-driven computational modeling platform for the nanoscience community, significantly aiding in the development of safe and effective nanomaterials.

Revealing Enhanced Optical Nonlinearity and Robust Ferromagnetism in Atomically Sharp Stacking Binary-Ternary Magnetic Heterostructures.

Two-dimensional (2D) materials provide a versatile platform for the integration of diverse crystals, enabling the formation of heterostructures with intriguing functionalities. Coherently growing 2D heterostructures are highly desirable for property manipulation due to their strong interfacial interaction. In this work, we propose a general synthesis approach and provide insight into well-designed 2D binary-ternary magnetic heterostructures. Atomically sharp interfaces were achieved in typical lateral and vertical Cr1+mSe2(001)/CuCr2Se4(111) heterostructures owing to their similar lattice arrangement, with the observation of a significant enhancement of optical second-harmonic generation. Further magnetism measurements revealed a Curie temperature up to 360 K and thickness- and temperature-dependent magnetism in this heterostructure. Additionally, we synthesized three analogous 2D magnetic heterostructures in Fe-Cr-S, Co-Cr-S, and Cu-Cr-S systems, demonstrating the ubiquitous nature of the coherent heteroepitaxy. Our work involves the development of an innovative platform for investigating the underlying physics and potential applications of 2D binary-ternary heterostructures as well as the fabrication of associated functional devices.

Savolitinib conferred sensitivity in a patient with D1228H mutation-induced capmatinib-resistant MET exon 14 skipping mutated lung adenocarcinoma.

Traditionally, the D1228 E/G/H/N mutation has been thought to cause Type I MET-TKI resistance. We reported a 75-year-old female with non-small cell lung cancer harboring MET exon 14 skipping mutation, who developed acquired MET D1228H mutation induced by capmatinib treatment. Interestingly, the patient demonstrated marked response to second-line savolitinib treatment with the duration of response of 19 months and several additional metastatic lesions appeared. Pathological assessment of rebiopsy sample showed adenocarcinoma and targeted next-generation sequencing revealed the loss of MET D1228H mutation and presence of MET p.Y1230N mutation. In response, the treatment regimen was amended to include a daily administration of 60 mg of cabozantinib, which resulted in moderate size reduction of the tumours. The switch of resistance mutations indicated that different type Ib MET inhibitors may exhibit distinct mechanisms of resistance. We call for futher studies on resistance based on patient-derived pre-clinical models including patient-derived tumor-like cell clusters, patient-derived organoids, and patient-derived xenografts.

Driving Efficiency: Leveraging Model-Informed Approaches in 505(b)(2) Regulatory Actions.

Introduced by the Hatch-Waxman Amendments of 1984, 505(b)(2) applications permit the US Food and Drug Administration to rely, for approval of a new drug application, on information from studies not conducted by or for the applicant and for which the applicant has not obtained a right of reference. This pathway is designed to circumvent the unnecessary duplication of studies already conducted on a previously approved drug. It can lead to a considerably more efficient and expedited route to approval compared to a traditional development path. Model-informed drug development refers to the utilization of a diverse array of quantitative models in drug development to streamline the decision-making process. In this approach, diverse quantitative models that integrate knowledge of physiology, disease processes, and drug pharmacology are employed to address drug development challenges and guide regulatory decisions. Integration of these model-informed approaches into 505(b)(2) regulatory submissions and decision-making can further expedite the approval of new drugs. This article discusses some applications of model-informed approaches that were used to support 505(b)(2) drug development and regulatory actions. Specifically, various quantitative models such as population pharmacokinetic and exposure-response models have been employed to provide evidence of effectiveness, guide dosing in subgroups such as subjects with hepatic or renal impairment, and inform policies. These case study examples collectively underscore the significance of model-informed approaches in drug development and regulatory decisions associated with 505(b)(2) submissions.

Knowledge, attitude and practice regarding constipation in pregnancy among pregnant women in Shanghai: a cross-sectional study.

Objective: This study aims to investigate the Knowledge, Attitude, and Practice (KAP) pertaining to constipation during pregnancy among pregnant women in Shanghai. Methods: Demographic data and KAP scores were collected using a questionnaire. Differences across groups were analyzed using either Wilcoxon-Mann-Whitney tests or Kruskal-Wallis analysis of variance. Spearman's correlation analysis was utilized to evaluate the relationships between KAP scores. Multivariable logistic regression analyses were conducted to identify factors that influence KAP scores. Results: Encompassing 241 individuals (46.6%) aged between 30 and 34 years, with 349 participants (67.5%) being nulliparous. The median scores for knowledge (possible range: 0-26), attitude (possible range: 7-35), and practice (possible range: 14-70) were 22 (18, 24), 26 (23, 29), and 51 (46, 56), respectively. Multivariate analysis indicated that being a medical professional (OR = 2.222, p = 0.043) and receiving education on constipation during pregnancy (OR = 0.432, p < 0.001) were significantly associated with higher knowledge scores. Factors significantly associated with practice included being aged 30-34 years (OR = 2.745, p < 0.001), aged 35 years and above (OR = 2.514, p < 0.001), working in education (OR = 2.310, p = 0.012), and not experiencing constipation before pregnancy (OR = 1.894, p = 0.001). Conclusion: Pregnant women demonstrated satisfactory knowledge, positive attitudes, and proactive practices concerning constipation during pregnancy. To further augment clinical practice, healthcare providers should tailor educational interventions and guidance specifically for pregnant women who are not medical professionals and those who have not received education and guidance related to constipation during pregnancy.

Novel Two-Terminal Synapse/Neuron Based on an Antiferroelectric Hafnium Zirconium Oxide Device for Neuromorphic Computing.

Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.