Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

Perovskite materials have been one of the most important research objects in materials science due to their excellent photoelectric properties as well as correspondingly complex structures. Machine learning (ML) methods have been playing an important role in the design and discovery of perovskite materials, while feature selection as a dimensionality reduction method has occupied a crucial position in the ML workflow. In this review, we introduced the recent advances in the applications of feature selection in perovskite materials. First, the development tendency of publications about ML in perovskite materials was analyzed, and the ML workflow for materials was summarized. Then the commonly used feature selection methods were briefly introduced, and the applications of feature selection in inorganic perovskites, hybrid organic-inorganic perovskites (HOIPs), and double perovskites (DPs) were reviewed. Finally, we put forward some directions for the future development of feature selection in machine learning for perovskite material design.

Cooperative Characterization of In Situ TEM and Cantilever-TGA to Optimize Calcination Conditions of MnO2 Nanowire Precursors

Calcination plays a vital role during material preparation. However, the calcination conditions have often been determined empirically or have been based on trial and error. Herein we present a cooperative characterization approach to optimize calcination conditions by gas-cell in situ TEM in collaboration with microcantilever-based thermogravimetric analysis (cantilever-TGA) techniques. The morphological evolution of precursors under atmospheric conditions is observed with in situ TEM, and the right calcination temperature is provided by cantilever-TGA. The proposed approach successfully optimizes the calcination conditions of fragile MnO₂ nanowire precursors with multiple valence products. The cantilever-TGA shows that a calcination temperature above 560 °C is required to transform the MnO₂ precursor to Mn₃O₄ under an N₂ atmosphere, but the in situ TEM indicates that the nanowire structure is destroyed within only 30 min under calcination conditions. Our method further suggests that heating the precursor at 400 °C using an H₂-containing atmosphere can produce Mn₃O₄ nanowires with good electrical properties.

Functionally Collaborative Nanostructure for Direct Monitoring of Neurotransmitter Exocytosis in Living Cells

Neurotransmitter exocytosis of living cells plays a vital role in neuroscience. However, the available amperometric technique with carbon fiber electrodes typically measures exocytotic events from one cell during one procedure, which requires professional operations and takes time to produce statistical results of multiple cells. Here, we develop a functionally collaborative nanostructure to directly measure the neurotransmitter dopamine (DA) exocytosis from living rat pheochromocytoma (PC12) cells. The functionally collaborative nanostructure is constructed of metal-organic framework (MOF)-on-nanowires-on-graphene oxide, which is highly sensitive to DA molecules and enables direct detection of neurotransmitter exocytosis. Using the microsensor, the exocytosis from PC12 cells pretreated with the desired drugs (e.g., anticoronavirus drug, antiflu drug, or anti-inflammatory drug) has been successfully measured. Our achievements demonstrate the feasibility of the functionally collaborative nanostructure in the real-time detection of exocytosis and the potential applicability in the highly efficient assessment of the modulation effects of medications on exocytosis.

1ppm-detectable hydrogen gas sensors by using highly sensitive P+/N+ single-crystalline silicon thermopiles

Hydrogen (H₂) is currently of strategic importance in the pursuit of a decarbonized, environmentally benign, sustainable global energy system; however, the explosive nature of H₂ requires leakage monitoring to ensure safe application in industry. Therefore, H₂ gas sensors with a high sensitivity and fast response across a wide concentration range are crucial yet technically challenging. In this work, we demonstrate a new type of MEMS differential thermopile gas sensor for the highly sensitive, rapid detection of trace H₂ gas in air. Facilitated by a unique MIS fabrication technique, pairs of single-crystalline silicon thermopiles (i.e., sensing and reference thermopiles) are batch fabricated with high-density single-crystalline silicon thermocouples, yielding an outstanding temperature sensitivity at the sub-mK level. Such devices ensure the detection of miniscule temperature changes due to the catalytic reaction of H₂ with a detection limit as low as ~1 ppm at an operating temperature of 120 °C. The MEMS differential thermopiles also exhibit a wide linear detection range (1 ppm-2%, more than four orders of magnitude) and fast response and recovery times of 1.9 s and 1.4 s, respectively, when detecting 0.1% H₂ in air. Moreover, the sensors show good selectivity against common combustible gases and volatile organics, good repeatability, and long-term stability. The proposed MEMS thermopile H₂ sensors hold promise for the trace detection and early warning of H₂ leakage in a wide range of applications.

Enhancing regulatory T-cell function via inhibition of high mobility group box 1 protein signaling in immune thrombocytopenia

Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder. Abnormally increased levels of High Mobility Group Box 1 (HMGB1) protein associate with thrombocytopenia and therapeutic outcome in ITP. Previous studies proposed that a natural inhibitor of HMGB1, 18β-glycyrrhetinic acid (18β-GA), could be used for its anti-inflammatory and immune-modulatory effects, although its ability to correct immune balance in ITP is unclear. In this study, we showed that plasma HMGB1 correlated negatively with platelet counts in ITP patients, and confirmed that 18β-GA stimulated the production of regulatory T cells (Treg), restored the balance of CD4+ T-cell subsets and enhanced the suppressive function of Treg through blocking the effect on HMGB1 in patients with ITP. HMGB1 short hairpin RNA interference masked the effect of 18β-GA in Treg of ITP patients. Furthermore, we found that 18β-GA alleviated thrombocytopenia in mice with ITP. Briefly, anti-CD61 immune-sensitized splenocytes were transferred into severe combined immunodeficient mice to induce a murine model of severe ITP. The proportion of circulating Treg increased significantly, while the level of plasma HMGB1 and serum antiplatelet antibodies decreased significantly in ITP mice along 18β-GA treatment. In addition, 18β-GA reduced phagocytic activity of macrophages towards platelets both in ITP patients and ITP mice. These results indicate that 18β-GA has the potential to restore immune balance in ITP via inhibition of HMGB1 signaling. In short, this study reveals the role of HMGB1 in ITP, which may serve as a potential target for thrombocytopenia therapy.

A bibliometric analysis of primary immune thrombocytopenia from 2011 to 2021

Primary immune thrombocytopenia (ITP) is an autoimmune disorder characterized by isolated thrombocytopenia. This bibliometric analysis was applied to identify the characteristics of global scientific output, the hotspots, and frontiers of ITP over the past 10 years. We retrieved publications from 2011 to 2021 from the Web of Science Core Collection (WoSCC). Bibliometrix package, VOSviewer, and Citespace were used to analyse and visualize the trend, distribution, and hotspots of research on ITP. Altogether, there were 2084 papers, written by 9080 authors from 410 organizations in 70 countries/regions, published in 456 journals with 37 160 co-cited references. In the last decades, the most productive journal was British Journal of Haematology, China was the most productive country. and the most cited journal was Blood. Shandong University was the most productive institution in the field of ITP. NEUNERT C, 2011, BLOOD, CHENG G, 2011, LANCET, and PATEL VL, 2012, BLOOD were the top three most cited documents. "Thrombopoietin receptor agonist", "regulatory T cell" and "sialic acid" were three hotspots of the last decade. And "immature platelet fraction", "Th17", and "fostamatinib" would be research frontiers in the feature. The present study provided a novel insight for future research directions and scientific decision-making.

Ultra-Responsive MEMS Sensing Chip for Differential Thermal Analysis (DTA)

Ultra-responsive single-crystal silicon MEMS thermopiles for differential thermal analysis (DTA) are developed. Facilitated by a unique "microholes interetch and sealing (MIS)" technique, pairs of suspended thermopiles are batch fabricated in a differential form, with high-density (54 pairs) n-type/p-type single-crystal silicon thermocouples integrated within each thermopile (sample area ~0.045 mm²). The fabricated MEMS thermopile sensors exhibit outstanding power responsivity of 99.5 V/W and temperature responsivity of 27.8 mV/°C, which are more than 4 times higher than those reported for material thermal analysis. The high-responsivity MEMS DTA chips allow us to accurately measure the indium melting point at different heating rates of ~1-100 °C/s. We also perform DTA measurement of the dehydration process of CuSO₄·5H₂O and the crystals show three stages of losing water of crystallization before becoming anhydrous copper sulfate salt. Our high-performance, cost-effective MEMS sensing chips hold promise for rapid and accurate DTA characterization for a wide range of applications.

Microcantilever-Based In Situ Temperature-Programmed Desorption (TPD) Technique

Temperature-programmed desorption (TPD) is an essential technique for characterizing the fundamental properties of advanced catalysts like catalytic activity and kinetics. However, the available TPD instruments are bulky and use ex situ detectors to measure the probe molecules in the elution gas flow. Herein, we demonstrate an in situ TPD technique by developing a silicon microcantilever that integrates functional elements for mass measuring and programmable sample heating. An only nanogram-level sample is required to load on the microcantilever free end, where the integrated microheater provides programmed temperatures and the desorption-induced mass change can be measured in situ. In situ TPD can continuously measure the number of desorbed molecules from the catalyst during programmed heating, without the need for ex situ detectors. With a single-time in situ TPD measurement, the desorption activation energy can be directly calculated. The proposed in situ TPD method outperforms the existing TPD techniques and is expected to enable next-generation TPD applications.

A Star-Shaped Copolymer with Tetra-Hydroxy-Phenylporphyrin Core and Four PNIPAM-b-PMAGA Arms for Targeted Photodynamic Therapy

The novel thermosensitive star-shaped tetra-hydroxy-phenylporphyrin-cored (THPP) double hydrophilic poly(N-isopropylacrylamide)-b-poly(methylacrylamide glucose) block copolymers (THPP-(PNIPAM-b-PMAGA)₄) were synthesized via the reversible addition-fragmentation chain transfer (RAFT) polymerization. Notably, the low critical solution temperatures (LCSTs) of THPP-(PNIPAM-b-PMAGA)₄ were above normal body temperature (37 °C) which depended on the hydrophilic PMAGA contents of copolymers. When the temperature was higher than the LCST of the copolymer, the copolymer could be neutralized into micelles in aqueous and could be coated with antitumor drugs and released around tumor cells. The MTT study indicated that THPP-(PNIPAM-b-PMAGA)₄ had a low toxicity to L929 and HeLa cells in the absence of light. However, THPP-(PNIPAM-b-PMAGA)₄ showed a high toxicity with HeLa cells under light irradiation which could be used as a potential photosensitizer for photodynamic therapy (PDT). In addition, THPP-(PNIPAM-b-PMAGA)₄ showed specific a recognition function with Concanavalin A (Con A) to achieve active targeted drug delivery. This work provides a new approach for the development of tumor targeting and chemotherapy/PDT.

Study on naked eye tracing of inguinal sentinel lymph nodes in penile cancer patients with carbon nanoparticle suspension injection

Objective

Exploratory study of the effect and clinical value of carbon nanoparticle suspension injection (CNSI) as a tracer for inguinal sentinel lymph nodes in penile cancer.

Method

We selected 29 patients with penile cancer in our department from January 2019 to October 2022. According to whether the CNSI tracer was injected during the pathological biopsy of the inguinal lymph nodes, the enrolled patients were assigned to the control group, the group in which CNSI was injected 12 h before the surgery (12HBS group) and the group in which CNSI was injected 0.5 h before the surgery (0.5HBS group). Evaluating the effectiveness of CNSI as a lymphatic tracer involves analyzing the following: its safety, the statistical analysis of the detection rate (DR) of different groups, the number of lymph nodes sent for each case (NOLNSFEC), the difference of positive rate of lymphatic metastasis (PROLM), and operation time (OT).

Results

The lymph nodes in the 12HBS group and 0.5HBS group had an obvious black staining appearance, and no adverse reactions or surgical complications were found. Most of the black-stained areas caused by CNSI injection were removed with penile excision, which did not affect the postoperative appearance. This did not affect the pathological analysis. The DR of lymph nodes in the 12HBS group was higher (p < 0.05) than that in the control group. More lymph nodes were removed for examination (p < 0.05), which improved the efficiency of surgery. Compared with the 12HBS group, the number of lymph nodes removed in the 0.5HBS group decreased (p < 0.05). The OT was shortened (p < 0.05), but there was no significant difference in the DR and PROLM.

Conclusion

CNSI was applied to the naked-eye tracing of inguinal sentinel lymph nodes in penile cancer, which is safe and efficient. Injection of CNSI 0.5 h before surgery can help identify the "foremost position" of sentinel lymph nodes and reduce surgical trauma.

Numerical Simulation of an Improved Updraft Biomass Gasifier Based on Aspen Plus

In this paper, numerical investigation and optimization is conducted upon an improved updraft gasifier which is expected to overcome the weakness of conventional updraft gasifier. The comprehensive Aspen Plus model of the improved updraft gasifier is based on the RYield and RCSTR reactor. The tar prediction model is constructed, and the yield of tar is determined by the volatile of biomass and gasification temperature. The Aspen Plus simulation results agree very well with experiment results for the product yields and gasification efficiency, which shows the accuracy of the Aspen Plus model. The tar content in syngas of the improved gasifier is proved to be much lower than that of the conventional one by this model. The inflection point of the gasification efficiency occurs when air ratio is 0.25, and the optimum steam proportion in the air is 7.5%. Such a comprehensive investigation could provide necessary information for the optimal design and operation of the improved updraft gasifier.

A composite hydrogel containing resveratrol-laden nanoparticles and platelet-derived extracellular vesicles promotes wound healing in diabetic mice

Diabetic wounds are difficult to heal because of persistent inflammation and limited angiogenesis. Resveratrol (RES) is an anti-inflammatory and antioxidant agent. Platelet-derived extracellular vesicles (PDEVs) are rich in growth factors and cytokines, which promote proliferation and angiogenesis. However, single drug treatment has limited efficacy and delivery efficiency. Bioengineering can improve the limited effect of single drugs by combining drugs and materials to obtain complementary or cooperative bioengineered drugs. In this study, gelatin methacrylate (GelMA) and silk fibroin glycidyl methacrylate (SFMA) were used to synthesize GelMA/SFMA composite hydrogels with suitable mechanical properties, swelling ratio and biodegradability. The composite hydrogel was used as a wound dressing for sustained drug release. RES was loaded into mesoporous silica nanoparticles (MSNs) to synthesize MSN-RES to enhance the release dynamic, and MSN-RES and PDEVs were combined with the composite hydrogels to form GelMA/SFMA/MSN-RES/PDEVs hydrogels. The GelMA/SFMA/MSN-RES/PDEVs had low cytotoxicity and good biocompatibility, inhibited macrophage iNOS expression, and promoted the tube formation by human umbilical vein endothelial cells (HUVECs) in vitro. In a diabetic mouse wound model, the GelMA/SFMA/MSN-RES/PDEVs hydrogels decreased the expression of pro-inflammatory factors TNF-α and iNOS, increased the expression of anti-inflammatory factors TGF-β1 and Arg-1, promoted angiogenesis, and accelerated wound healing. Interestingly, the GelMA/SFMA/MSN-RES/PDEVs hydrogels promoted the expression of extracellular purinergic signaling pathway-related CD73 and adenosine 2A receptor (A2A-R). Therefore, the GelMA/SFMA/MSN-RES/PDEVs hydrogels could be used as wound dressings to regulate the inflammation and angiogenesis of diabetic wounds and accelerate wound healing. STATEMENT OF SIGNIFICANCE: Drugs often fail to function because of a continuous oxidative stress microenvironment and inflammation. Here, a GelMA/SFMA hydrogel, with enhanced mechanical properties and liquid absorption ability, is proposed for sustained release of drugs. In addition to carrying platelet-derived extracellular vesicles (PDEVs) with pro-angiogenic effects, the hydrogels were also loaded with nanoparticle-encapsulated resveratrol with anti-inflammatory activities, aiming to reduce inflammation and oxidative stress in the wound microenvironment, such that the wound could receive proliferative repair signals to achieve sequential treatment and heal quickly. We also experimentally predicted that the regulatory mechanism of the GelMA/SFMA/MSN-RES/PDEVs in wound healing might be related to the extracellular purinergic signaling pathway.

Influence of land use changes on the remaining available aquifer storage (RAAS): A case study of the Taoerhe alluvial-proluvial fan

Groundwater resources are important water sources for people living in arid-semiarid China. To solve the problem of continuously declining groundwater levels, groundwater artificial recharge has been widely conducted by using available aquifers. However, the effects of land use changes on the available aquifer storage, especially on the remaining available aquifer storage (RAAS), have not been fully explored. Here, we quantitatively evaluated the effects of land use changes on the RAAS, exemplifying the Taoerhe alluvial-proluvial fan. Independent component analysis (ICA) is used to determine precipitation- and groundwater extraction-affected RAASs, and regression equations are established for land use type areas and precipitation- and groundwater extraction-affected RAASs through stepwise regression and all-subsets regression. An integrated model combining the future land use simulation (FLUS) model and Markov-chain model is established to predict three land use change scenarios in 2036, and the impacts of land use changes on the precipitation- and groundwater extraction-affected RAASs are evaluated. The results show that land use changes were generally active from 2000 to 2018; during this time, the RAAS showed a fluctuating upward trend. Rational land use changes are critical to the RAAS. In the 2036 baseline scenario, the precipitation-affected RAAS is the smallest and the groundwater extraction-affected RAAS is the largest among the three scenarios, contrary to the economic development scenario results. The woodland conservation scenario shows that the groundwater level can be maintained at a stable level with appropriate woodland protection measures to ensure the stability of the RAAS, providing the most promising results for groundwater development and utilization in the study area. These results temporally quantify the effects of land use changes on the precipitation- and groundwater extraction-affected RAASs and provide a reference for developing artificial recharge schemes in arid-semiarid regions and studying the effects of land use changes on available aquifer storages.

Search for ABO3 Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO₃-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (E_g), Curie temperature (T_c), and dielectric loss (tan δ) of ABO₃-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, E_g, and T_c are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO₃ ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO₃ ferroelectric perovskites with desired multiple properties.

Highly Sensitive MEMS Sensor Using Bimetallic Pd-Ag Nanoparticles as Catalyst for Acetylene Detection

Acetylene detection plays an important role in fault diagnosis of power transformers. However, the available dissolved gas analysis (DGA) techniques have always relied on bulky instruments and are time-consuming. Herein, a high-performance acetylene sensor was fabricated on a microhotplate chip using In₂O₃ as the sensing material. To achieve high sensing response to acetylene, Pd-Ag core-shell nanoparticles were synthesized and used as catalysts. The transmission electron microscopy (TEM) image clearly shows that the Ag shell is deposited on one face of the cubic Pd nanoseeds. By loading the Pd-Ag bimetallic catalyst onto the surface of In₂O₃ sensing material, the acetylene sensor has been fabricated for acetylene detection. Due to the high catalytic performance of Pd-Ag bimetallic nanoparticles, the microhotplate sensor has a high response to acetylene gas, with a limit of detection (LOD) of 10 ppb. In addition to high sensitivity, the fabricated microhotplate sensor exhibits satisfactory selectivity, good repeatability, and fast response to acetylene. The high performance of the microhotplate sensor for acetylene gas indicates the application potential of trace acetylene detection in power transformer fault diagnosis.

Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research

The aim of this study was to find the application value of selective polyadenylation in immune cell infiltration, biological transcription function and risk assessment of survival and prognosis in lung adenocarcinoma (LUAD). The processed original mRNA expression data of LUAD were downloaded, and the expression profiles of 594 patient samples were collected. The (APA) events in TCGA-NA-SEQ data were evaluated by polyadenylation site use Index (PDUI) values, and the invasion of stromal cells and immune cells and tumor purity were calculated to group and select the differential genes. Lasso regression and stratified analysis were used to examine the role of risk scores in predicting patient outcomes. The study also used the GDSC database to predict the chemotherapeutic sensitivity of each tumor sample and used a regression method to obtain an IC50 estimate for each specific chemotherapeutic drug treatment. Then CIBERSORT algorithm was used to conduct Spearman correlation analysis, immune regulatory factor analysis and TIDE immune system function analysis for gene expression level and immune cell content. Finally, the Kaplan-Meier curve was used to analyze the correlation between stromal score and the immune score of LUAD. In this study, APA's LUAD risk score prognostic model was constructed. KM survival analysis showed that immune score affected the prognosis of LUAD patients ( P  = 0.027) but the matrix score was not statistically significant ( P  = 0.1). We extracted 108 genes with APA events from 827 different genes and based on PUDI clustering and heat map, the survival rate of patients in the four groups was significantly different ( P  = 0.05). Multiple omics studies showed that risk score was significantly positively correlated with Macrophages M0, T cells Follicular helper, B cells naive and NK cells resting. It is significantly negatively correlated with dendritic cells resting, mast cells resting, monocyte, T cells CD4 memory resting and B cells memory. We further explored the relationship between the expression of immunosuppressor genes and risk score and found that ADORA2A, BTLA, CD160, CD244, CD274, CD96, CSF1R and CTLA4 genes were highly correlated with the risk score. Selective poly adenylation plays an important role in the development and progression of LUAD, immune invasion, tumor cell invasion and metastasis and biological transcription, and affects the survival and prognosis of LUAD patients.

Immunomodulatory Factor TIM3 of Cytolytic Active Genes Affected the Survival and Prognosis of Lung Adenocarcinoma Patients by Multi-Omics Analysis

[Objective] Using multi-omics research methods to explore cytolytic activity-related genes through the immunoregulatory factors HAVCR2 (TIM3) affecting the survival and prognosis of lung adenocarcinoma. [Methods] We combined Cox single factor regression and lasso regression feature selection algorithm to screen out the key genes of cytolytic activity in lung adenocarcinoma, and applied multi-omics research to explore the clinical predictive value of the model, including onset risk, independent prognosis, clinical relevance, signal transduction pathways, drug sensitivity, and the correlation of immune regulatory factors, etc. TCGA data are used as the experimental group, and GEO data is used as the external data control group to verify the stability of the model. The survival curve was generated by the Kaplan–Meier method and compared by log-rank, and the Cox proportional hazard model was used for multivariate analysis. In this study, 10 fresh tissue samples of lung adenocarcinoma were collected for cellular immunohistochemical experiments to analyze the expression of immunoregulatory factors in cancer tissues, and the key immunoregulatory factors were verified and screened out. [Results] A total of 450 genes related to cytolytic activity were differentially expressed, of which 273 genes were up-regulated and 177 genes were down-regulated. A total of 91 key genes related to cytolytic activity related to the prognosis of lung adenocarcinoma were screened through Cox single factor regression. The ROC curve results showed that the AUC values of 1, 3, and 5 years in the training set and test set were all greater than 0.7, indicating that the model has a valid verification. The level of risk score is significantly related to the sensitivity of patients to AKT inhibitor VIII, Lenalidomide, and Tipifarnib. In addition, our study also found that receptor and MHC genes related to immunomodulatory, and chemokines, including HAVCR2, are more highly expressed in the low-risk group. [Conclusions] HAVCR2 (TIM3) immunoregulatory factors affect the expression of key genes that affect cytolytic activity in lung adenocarcinoma cells, and to some extent indirectly affect the survival and prognosis of patients with lung adenocarcinoma.

Polymeric Nanoreactors with Chemically Tunable Redox Responsivity

Bioresponsive nanomaterials are increasingly important in a variety of applications such as disease imaging, drug delivery, and tissue engineering. However, it remains a big challenge to manipulate response efficacy of such materials for performance optimization in a highly complex milieu in vivo. Here, we developed chemically adjustable nanoreactors (CANs) with the structure of polymeric cores and albumin shells to achieve tunable redox responsivity. In vitro characterization demonstrates stable, spherical nanoparticles of the CANs with a particle size of about 50 nm. The fluorescence activation ratios of the CANs are determined by various albumin modification densities on the shell. Meanwhile, the response sensitivity of the CANs to GSH levels (0.6-4 mM) can be tuned by acid-base properties of polymeric blocks in the core. This unique tunable redox responsivity enables the CANs suitable for probe optimization in cancer imaging both in vivo and at histological levels. Overall, this study offers a new design strategy for manipulation on performance of core/shell nanoreactors or bioresponsive nanomaterials.

Impaired mitochondria of Tregs decreases OXPHOS-derived ATP in primary immune thrombocytopenia with positive plasma pathogens detected by metagenomic sequencing

Background

Primary immune thrombocytopenia (ITP) is an autoimmune disease. Some ITP patients are associated with pathogen infection undetected with conventional technologies. Investigating the changes of T cells and potential metabolic mechanism are important for better understanding of ITP.

Methods

The study enrolled 75 newly diagnosed ITP patients. The pathogens of patients were detected by metagenomic next-generation sequencing (mNGS). Plasma lipids were measured by liquid chromatography-mass spectrometry (LC–MS). CD4 T cell and CD8 T cell were analyzed using flow cytometry. Mitochondrial reactive oxygen species (ROS) and mitochondrial membrane potential were measured by flow cytometry. Seahorse XF real-time ATP rate assay was used to investigate the change of cellular metabolism.

Results

Positive plasma pathogens were detected in seven ITP patients. Of them, 5 (71.4%) positive pathogen-ITP patients were no response (NR) after first-line treatment with corticosteroids. Regulatory T cells (Tregs) increased significantly in positive pathogen-ITP patients compared to negative pathogen-ITP patients and healthy controls (HC). Mitochondrial membrane potential of Th17 and Tregs were decreased in positive pathogen-ITP and negative pathogen-ITP patients, compared to HC (all p < 0.05). The overall metabolism flux of positive pathogen-ITP patients was decreased, as compared to HC (p = 0.004), of them a higher proportion of glycolysis-derived ATP and a smaller proportion of oxidative phosphorylation (OXPHOS)-derived ATP were found in Tregs. The ATP rate index of Tregs was decreased significantly in positive pathogen-ITP patients compared to negative pathogen-ITP patients and HC (p < 0.05).

Conclusions

Impaired mitochondria function of Tregs in positive pathogen-ITP patients caused a decrease of OXPHOS-derived ATP and overall metabolism flux that might be the cause of steroid resistance in ITP patients.

Thermogravimetric Analysis on a Resonant Microcantilever

Thermogravimetric analysis (TGA) is a widely applied classic method for material characterization. However, the existing TGA method has reached its technical ceiling in regard to its sensitivity, efficiency, application scope, and extensibility. With temperature-programming and a picogram (10^-12 g) mass resoluble measurement function, an integrated resonant microcantilever is proposed and developed into micro-electromechanical system-based TGA (MEMS TGA) technology to satisfy the significantly higher TGA requirements. With only a nanogram (10^-9 g) level of a loaded sample, the microcantilever can conduct ultrasensitive mass-loss analysis along with ultrafast and controllable heating up to 1200 °C. Experiments have verified that MEMS TGA can improve heating efficiency by at least one order of magnitude compared to conventional TGA while maintaining measurement accuracy. The trace sample requirement also enables MEMS TGA to directly test explosive substances in an air atmosphere, while conventional TGA with a larger amount of sample has difficulty in avoiding explosion-induced equipment damage during heating. The silicon MEMS TGA microchip also exhibits functional combination (even integration) with other analytical techniques such as Raman spectroscopy to realize operando characterization.

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H₂ flow of 3 L min^-1, and a thickness of 200 μm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.