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Aim Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. Methods PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. Results The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the ‘sunflower method’ (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. Conclusions The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Purinergic ligand-gated ion channel 7 receptor (P2X7R) is a nonselective cation channel of the purinergic receptor family. P2X7R is activated by adenosine triphosphate (ATP) and plays a significant role in inflammatory and autoimmune diseases by triggering cellular signal transduction. More importantly, P2X7R is abnormally expressed in many tumor cells and is involved in the progression of various tumor cells. Studies have shown that the irregular expression of P2X7R in colorectal cancer (CRC) can not only indirectly affect the occurrence and development of CRC by promoting inflammatory bowel disease but also directly affect the proliferation and metastasis of CRC cells. P2X7R plays a bidirectional role in cancer induction and inhibition by mediating complex signaling pathways in CRC, and its expression level is closely related to the overall survival of CRC patients. Therefore, P2X7R may be a biomarker and potential therapeutic target for the development and prognosis of CRC. In this paper, we review the research progress on P2X7R in CRC.

It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms’ results offers the insight that reduced Na2O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

Background The Mg–Al-lactate layered double hydroxide nanosheet (LDH-NS) has shown great potential as an optimal nanocarrier for extensive use in plants. However, previous studies in plant sciences have not provided a clear description of the application for the LDH-NSs-based double-stranded RNA (dsRNA) delivery (LDH-dsRNA) system in different tissues of both model and non-model species. Results LDH-NSs were synthesized by using the co-precipitation method, while the dsRNAs targeting genes of interest were prepared in vitro using T7 RNA polymerase. The LDH-dsRNA bioconjugates with a neutral charge were produced by incubating with the mass ratio of LDH-NSs to dsRNA at 3:1, which were then introduced into intact plant cells using three different approaches, including injection, spray, and soak. The LDH-dsRNA delivery method was optimized by inhibiting the expression of the Arabidopsis thaliana ACTIN2 gene. As a result, soaking A. thaliana seedlings in a medium containing LDH-dsRNA for 30 min led to the silencing of 80% of the target genes. The stability and effectiveness of the LDH-dsRNA system were further confirmed by the high-efficiency knockdown of plant tissue-specific genes, including that encoding phytoene desaturase (PDS), WUSCHEL (WUS), WUSCHEL-related homeobox 5 (WOX5), and ROOT HAIR DEFECTIVE 6 (RHD6). In addition, the LDH-dsRNA system was employed in cassava, where it was found that the expression of the gene encoding nucleotide-binding site and leucine-rich repeat (NBS-LRR) was significantly reduced. As a result, the resistance of cassava leaves to pathogens was weakened. Noteworthy, the injection of LDH-dsRNA into leaves resulted in a significant downregulation of target genes in both stems and flowers, indicating the successful transport of LDH-dsRNA from leaves to other parts of plants. Conclusions LDH-NSs have proven to be a highly effective molecular tool for delivering dsRNA into intact plant cells, enabling accurate control of target gene expression.

Cell therapy is an accessible method for curing damaged organs or tissues. Yet, this approach is limited by the delivery efficiency of cell suspension injection. Over recent years, biological scaffolds have emerged as carriers of delivering therapeutic cells to the target sites. Although they can be regarded as revolutionary research output and promote the development of tissue engineering, the defect of biological scaffolds in repairing cell-dense tissues is apparent. Cell sheet engineering (CSE) is a novel technique that supports enzyme-free cell detachment in the shape of a sheet-like structure. Compared with the traditional method of enzymatic digestion, products harvested by this technique retain extracellular matrix (ECM) secreted by cells as well as cell-matrix and intercellular junctions established during in vitro culture. Herein, we discussed the current status and recent progress of CSE in basic research and clinical application by reviewing relevant articles that have been published, hoping to provide a reference for the development of CSE in the field of stem cells and regenerative medicine.

Redox catalysts play a vital role in chemical looping oxidative dehydrogenation processes, which have recently been considered to be a promising prospect for propylene production. This work describes the coupling of surface acid catalysis and selective oxidation from lattice oxygen over MoO3-Fe2O3 redox catalysts for promoted propylene production. Atomically dispersed Mo species over γ-Fe2O3 introduce effective acid sites for the promotion of propane conversion. In addition, Mo could also regulate the lattice oxygen activity, which makes the oxygen species from the reduction of γ-Fe2O3 to Fe3O4 contribute to selectively oxidative dehydrogenation instead of over-oxidation in pristine γ-Fe2O3. The enhanced surface acidity, coupled with proper lattice oxygen activity, leads to a higher surface reaction rate and moderate oxygen diffusion rate. Consequently, this coupling strategy achieves a robust performance with 49% of propane conversion and 90% of propylene selectivity for at least 300 redox cycles and ultimately demonstrates a potential design strategy for more advanced redox catalysts.

Neuropathological mechanisms of manic syndrome or manic episodes in bipolar disorder remain poorly characterised, as the research progress is severely limited by the paucity of appropriate animal models. Here we developed a novel mania mice model by combining a series of chronic unpredictable rhythm disturbances (CURD), which include disruption of circadian rhythm, sleep deprivation, exposure to cone light, with subsequent interference of followed spotlight, stroboscopic illumination, high-temperature stress, noise disturbance and foot shock. Multiple behavioural and cell biology tests comparing the CURD-model with healthy controls and depressed mice were deployed to validate the model. The manic mice were also tested for the pharmacological effects of various medicinal agents used for treating mania. Finally, we compared plasma indicators of the CURD-model mice and the patients with the manic syndrome. The CURD protocol produced a phenotype replicating manic syndrome. Mice exposed to CURD presented manic behaviours similar to that observed in the amphetamine manic model. These behaviours were distinct from depressive-like behaviours recorded in mice treated with a depression-inducing protocol of chronic unpredictable mild restraint (CUMR). Functional and molecular indicators in the CURD mania model showed multiple similarities with patients with manic syndrome. Treatment with LiCl and valproic acid resulted in behavioural improvements and recovery of molecular indicators. A novel manic mice model induced by environmental stressors and free from genetic or pharmacological interventions is a valuable tool for research into pathological mechanisms of mania.

Hepatocellular carcinoma (HCC), one of the most malignant tumors, is characterized by its stubborn immunosuppressive microenvironment. As one of the main members of the tumor microenvironment (TME) of HCC, tumor-associated macrophages (TAMs) play a critical role in its occurrence and development, including stimulating angiogenesis, enhancing immunosuppression, and promoting the drug resistance and cancer metastasis. This review describes the origin as well as phenotypic heterogeneity of TAMs and their potential effects on the occurrence and development of HCC and also discusses about various adjuvant therapy based strategies that can be used for targeting TAMs. In addition, we have highlighted different treatment modalities for TAMs based on immunotherapy, including small molecular inhibitors, immune checkpoint inhibitors, antibodies, tumor vaccines, adoptive cellular immunotherapy, and nanocarriers for drug delivery, to explore novel combination therapies and provide feasible therapeutic options for clinically improving the prognosis and quality of life of HCC patients.

Organoids are a new type of 3D model for tumor research, which makes up for the shortcomings of cell lines and xenograft models, and promotes the development of personalized precision medicine. Long-term culture, expansion and storage of organoids provide the necessary conditions for the establishment of biobanks. Biobanks standardize the collection and preservation of normal or pathological specimens, as well as related clinical information. The tumor organoid biobank has a good quality control system, which is conducive to the clinical transformation and large-scale application of tumor organoids, such as disease modeling, new drug development and high-throughput drug screening. This article summarized the common tumor types of patient-derived organoid (PDO) biobanks and the necessary information for biobank construction, such as the number of organoids, morphology, success rate of culture and resuscitation, pathological types. In our results, we found that patient-derived tumor organoid (PDTO) biobanks were being established more and more, with the Netherlands, the United States, and China establishing the most. Biobanks of colorectal, pancreas, breast, glioma, and bladder cancers were established more, which reflected the relative maturity of culture techniques for these tumors. In addition, we provided insights on the precautions and future development direction of PDTO biobank building.

Background Tumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone. Methods Immune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC. Results Immune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients. Conclusions The immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

The prior information constrained impedance inversion is an important tool to improve the inversion effect. With the traditional constrained prior information extracted from logging data by the analytic formula, it is difficult to accurately describe the information of a complex reservoir. In addition, the traditional inversion method is trace-by-trace, which ignores the lateral information contained in seismic data. This paper presents a multichannel seismic impedance inversion method combining logging and seismic. In this method, the dictionary learning method is used to extract the vertical prior information of the formation from the logging data. At the same time, we can learn the dip information from seismic data cube. Under the framework of multichannel inversion, regularization and sparse representation technology are used to simultaneously add the vertical and the transverse distribution prior information into the inversion process. Block coordinate descent method is used to solve the multichannel inversion problem, making the seismic inversion efficient. This method excavates the spatial prior information in a data-driven way and is used for constrained inversion, avoiding the false prior cognition caused by manual interpretation. Through the model and field data testing, it is verified that this method is effective.

Background Liver transplantation and liver resection are curative options for early hepatocellular carcinoma (HCC). The outcome is in part depended on the immunological response to the malignancy. In this study, we aimed to identify immunological profiles of non-HCV/non-HBV HCC patients. Methods Thirty-nine immune cell subsets were measured with multicolor flow cytometry. This immunophenotyping was performed in peripheral blood (PB) and tumor specimens of 10 HCC resection patients and 10 healthy donors. The signatures of the highly differential leukocyte count (hDIF) were analyzed using multidimensional techniques. Functional capability was measured using intracellular IFN-γ staining (Trial Registration DRKS00013567). Results The hDIF showed activation (subsets of T-, B-, NK- and dendritic cells) and suppression (subsets of myeloid-derived suppressor cells and T- and B-regulatory cells) of the antitumor response. Principal component analysis of PB and tumor infiltrating leukocytes (TIL) illustrated an antitumor activating gradient. TILs showed functional capability by secreting IFN-γ but did not kill HCC cells. Conclusions In conclusion, the measurement of the hDIF shows distinct differences in immune reactions against non-HBV/non-HCV HCC and illustrates an immunosuppressive gradient toward peripheral blood. Trial Registration DRKS00013567

This study investigates a novel forming design, that is, intercritical rolling (IR) + low-temperature rolling (LTR) + short-time intercritical annealing (STIA) + one-step stamping (OSS), to decrease or eliminate the yield point elongation (YPE) in a medium-Mn alloy. Results show that the novel forming design leads to ultra-fine microstructure and excellent mechanical properties in the stamped parts. The dislocation density or austenite mechanical stability may be the underlying reasons for the decreased YPE in medium-Mn steels. This novel forming design can increase the work hardening exponent of experimental samples by increasing the dislocation density in ferrite or decreasing austenite mechanical stability, resulting in a continuous yielding feature in stamped parts.

With the continuous development of science and technology, electronic devices have begun to enter all aspects of human life, becoming increasingly closely related to human life. Users have higher quality requirements for electronic devices. Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market. Considering the large output of electronic devices, improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved. This study investigates the electronic device testing machine allocation problem (EDTMAP), aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation. First, a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines. Second, we developed a discrete multi-objective artificial bee colony (DMOABC) algorithm to solve EDTMAP. A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm, respectively. Numerical experiments were conducted to evaluate the performance of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2). Finally, the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules. The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.

It is well known that on the interannual timescale, the westward extension of the western North Pacific subtropical high (WNPSH) results in enhanced rainfall over the Yangtze River basin (YRB) in summer, and vice versa. This study identifies that this correspondence experiences a decadal change in the late 1970s. That is, the WNPSH significantly affects YRB precipitation (YRBP) after the late 1970s (P2) but not before the late 1970s (P1). It is found that enhanced interannual variability of the WNPSH favors its effect on YRB rainfall in P2. On the other hand, after removing the strong WNPSH cases in P2 and making the WNPSH variability equivalent to that in P1, the WNPSH can still significantly affect YRB rainfall, suggesting that the WNPSH variability is not the only factor that affects the WNPSH–YRBP relationship. Further results indicate that the change in basic state of thermal conditions in the tropical WNP provides a favorable background for the enhanced WNPSH–YRBP relationship. In P2, the lower-tropospheric atmosphere in the tropical WNP gets warmer and wetter, and thus the meridional gradient of climatological equivalent potential temperature over the YRB is enhanced. As a result, the WNPSH-related circulation anomalies can more effectively induce YRB rainfall anomalies through affecting the meridional gradient of equivalent potential temperature over the YRB.

Disassembly sequence planning (DSP) can effectively increase the disassembly efficiency, shorten the disassembly cycle, reduce disassembly costs and reduce environmental hazards of end-of-life (EOL) products, playing an important role in manufacturing industries. Thus, it is urgent to propose an approach to solve the DSP problem. DSP is a famous NP-hard combinatorial optimization problem. As the size of components increases, exact algorithms can hardly obtain the optimal disassembly sequence. Therefore, we proposes a promising intelligence algorithm, modified grey wolf optimizer (MGWO), for solving the DSP problem. MGWO inherits the main idea of the hierarchy and hunting mechanism of the original grey wolf optimizer (GWO). Three new operators are designed in MGWO to ensure the feasibility of solutions under the complex constraint of disassembly precedence. The feasible solution generator (FSG) is designed to obtain feasible disassembly sequences, the neighborhood search operator (NSO) is developed to make wolves (solutions) self-evolving, and the guided search operator (GSO) is used to make the wolf group guided by three leaders of wolves. Two engineering cases are applied to validate the effectiveness of the proposed operators. Then, they and one real-world application are used to compare the MGWO with other reported methods. The results demonstrate that MGWO can solve the DSP problem effectively.

Photocatalysts BiVO4 were prepared by three different methods and applied to the synthesis of imines under visible light irradiation. Multiple characterization methods, including XRD, SEM, TEM, UV–Vis, FL and BET were applied to analyze the crystal types, morphologies, optical and charge separated properties and specific surface areas of these catalysts. The catalytic performance of these catalysts was measured. The relationship between the morphology, crystal type and photocatalytic performance of the BiVO4 was explored. The results showed that the ms-BiVO4 (prepared by DMF method) with decahedron structure had the best visible light absorption capacity and the largest specific surface area, which had the best conversion rate (~ 64.2%) in the synthesis of imine. The photostability of the catalyst was verified by cyclic experiments. A capture experiment verified that the reaction was mainly completed through the synergistic action of h+, e− and ·O2−.

Circular RNAs (circRNAs) play critical regulatory roles in cancer biological processes. Nevertheless, the contributions and underlying mechanisms of circRNAs to pancreatic ductal adenocarcinoma (PDAC) remain largely unexplored. Dysregulated circRNAs between cancerous tissues and matched adjacent normal tissues were identified by circRNA microarray in PDAC. The biological effect of hsa_circ_007367 both in vitro and in vivo was demonstrated by gain- and loss-of-function experiments. Further, dual-luciferase reporter and RNA pull-down assays were performed to confirm the interaction among hsa_circ_007367, miR-6820-3p, and Yes-associated protein 1 (YAP1). The expression of hsa_circ_007367 and YAP1 were detected by in situ hybridization (ISH) and immunohistochemistry (IHC) using tissue microarray (TMA) in 128 PDAC samples. We first identified that a novel circRNA, hsa_circ_0007367, was markedly upregulated in PDAC tissues and cells. Functionally, in vivo and in vitro data indicated that hsa_circ_0007367 promotes the proliferation and metastasis of PDAC. Mechanistically, we confirmed that hsa_circ_0007367 could facilitate the expression of YAP1, a well-known oncogene, by sponging miR-6820-3p, which function as a tumor suppresser in PDAC cells. The results of ISH and IHC demonstrated that hsa_circ_0007367 and YAP1 were upregulated in PDAC tissues. Furthermore, clinical data showed that higher hsa_circ_0007367 expression was correlated with advanced histological grade and lymph node metastasis in PDAC patients. In conclusion, our findings reveal that hsa_circ_0007367 acts as an oncogene via modulating miR-6820-3p/YAP1 axis to promote the progression of PDAC, and suggest that hsa_circ_0007367 may serve as a potential therapeutic target for treatment of PDAC.

Series elastic actuators (SEAs) have emerged as promising devices to enhance the safety of human-robot interactions in the manufacturing industry. However, the control of a hydraulic SEA under disturbance remains an unexplored issue. To address this problem, an admittance control method with parameter self-optimization is developed in this study. The hydraulic SEA and its dynamic model are first developed, and then, an admittance controller that combines a passive disturbance observer (DOB) and a feedback compensator is developed based on load movement dynamics. The control law of the framework is made independent of the hydraulic dynamics by considering the uncertainty and tracking error as disturbances. This simplifies the controller computation, enhances system robustness, and facilitates practical application. Next, the control performance is further improved by optimizing the control parameters using an improved crowding-based dynamic population size differential evolution (crowding-based dynNP-DE) algorithm. Benchmark and optimization experiments are performed to verify the superiority of the modified algorithm and obtain the control parameters. Finally, the optimized parameters are applied to practical experiments to validate the improved performance of the proposed admittance control scheme. The results show that the proposed method effectively reduces the SEA stiffness tracking error, with respect to the external contact force.

High strain rate rolling (HSRR) is developed to improve the strength, plasticity and stress corrosion resistance of 7050 aluminum alloy simultaneously. The volume fraction of the small-angle grain boundary in the HSRRed alloy sheet is 89.9%. TEM shows that the presence of dislocations and sub-grains promotes the precipitation of the second phase. The alloy in the T6 state has the highest strength (622 MPa) and the biggest elongation to rupture (20.6%), but poor corrosion resistance. The over-aged state shows the best corrosion resistance. The RRA state can achieve the tensile strength equivalent to T6 of 614 MPa, the highest yield strength of 561 MPa and corrosion resistance comparable to the over-aged state. TEM observation shows the existence of sub-grains and dislocations plays an important role in the nucleation of the second phase. The high strength can be attributed to the higher volume fraction of the precipitates, and the high plasticity can be attributed to the higher work hardening rate. The better corrosion resistance can be ascribe to the wider precipitate-free zones at the grain boundary and the presence of the larger size second phase particles.