Semilocal Kinetic Energy Density Functionals on Atoms and Diatoms.

An accurate semilocal kinetic energy density functional (KEDF) is crucial for reliable orbital-free density functional theory calculations. In our study, we assessed the performance of representative semilocal KEDFs using a more stringent indicator. Our findings highlight the superiority of the Perdew-Constantin (PC) functional in delivering energies close to the reference values. Upon analysis of the PC functional, we identified that enhancing its performance can be achieved through a more effective region selection regime. Experimenting with various region selection indicators, we discovered that the Laplacian-dependent reduced density gradient proves to be helpful. Subsequently, we empirically constructed an augmented variant of the PC functional, which not only yields energies close to the references but also, more importantly, demonstrates qualitative predictions for stable molecules and provides reasonable quantitative estimates for bond lengths in diatomic systems.

Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest.

BACKGROUND: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approach for primary care screening of depression among adolescents. METHODS: The data were from a large cross-sectional study conducted in China from July to September 2021, enrolling 8635 adolescents aged 10-17 with their parents. We used the Patient health questionnaire (PHQ-9) to rate adolescent depression symptoms, using scales and single-item questions to collect demographic information and other variables. Initial model variables screening used the RF importance assessment, followed by building prediction model using the screened variables through the ANN. RESULTS: The rate of depression symptoms in adolescents was 24.6%, and the depression risk prediction model was built based on 70% of the training set and 30% of the test set. Ten variables were included in the final prediction model with a model accuracy of 85.03%, AUC of 0.892, specificity of 89.79%, and sensitivity of 70.81%. The top 10 significant factors of depression risk were adolescent rumination, adolescent self-esteem, adolescent mobile phone addiction, peer victimization, care in parenting styles, overprotection in parenting styles, academic pressure, conflict in parent-child relationship, parental rumination, and relationship between parents. CONCLUSIONS: The ANN model based on the RF effectively identifies depression risk in adolescents and provides a methodological reference for large-scale primary screening. Cross-sectional studies and single-item scales limit further improvements in model accuracy.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

Research on autonomous navigation system of greenhouse electric crawler tractor based on LiDAR.

The application of autonomous navigation technology of electric crawler tractors is an important link in the development of intelligent greenhouses. Aiming at the characteristics of enclosed and narrow space and uneven ground potholes in greenhouse planting, to improve the intelligence level of greenhouse electric crawler tractors, this paper develops a navigation system of electric crawler tractors for the greenhouse planting environment based on LiDAR technology. The navigation hardware system consists of five modules: the information perception module, the control module, the communication module, the motion module, and the power module. The software system is composed of three layers: the application layer, the data processing layer, and the execution layer. The developed navigation system uses LiDAR, Inertial Measurement Unit (IMU) and wheel speed sensor to sense the greenhouse environment and the crawler tractor's information, employs the Gmapping algorithm to build the greenhouse environment map, and utilizes the adaptive Monte Carlo positioning algorithm for positioning. The simulation test of different global path planning algorithms in Matlab shows that the A* algorithm obtains the optimal overall global path. In the scene of map 5, the path planned by the A* algorithm is the most significant, and the number of inflection points is reduced by 40.00% and 87.50%, respectively; meanwhile, the path length is the same as that of the Dijkstra algorithm, but the runtime is reduced by 68.87% and 81.49%, respectively; compared with the RRT algorithm, the path length is reduced by 7.27%. Therefore, the A* algorithm and the Dynamic Window Approach (DWA) method are used for tractor navigation and obstacle avoidance, which ensures global path optimality while also achieving effective local path planning for obstacle avoidance. The test results suggest that the maximum lateral deviation of the built map is 6 cm, and the maximum longitudinal deviation is 16 cm, which meets the requirement of map accuracy. Additionally, the results of the navigation accuracy test indicate that the maximum lateral deviation of navigation is less than 13 cm, the average lateral deviation is less than 7 cm, and the standard lateral deviation is less than 8 cm. The maximum heading deviation is less than 14°, the average heading deviation is less than 7°, and the standard deviation is less than 8°. These results show that the developed navigation system meets the navigation accuracy requirements of electric crawler tractors in the greenhouse environment.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION: This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Comprehensive enhancement of flame retardant starch/cellulose/diatomite composite foams via metal-organic coordination.

In recent years, considerable attention has been given to the utilization of biomass for producing bio-based foams, such as starch-based foams. Despite their renewability and widespread availability, these foams still present certain drawbacks regarding their poor mechanical properties and flammability. To tackle these concerns, a metal ion cross-linking strategy was employed by incorporating calcium ions (Ca2+) solution into foamed starch/cellulose slurry. Followed by ambient drying, starch/cellulose composite foam was successfully fabricated with a remarkable enhancement in various properties. Specifically, compared to the control sample, the compressive strength and modulus increased by 26.2 % and 123.0 %, respectively. Additionally, the Ca2+ cross-linked starch/cellulose composite foam exhibited excellent heat resistance, water stability, and flame retardancy. The limiting oxygen index (LOI) reached 52 %, with a vertical combustion rating of V-0. Along with the addition of 2 phr diatomite, it demonstrated a significant enhancement on flame retardancy with a LOI of 65 %, although the apparent density of the composite foam was not low enough. This study indicated a green and simple method to obtain starch-based composite foams with enhanced comprehensive properties including thermal, water stability, mechanical, and flame retardancy, expanding their potential applications in areas such as building materials and rigid packaging.

Trinitromethyl-Substituted 1H-1,2,4-Triazole Bridging Nitropyrazole: A Strategy of Utterly Manipulable Nitration Achieving High-Energy Density Material.

Nitro groups have been demonstrated to play a decisive role in the development of the most powerful known energetic materials. Two trinitromethyl-substituted 1H-1,2,4-triazole bridging nitropyrazoles were first synthesized by straightforward routes and were characterized by chemical (MS, NMR, IR spectroscopy, and single-crystal X-ray diffraction) and experimental analysis (sensitivity toward friction, impact, and differential scanning calorimetry-thermogravimetric analysis test). Their detonation properties (detonation pressure, detonation velocity, etc.) were predicted by the EXPLO5 package based on the crystal density and calculated heat of formation with Gaussian 09. These new trinitromethyl triazoles were found to show suitable sensitivities, high density, and highly positive heat of formation. The combination of exceedingly high performances superior to those of HMX (1,3,5,7-tetranitrotetraazacyclooctane), and its straightforward preparation highlights compound 8 as a promising high-energy density material (HEDM). This work supports the effectivity of utterly manipulable nitration and provides a generalizable design synthesis strategy for developing new HEDMs.

Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76-0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70-8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73-2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.

Rapidly mining candidate cotton drought resistance genes based on key indicators of drought resistance.

BACKGROUND: Focusing on key indicators of drought resistance is highly important for quickly mining candidate genes related to drought resistance in cotton. RESULTS: In the present study, drought resistance was identified in drought resistance-related RIL populations during the flowering and boll stages, and multiple traits were evaluated; these traits included three key indicators: plant height (PH), single boll weight (SBW) and transpiration rate (Tr). Based on these three key indicators, three groups of extreme mixing pools were constructed for BSA-seq. Based on the mapping interval of each trait, a total of 6.27 Mb QTL intervals were selected on chromosomes A13 (3.2 Mb), A10 (2.45 Mb) and A07 (0.62 Mb) as the focus of this study. Based on the annotation information and qRT‒PCR analysis, three key genes that may be involved in the drought stress response of cotton were screened: GhF6'H1, Gh3AT1 and GhPER55. qRT‒PCR analysis of parental and extreme germplasm materials revealed that the expression of these genes changed significantly under drought stress. Cotton VIGS experiments verified the important impact of key genes on cotton drought resistance. CONCLUSIONS: This study focused on the key indicators of drought resistance, laying the foundation for the rapid mining of drought-resistant candidate genes in cotton and providing genetic resources for directed molecular breeding of drought resistance in cotton.

Antibiotic altered liver damage induced by aflatoxin B1 exposure in mice by modulating the gut microbiota.

Aflatoxins B1 (AFB1) and antibiotic (AN) carry co-exposure risks, with the gut being a target organ for their combined effects. However, the current understanding of the impact of AN on gut and liver injury induced by AFB1 remains limited. In this study, we conducted a 9-week investigation into the implications of AN (ampicillin and penicillin) treatment on AFB1-induced intestinal and liver injury in C57BL/6J male mice fed a normal diet (ND) and a high-fat diet (HFD). The results showed that AN treatment significantly reduce the total number and diversity of intestinal species in both ND and HFD mice exposed to AFB1. Moreover, AN treatment alleviated AFB1-induced liver injury and lipid accumulation in mice on ND and HFD, while improving abnormal lipid metabolism in the liver and serum. However, AN treatment also promoted intestinal damage and reduced the levels of short-chain fatty acids in the gut. Correlation analysis demonstrated that, under the two dietary patterns, microorganisms across various genera were significantly positively or negatively correlated with alterations in liver, serum, and intestinal biochemical indexes. These genera include Akkermansia, Robinsoniella, Parabacteroides, Escherichia-Shigel, and Parabacteroides, Odoribacter. AN may alleviate long-term AFB1-induced liver injury through the regulation of intestinal microorganisms, with the effect being more pronounced in mice following an HFD pattern. These findings provide novel insights into the effects of AFB1 on the gut‒liver axis under complex exposure conditions, as well as the relationship between gut microbial homeostasis and liver injury across different dietary patterns.

Synthesis and detonation characters of 3,4,5-1H-trinitropyrazole and its nitrogen-rich energetic salts.

The development of energetic materials is still facing challenges due to the inherent contradiction between energy and sensitivity. Two new nitrogen-rich energetic salts of 3,4,5-1H-trinitropyrazole (HTNP) were synthesized. They are fully characterized by X-ray diffraction, NMR, MS and IR spectroscopy. The DSC and BAM tests were carried out as well. These TNP salts show high thermostability and high positive heat of formation. Their detonation performances were calculated by the EXPLO5 program. Most noteworthy is that DATr salt exhibits superior sensitivity and detonation performance comparable to secondary explosive RDX, making it promising for use as a new-generation green energetic material.

Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis.

BACKGROUND: Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS: Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS: The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).

Preparation of Highly Energetic Coordination Compounds with Rich Oxidants and Lower Sensitivity Based on Methyl Groups.

Revamping the structure of energy storage is an efficient strategy for striking a balance between the performance and sensitivity of energetic materials to achieve high energy and reduced sensitivity. In continuation of prior research, this study utilized the ligand 3,5-dimethyl-1H-pyrazole-4-carbonhydrazide (DMPZCA) and innovatively designed and synthesized the compound ECCs [Cu(HDMPZCA)2(ClO4)2](ClO4)2·2H2O (ECCs-1·2H2O). Compared with the former research, solvent-free compound ECCs-1 refers to an innovative material characterized by a dual structure involving ionic salts and coordination compounds. Due to these unique structures, ECCs-1 exhibits an increased [ClO4-] content, a higher oxygen balance constant (OB = -7.9%), and improved mechanical sensitivity (IS = 8 J, FS = 32 N). Theoretical calculations support the superior detonation performance of ECCs-1. Additionally, experimental results confirm its ignition capability through lower-threshold lasers and highlight the outstanding initiation potential and explosive power, making it a suitable candidate for primary explosives.

Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients.

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Regulating the Coordination Environment by Using Isomeric Ligands: Enhancing the Energy and Sensitivity of Energetic Coordination Compounds.

Transforming the energy storage structure is an effective approach to achieve a balance between the detonation performance and the sensitivity of energetic compounds, with a goal of high energy and low sensitivity. Building upon previous work, this study employed an isomeric compound 1H-pyrazole-3-carbohydrazide (3-PZCA) as a ligand and creatively designed the energetic coordination compound (ECC) Ag(3-HPZCA)2(ClO4)3 (ECC-1). It is a novel material with a dual structure of ionic salts and coordination compounds, which represents the first report of such a structure in Ag(I)-based ECCs. With its unique structures, ECC-1 exhibits a larger [ClO4-] content, a higher oxygen balance constant (OB = 0%), and superior mechanical sensitivity (IS = 13 J and FS = 40 N). Theoretical calculations indicate that ECC-1 has a higher detonation performance compared to previous work. Furthermore, the explosive experiment testing results demonstrate that it can be ignited by lower-threshold lasers and possesses excellent initiation capability and explosive power, making it suitable not only as a primary explosive but also as a secondary explosive.

Predictors of long-term decannulation in patients with disorders of consciousness.

Purpose: The predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC) are not comprehensively understood, making prognosis difficult. The primary objective of this study was to identify predictors of tracheostomy decannulation in patients with disorders of consciousness (DOC). The secondary aim was to evaluate the feasibility and safety of the modified Evans blue dye test (MEBDT) in tracheostomized DOC patients. Methods: This retrospective study included all patients with disorders of consciousness (DOC) who underwent tracheostomy and were admitted between January 2016 and September 2022. Age, sex, etiology, initial Glasgow coma scale (GCS), initial Coma Recovery Scale-Revised (CRS-R), diagnosis of unresponsive wakefulness syndrome (UWS) or minimal consciousness state (MCS), MEBDT, initial modified Rankin scale (mRS), and initial Functional Oral Intake Scale (FOIS) were collected upon study enrollment. The relationship between clinical characteristics and cannulation status was investigated through a Cox regression model. Results: A total of 141 patients were included in the study. The average age of these patients was 52.5 ± 16.7 years, with 42 (29.8%) being women. During the study period, 86 subjects (61%) underwent successful decannulation. Univariate analysis revealed that decannulated patients exhibited a significantly better conscious state compared to those without decannulation (CRS-R: p < 0.001; GCS: p = 0.023; MCS vs. UWS: p < 0.001). Additionally, a negative modified Evans blue dye test (MEBDT) result was significantly associated with tracheostomy decannulation (p < 0.001). In the multivariate analysis, successful decannulation was associated with a higher level of consciousness (MCS vs. UWS, p < 0.001, HR = 6.694) and a negative MEBDT result (negative vs. positive, p = 0.006, HR = 1.873). The Kaplan-Meier analysis further demonstrated that MEBDT-negative patients and those in the MCS category had a higher probability of decannulation at 12 months (p < 0.001). Conclusion: The findings of this study indicate that a negative MEBDT result and a higher level of consciousness can serve as predictive factors for successful tracheostomy decannulation in DOC patients.

Glutamine deficiency drives transforming growth factor-ß signaling activation that gives rise to myofibroblastic carcinoma-associated fibroblasts.

Tumor-promoting carcinoma-associated fibroblasts (CAFs), abundant in the mammary tumor microenvironment (TME), maintain transforming growth factor-ß (TGF-ß)-Smad2/3 signaling activation and the myofibroblastic state, the hallmark of activated fibroblasts. How myofibroblastic CAFs (myCAFs) arise in the TME and which epigenetic and metabolic alterations underlie activated fibroblastic phenotypes remain, however, poorly understood. We herein show global histone deacetylation in myCAFs present in tumors to be significantly associated with poorer outcomes in breast cancer patients. As the TME is subject to glutamine (Gln) deficiency, human mammary fibroblasts (HMFs) were cultured in Gln-starved medium. Global histone deacetylation and TGF-ß-Smad2/3 signaling activation are induced in these cells, largely mediated by class I histone deacetylase (HDAC) activity. Additionally, mechanistic/mammalian target of rapamycin complex 1 (mTORC1) signaling is attenuated in Gln-starved HMFs, and mTORC1 inhibition in Gln-supplemented HMFs with rapamycin treatment boosts TGF-ß-Smad2/3 signaling activation. These data indicate that mTORC1 suppression mediates TGF-ß-Smad2/3 signaling activation in Gln-starved HMFs. Global histone deacetylation, class I HDAC activation, and mTORC1 suppression are also observed in cultured human breast CAFs. Class I HDAC inhibition or mTORC1 activation by high-dose Gln supplementation significantly attenuates TGF-ß-Smad2/3 signaling and the myofibroblastic state in these cells. These data indicate class I HDAC activation and mTORC1 suppression to be required for maintenance of myCAF traits. Taken together, these findings indicate that Gln starvation triggers TGF-ß signaling activation in HMFs through class I HDAC activity and mTORC1 suppression, presumably inducing myCAF conversion.

Optimization of performance and sensitivity: preparation of two Ag(I)-based ECPs by using isomeric ligands.

For energetic compounds, their structure determines their performance, and even minor variations in their structure can have a significant impact on their performance. The application scenarios for energetic materials are diverse, and their performance requirements vary as well. To investigate the influence of different substituent positions on the performance of primary explosives, we prepared two Ag(I)-based complexes, [Ag(2-IZCA)ClO4]n (ECPs-1) and [Ag(4-IZCA)ClO4]n (ECPs-2), using structurally isomeric ligands, 1H-imidazole-2-carbohydrazide (2-IZCA) and 1H-imidazole-4-carbohydrazide (4-IZCA). The structures were confirmed using infrared, elemental analysis, and single-crystal X-ray diffraction. Experimental results demonstrate that both ECPs exhibit good thermal stability. However, compared to ECPs-1, ECPs-2 exhibits a lower thermal initial decomposition temperature (Td = 210 °C), lower mechanical sensitivity (IS = 27 J, FS = 84 N), and more concentrated energy output. Although theoretical predictions suggest similar detonation velocities and pressures for both compounds, actual detonation performance tests indicate that ECPs-2 has stronger explosive power and initiating capability, with potential for use as a laser initiator (E = 126 mJ). The simple preparation method and inexpensive starting materials enrich the research on primary explosives.