A case study of displacement, stress condition and failure criterion of surrounding rock in deep super-large section double chambers in the Longgu Coal Mine, Shandong, China.

To investigate the stability of a super-large section chamber group, an analysis was conducted based on the in-situ geological conditions of a super-large section chamber group utilized as a coal gangue separation system at the Longgu Coal Mine. Field measurement and numerical simulation were employed to analyze the failure area and stress state of the surrounding rock under varying chamber spacings. The results indicate that the extent of the plastic zone significantly expands when the spacing between chambers is less than 2.0 times the chamber width. When the distance between chambers is 1.5 times the chamber width, it results in the rock pillars being entirely within the plastic zone. As the chamber spacing decreases, the tangential stress within the rock pillar range increases. When the chamber spacing is 2.5, 2.0, and 1.5 times the chamber width, the maximum tangential stress is 1.19, 1.46, and 1.18 times that in the case of a single chamber, respectively. Based on the displacement analysis, it was observed that as the distance between the chambers decreases, there is a notable increase in the displacement of the pillar sides and chamber top, indicating a higher risk of collapse. Integrating the plastic area and stress analysis allows for the categorization of the rock pillar area into four sections: the broken area, loose area, stable area, and firm area. Drawing upon the theoretical solution of the plastic zone of a circular chamber and the equivalent radius method, an approximate solution for the plastic zone of a non-circular chamber has been provided. Furthermore, the minimum reasonable spacing between chambers in a super-large section chamber group is provided as a distance criterion for the failure of a double chamber.

The Effectiveness and Safety of Acupuncture for Mammary Hyperplasia: A Systematic Review and Meta-Analysis.

Objective: This study aims to systematically evaluate the effectiveness and safety of acupuncture in treating mammary hyperplasia. Methods: A comprehensive search was conducted in various databases, including PubMed, Web of Science, Cochrane Library, Embase, SinoMed, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP Database, from their inception until July 2023. Only randomized controlled trials (RCTs) researching the use of acupuncture for mammary hyperplasia were included. Quality assessment and data analysis were performed using RevMan 5.3 software. Results: Eight RCTs, comprising 573 patients, were included in this analysis. The meta-analysis revealed that in the acupuncture group, the experimental group was better than the control group in improving breast pain, breast lump extent, and the texture of breast lump (P=0.0007, I2=61%; P=0.02, I2=0%; P=0.0002, I2=0%). We found that both could be improved, but after statistical analysis, there was no significant advantage in the acupuncture group compared with the patent Chinese medicine group (p>0.05). Conclusion: Acupuncture appears to be an effective and safe treatment for mammary hyperplasia, providing pain relief and reducing mass volume, texture, and extent. However, the insufficient quality of the available evidence indicates the need for further methodologically rigorous and convincingly designed studies to determine the efficacy and safety of acupuncture in the treatment of breast hyperplasia.

Wake biofilm up to enhance suicidal uptake of gallium for chronic lung infection treatment.

The hypometabolic and nutrient-limiting condition of dormant bacteria inside biofilms reduces their susceptibility to antibacterial agents, making the treatment of biofilm-dominating chronic infections difficult. Herein, we demonstrate an intratracheal aerosolized maltohexaose-modified catalase-gallium integrated nanosystem that can 'wake up' dormant Pseudomonas aeruginosa biofilm to increase the metabolism and nutritional iron demand by reconciling the oxygen gradient. The activated bacteria then enhance suicidal gallium uptake since gallium acts as a 'Trojan horse' to mimic iron. The internalized gallium ions disrupt biofilms by interfering with the physiological processes of iron ion acquisition and utilization, biofilm formation, and quorum sensing. Furthermore, aerosol microsprayer administration and bacteria-specific maltohexaose modification enable accumulation at biofilm-infected lung and targeted release of gallium into bacteria to improve the therapeutic effect. This work provides a potential strategy for treating infection by reversing the dormant biofilm's resistance condition.

Unveiling the role of climate in spatially synchronized locust outbreak risks.

Desert locusts threaten crop production and food security. Spatially synchronized locust outbreaks further exacerbate these crises. Continental-scale understanding of such compound locust risks and underlying climatic drivers is crucial to designing coordinated and predictive control strategies but remains elusive. Here, we develop a data-driven framework to assess the compound risk of locust outbreaks in the Middle East and North Africa (MENA) and elucidate the role of climate in locust dynamics. We find that more than one-fifth of high-risk country pairs faced spatially synchronized locust risks from 1985 to 2020, dominated by concurrent winds or inundations. Individual locusts are more prone to infest arid areas punched by extreme rainfall. The spatial prevalence of locusts is strongly modulated by climate variability such as El Niño-Southern Oscillation. A warming climate will lead to widespread increases in locust outbreaks with emerging hotspots in west central Asia, posing additional challenges to the global coordination of locust control.

Advances in fibreoptic ductoscopy for the diagnosis and treatment of pathologic papillary overflow.

Fibreoptic mammography is widely recognised as the first screening method for pathologic papillary overflow due to its significant advantages in the diagnosis of ductal dilatation, intraductal papilloma and intraductal carcinoma. The use of fibreoptic ductoscopic excisional biopsy techniques, such as biopsy needles, vacuum negative pressure aspiration, biopsy forceps and grasping baskets, has not been promoted largely due to their existing deficiencies. The imaging effect of fibreoptic ductoscopy compared with electronic ductoscopy is also one of the important factors limiting the progress of microscopic excisional biopsy techniques. Finding a more suitable operating space for electronic fibreoptic ductoscopy and the use of electrosurgical excision biopsy techniques should be the focus of research in view of achieving accurate diagnoses in electronic fibreoptic ductoscopy and microscopic excision biopsy. In this review, the development history, clinical application and existing problems of fibreoptic ductoscopy are reviewed and assessed to provide references for the clinical diagnosis and treatment of pathologic papillary overflow.

Risk Factors for Granulomatous Mastitis and Establishment and Validation of a Clinical Prediction Model (Nomogram).

Background: This study aimed to explore the risk factors and clinical characteristics of granulomatous mastitis (GM) using a case-control study and establish and validate a clinical prediction model (nomogram). Methods: This retrospective case-control study was conducted in three hospitals in China from June 2017 to December 2021. A total of 1634 GM patients and 186 healthy women during the same period were included and randomly divided into the modeling and validation groups in a 7:3 ratio. To identify the independent risk factors of GM, univariate and multivariate logistic analyses were conducted and used to develop a nomogram. The prediction model was internally and externally validated using the Bootstrap technique and validation cohort. The receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the discrimination and calibration of the prediction model. Decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical significance of the model. Results: The average age of GM patients was 33.14 years (mainly 20-40). The incidence was high within five years from delivery and mainly occurred in the unilateral breast. The majority of the patients exhibited local skin alterations, while some also presented with systemic symptoms. On multivariate logistic analysis, age, high prolactin level, sex hormone intake, breast trauma, nipple discharge or invagination, and depression were independent risk factors for GM. The mean area under the curve (AUC) in the modeling and validation groups were 0.899 and 0.889. The internal and external validation demonstrated the model's predictive ability and clinical value. Conclusion: Lactation-related factors are the main risk factors of GM, leading to milk stasis or increased ductal secretion. Meanwhile, hormone disorders could affect the secretion and expansion of mammary ducts. All these factors can obstruct or injure the duct, inducing inflammatory reactions and immune responses. Additionally, blunt trauma, depressed mood, and diet preference can accelerate the process. The nomogram can effectively predict the risk of GM.

Predisposing Factors for Granulomatous Lobular Mastitis: A Case-Control Study.

Background: Despite the rising incidence rate of granulomatous lobular mastitis (GLM), uncertainties persist about its etiologic and predisposing factors to guide clinical treatment and early prevention. The objective of this study is to explore the predisposing factors for GLM. Patients and methods: This case-control study was conducted from 2018 to 2021 at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University. Patients with GLM (cases) were matched with healthy examinees (controls) in a 1:1 ratio according to gender and living area. We analyzed their demographic features and investigated 75 factors that may be relevant to GLM using a standard questionnaire. Univariate and multivariable binary conditional logistic regression analyses were used to compare the differences between the two groups and evaluate the predisposing factors that may induce GLM. Results: There were 594 female GLM patients and 594 matched controls included in the study. The average age of the cases was 32.78 years (mainly 20 to 40). The incidence was high within five years after childbirth, and lesions were mainly in the unilateral breast. Univariate and multivariable conditional logistic regression analyses obtained six relevant factors and six high-risk factors. The six relevant factors included age, marriage, emotional abnormality, high prolactin, psychiatric drug intake, and sex hormone intake. Additionally, the independent high-risk factors for GLM included gestation, nipple invagination, blunt trauma, non-iatrogenic massage, lactation disorder, and nipple discharge (odds ratio (OR)=17.378, 8.518, 4.887, 3.116, 2.522, 1.685, P<0.05). Menopause was an independent protective factor (OR=0.249, P<0.05). Conclusion: The factors that increase milk and secretion production in the mammary duct are the main risk factors of GLM, especially when the nipples are invaginated. These factors can obstruct the duct and induce inflammation. Additionally, hormonal disorders, extrinsic trauma, and emotional abnormalities can accelerate the occurrence of GLM.

Cascaded Degradation-Aware Blind Super-Resolution.

Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation, especially in the case of the real world. To tackle this robustness issue, we propose a cascaded degradation-aware blind super-resolution network (CDASRN), which not only eliminates the influence of noise on blur kernel estimation but also can estimate the spatially varying blur kernel. With the addition of contrastive learning, our CDASRN can further distinguish the differences between local blur kernels, greatly improving its practicality. Experiments in various settings show that CDASRN outperforms state-of-the-art methods on both heavily degraded synthetic datasets and real-world datasets.

Oral Microalgae-Nano Integrated System against Radiation-Induced Injury.

The increasing applications of ionizing radiation in society raise the risk of radiation-induced intestinal and whole-body injury. Astaxanthin is a powerful antioxidant to reduce the reactive oxygen generated from radiation and the subsequent damage. However, the oral administration of astaxanthin remains challenging owing to its low solubility and poor bioavailability. Herein, we facilely construct an orally used microalgae-nano integrated system (SP@ASXnano) against radiation-induced intestinal and whole-body injury, combining natural microalgae Spirulina platensis (SP) with astaxanthin nanoparticles (ASXnano). SP and ASXnano show complementation in drug delivery to improve distribution in the intestine and blood. SP displays limited gastric drug loss, prolonged intestinal retention, constant ASXnano release, and progressive degradation. ASXnano improves drug solubility, gastric stability, cell uptake, and intestinal absorption. SP and ASXnano have synergy in many aspects such as anti-inflammation, microbiota protection, and fecal short-chain fatty acid up-regulation. In addition, the system is ensured with biosafety for long-term administration. The system organically combines the properties of microalgae and nanoparticles, which was expected to expand the medical application of SP as a versatile drug delivery platform.

Clinical characteristics and microbiota analysis of 44 patients with granulomatous mastitis.

Introduction: Granulomatous mastitis (GM) is a chronic inflammatory breast disease. In recent years, the role of Corynebacterium in GM onset has received more and more attention. This study aims to detect the dominant bacterium in GM patients and analyze the association between clinical characteristics and infectious factors. Methods: In this study, 88 samples from 44 GM patients, six acute lactation mastitis (ALM) patients, and 25 non-inflammatory breast disease (NIB) patients were divided into a GM pus group, a GM tissue group, an ALM pus group, and a NIB tissue group; then, 16S ribosomal DNA sequencing was used to explore their microbiota. The clinical data of all 44 GM patients were also retrospectively collected and analyzed to determine their relationship with infection. Results: The median age of the 44 GM patients was 33 years, and 88.6% of patients had primary-onset cases, while 11.4% were recurrences; additionally, 89.5% of patients were postpartum and 10.5% were nulliparous. The serum prolactin level was abnormal in nine patients (24.3%). Samples from 15 GM patients (34.1%) had a Corynebacterium abundance of >1% (1.08-80.08%), with eight (53.3%) displaying an abundance of >10%. Corynebacterium was the only genus with significant differences between the GM pus group and the other three groups (p < 0.05). Corynebacterium kroppenstedtii was the predominant Corynebacterium species. Among clinical characteristics, a statistical difference in breast abscess formation was observed according to Corynebacterium abundance in Corynebacterium-positive and- negative patients (p < 0.05). Discussion: This study explored the relationship between Corynebacterium infection and GM, compared the clinical characteristics between Corynebacterium-positive and- negative patients, and provided support for the role of Corynebacterium species-in particular, C. kroppenstedtii-in the pathogenesis of GM. The detection of Corynebacterium can predict GM onset, especially in patients with high prolactin levels or a history of recent lactation.

Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.

The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incorporated into the machine learning model to achieve multi-column prediction, which paves the way for chromatographic enantioseparation prediction by calculating the separation probability. The proposed research framework works well in retention time prediction and chromatographic enantioseparation facilitation, which sheds light on the application of machine learning techniques to the experimental scene and improves the efficiency of experimenters to speed up scientific discovery.

Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion.

Data-driven discovery of partial differential equations (PDEs) has recently made tremendous progress, and many canonical PDEs have been discovered successfully for proof of concept. However, determining the most proper PDE without prior references remains challenging in terms of practical applications. In this work, a physics-informed information criterion (PIC) is proposed to measure the parsimony and precision of the discovered PDE synthetically. The proposed PIC achieves satisfactory robustness to highly noisy and sparse data on 7 canonical PDEs from different physical scenes, which confirms its ability to handle difficult situations. The PIC is also employed to discover unrevealed macroscale governing equations from microscopic simulation data in an actual physical scene. The results show that the discovered macroscale PDE is precise and parsimonious and satisfies underlying symmetries, which facilitates understanding and simulation of the physical process. The proposition of the PIC enables practical applications of PDE discovery in discovering unrevealed governing equations in broader physical scenes.

Radiation-assisted strategies provide new perspectives to improve the nanoparticle delivery to tumor.

Nanoparticles (NPs), with advantages in tumor targeting, have been extensively developed for anticancer treatment. However, the delivery efficacy of NPs tends to be heterogeneous in clinical research. Surprisingly, a traditional cancer treatment, radiotherapy (radiation), has been observed with the potential to improve the delivery of NPs by influencing the features of the tumor microenvironment, which provides new perspectives to overcome the barriers in the NPs delivery. Since the effect of radiation can also be enhanced by versatile NPs, these findings of radiation-assisted NPs delivery suggest innovative strategies combining radiotherapy with nanotherapeutics. This review summarizes the research on the delivery and therapeutic efficacy of NPs that are improved by radiation, focusing on relative mechanisms and existing challenges and opportunities.

Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?

The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, the framework of semantic explainable artificial intelligence (S-XAI) is introduced, which utilizes a sample compression method based on the distinctive row-centered principal component analysis (PCA) that is different from the conventional column-centered PCA to obtain common traits of samples from the convolutional neural network (CNN), and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed. The experimental results demonstrate that S-XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching.

Visualized Gallium/Lyticase-Integrated Antifungal Strategy for Fungal Keratitis Treatment.

Fungal keratitis has been one of the common corneal infections that causes blindness, but an effective antifungal strategy remains a challenge. The exopolysaccharides both in the fungal cell walls and biofilms are a key that acts as a permeation barrier to weaken the therapeutic effect of antifungal agents. Herein, lyticase and gallium ions co-integrated nanosystems (MLPGa) are presented that can degrade exopolysaccharides and then effectively eradicate both planktonic Candida albicans and mature biofilms. The potential antifungal mechanism involves reactive oxygen species (ROS) production and metabolic interference of antioxidant-related genes, exopolysaccharide-related genes, iron-ion-utilization-related genes, fungal/biofilm-development-related genes, and virulence genes. Meanwhile, the Raman signals generated by the chelation between the nanosystems and the gallium ions provide a real-time visualization tool to monitor Ga release. Finally, the MLPGa-based antifungal strategy with good biocompatibility achieves a satisfactory therapeutic effect in a fungal keratitis mouse model. This study provides a unique approach to the effective treatment of fungal keratitis in clinical practice.

Search for rogue waves in Bose-Einstein condensates via a theory-guided neural network.

An important and incompletely answered question is whether machine learning methods can be used to discover the excitation of rogue waves (RWs) in nonlinear systems, especially their dynamic properties and phase transitions. In this work, a theory-guided neural network (TgNN) is constructed to explore the RWs of one-dimensional Bose-Einstein condensates. We find that such method is superior to the ordinary deep neural network due to theory guidance of underlying problems. The former can directly give any excited location, timing, and structure of RWs using only a small amount of dynamic evolution data as the training data, without the tedious step-by-step iterative calculation process. In addition, based on the TgNN approach, a phase transition boundary is also discovered, which clearly distinguishes the first-order RW phase from the non-RW phase. The results not only greatly reduce computational time for exploring RWs, but also provide a promising technique for discovering phase transitions in parameterized nonlinear systems.

Astaxanthin Activated the Nrf2/HO-1 Pathway to Enhance Autophagy and Inhibit Ferroptosis, Ameliorating Acetaminophen-Induced Liver Injury.

Acetaminophen (APAP)-induced liver injury (AILI) is a common liver disease in clinical practice. Only one clinically approved drug, N-acetylcysteine (NAC), for the treatment of AILI is available in clinics, but novel treatment strategies are still needed due to the complicated pathological changes of AILI and the side effects of NAC. Here, we found that astaxanthin (ASX) can prevent AILI through the Nrf2/HO-1 pathway. After treatment with ASX, there was a positive activation of the Nrf2/HO-1 pathway in AILI models both in vivo and in vitro accompanied by enhanced autophagy and reduced ferroptosis. In APAP-challenged L02 liver cells, ASX reduced autophagy and enhanced apoptosis of the cells. Furthermore, we developed ASX-loaded hollow mesoporous silica nanoparticles (HMSN@ASX) to improve the aqueous solubility of ASX and targeted delivery of ASX to the liver and then significantly improve the therapeutic effects. Taken together, we found that ASX can protect against AILI by activating the Nrf2/HO-1 pathway, which mainly affects oxidative stress, autophagy, and ferroptosis processes, and the HMSN@ASX nanosystem can target the liver to enhance the treatment efficiency of AILI.

Microalgae-based oral microcarriers for gut microbiota homeostasis and intestinal protection in cancer radiotherapy.

Protecting the whole small intestine from radiation-induced intestinal injury during the radiotherapy of abdominal or pelvic solid tumors remains an unmet clinical need. Amifostine is a promising selective radioprotector for normal tissues. However, its oral application in intestinal radioprotection remains challenging. Herein, we use microalga Spirulina platensis as a microcarrier of Amifostine to construct an oral delivery system. The system shows comprehensive drug accumulation and effective radioprotection in the whole small intestine that is significantly superior to free drug and its enteric capsule, preventing the radiation-induced intestine injury and prolonging the survival without influencing the tumor regression. It also shows benefits on the gut microbiota homeostasis and long-term safety. Based on a readily available natural microcarrier, this work presents a convenient oral delivery system to achieve effective radioprotection for the whole small intestine, providing a competitive strategy with great clinical translation potential.

High-throughput automated platform for thin layer chromatography analysis.

In this protocol, we detail steps for constructing a high-throughput automated platform for thin layer chromatography (TLC) analysis. We describe robotics and computer vision techniques that can handle 32 compounds under three different elution solvents in about 50 min. The established automated platform can obtain statistically standardized retardation factor (Rf) values and enhance reproducibility while reducing labor and time costs. For complete details on the use and execution of this protocol, please refer to Xu et al. (2022).1.

Corrigendum: Upregulation of Mir342 in Diet-Induced Obesity Mouse and the Hypothalamic Appetite Control.

[This corrects the article DOI: 10.3389/fendo.2021.727915.].