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Abstract Osteosarcoma (OS) is the most common primary malignant bone tumor. Recent evidence suggests that the novel long noncoding RNA DHRS4 antisense RNA 1 (DHRS4-AS1) serves an important role in cancer progression and metastasis. However, its function and molecular mechanism in OS remain largely unknown. In the present study, DHRS4-AS1 expression was detected in OS cells by quantitative PCR. Gain- and loss-of-function experiments were conducted to study the effects of DHRS4-AS1 on the proliferation and apoptosis of OS cells. The potential mechanism of DHRS4-AS1 was examined through bioinformatics analysis and rescue experiments. DHRS4-AS1 was downregulated in OS cell lines. DHRS4-AS1 depletion promoted proliferation and inhibited apoptosis in OS cells, whereas DHRS4-AS1 overexpression had the opposite effects. Further research suggested that DHRS4-AS1 inhibited OS progression by regulating the microRNA-362-5p/aminopeptidase puromycin sensitive axis. The present findings suggested that DHRS4-AS1 may serve as a potential therapeutic target for OS.

Abstract Oxidative stress and neuroinflammation are recognized as key factors in the development of neurodegenerative diseases, yet effective interventions and biomarkers to address oxidative stress and neuroinflammation in these conditions are limited. Uric acid (UA), traditionally associated with gout, is now gaining prominence as a potential target in neurodegenerative diseases. Soluble UA stands out as one of the most vital antioxidant compounds produced by the human body, accounting for up to 55% of the extracellular capacity to neutralize free radicals. While there is increasing evidence supporting the neuroprotective properties of UA in Parkinson’s disease and Alzheimer’s disease, gaps in knowledge still exist regarding the underlying mechanisms and how to effectively translate these benefits into clinical practice. Moreover, the current UA elevation therapy exhibits unstable antioxidant properties, individual variability, and even adverse effects, limiting its potential clinical applications. This review consolidates recent advancements in understanding how UA exerts neuroprotective effects on neurodegenerative diseases and emphasizes the dual roles of UA in managing oxidative stress and neuroinflammation. Additionally, the review elucidates the mechanisms through which UA confers neuroprotection. Based on this, the review underscores the significance of UA as a potential biomarker and aims to provide a comprehensive understanding of its potential as a therapeutic target, while also addressing possible challenges to clinical implementation.

Titanium and its alloys are promising structural materials for advanced aircraft engine applications. However, they are susceptible to ignition and combustion because of their active chemical performance, high combustion heat, and poor heat conductivity. In this study, the ignition and flame propagation behaviors of Ti, TC4 (Ti–6Al–4V), TC11 (Ti-6.5Al-3.5Mo-1.5Zr-0.3Si), and TC17 (Ti–5Al–2Sn–2Zr–4Mo–4Cr) alloys were systematically investigated and compared using promoted ignition-combustion tests in oxygen-enriched environments. The results show that the ignition phenomenon of the four titanium alloys all involve a sharp increase in temperature, explosive melting, and visible light. Nevertheless, the ignition temperature, maximum temperature and molten pool temperature follow the order as TC17>TC11>TC4>Ti. The critical pressures of the four alloys increases with increasing sample size and decreasing oxygen concentration, whereas ignition temperature of the four alloys decreases with increasing oxygen pressure. The activation energy for ignition of the four alloys are determined, and the difference in the activation energy for ignition among the four alloys is very slight. Assessment of the relevant burning kinetics reveal that the burning velocity for the four alloys increases with increasing oxygen pressure. The activation energy for combustion of Ti, TC4, TC11, and TC17 alloys are determined to be 131.20 kJ mol−1, 136.57 kJ mol−1, 150.56 kJ mol−1, and 100.19 kJ mol−1 respectively by fitting the relationship between oxygen pressure and molten pool temperature. Furthermore, the effects of alloy elements on ignition critical conditions, and the burning kinetics controlled by the melting point of solid-liquid interface and activation energy for combustion are discussed.

Abstract Frequent forest fires, driven by hotter and drier climates, threaten biodiversity and human health, causing significant economic losses, air pollution, soil erosion, and degeneration. Current active and passive fire protection methods often suffer from environmental pollution, poor flexibility, and limited availability in remote areas. However, fast-acting surface flame retardants for passive forest fire protection, particularly for foliage, are rare. Herein, we report an easily obtainable gelatin-based fire spray, which resulted in 1.8 and 16.3-fold extension in ignition time, 34% and 39% reductions in total heat release, 78% and 92% reductions in fire growth index for dead and fresh leaves, respectively. After the fire warning is suppressed, for instance by rain, the sprayed substances can decompose and provide nitrogen and phosphorus as leaf and soil fertilizers without affecting soil microbial function, which increase plant net photosynthesis by 84% and effective nitrogen and phosphorus by 664% and 140%, respectively. Our green flame retardant and fertilizer material allows for simultaneous tree fire protection and growth.

Due to the high dispersion of solid powder, it is very easy to form a powder/gas hybrid system, which greatly increases the probability and severity of explosion accidents. The researchers both domestically and internationally have conducted extensive research on the hybrids explosion systems. This paper reviews the combustion characteristics, explosion mechanisms, and explosion prevention experiments of hybrids. Current research results on explosion prevention technology are organized, and the current challenges in experimental studies of hybrids explosions are summarized, providing references for future experimental studies.

Land and soil resources are scarce in the Tibetan Plateau, and the region is facing ecological pressure from climate warming and increasing human activities. As a major ecological problem, gully erosion is destroying land and soil resources on the Tibetan Plateau, but related research is limited, and susceptibility areas and influencing factors are unclear. Machine learning methods are often applied to study gully initiation susceptibility, but they require a programming foundation. Therefore, the Redui watershed on the southern Tibetan Plateau with severe gully erosion was selected to evaluate the susceptibility and influencing factors of gully initiation through 12 influencing factors including topography, human activity, and underlying surface conditions, and all 2310 gully headcut sites. Two non-code nonlinear modeling methods, the categorical Regression (CATREG) and geographical detector (Geodetector) methods, were first used in the spatial modeling of gully initiation susceptibility. The results showed that the gully initiation susceptibility of the hillslope around the alluvial fan was highest. The very high susceptibility areas of the CATREG model and Geodetector model account for 18.2% and 16% of the total, respectively. The main influencing factors of gully initiation were elevation, relief, and soil type recognized by CATREG, and elevation, human footprint, and soil type recognized by Geodetector. Elevation is the primary factor controlling downstream susceptibility in both models. The primary factors in the upper and middle reaches are soil type and relief identified by CATREG. Human footprint, soil type, and distance to road are primary factors in the upper and middle reaches identified by Geodetector. The explanatory power of elevation, elevation-relief interaction, Geodetector model and CATREG model were 39%, 54%, 46.4% and 73.8%, respectively, at extremely significant levels (P

Abstract This paper aims to propose a faster and more accurate network for human spatiotemporal action localization tasks. Like the YOWO model, we also use convolutional neural networks (CNNs) for feature extraction, but our model differs from YOWO in three significant ways: firstly, we don’t use the feature fusion strategy, we only use spatial features extracted by 2D CNNs for action localization and spatiotemporal features extracted by 3D CNNs for action recognition; secondly, we make an improvement to the 2D CNNs network by introducing a coordinate attention mechanism and utilize the CIoU loss instead of the coordinate offset loss for bounding box regression; thirdly, we provide a more lightweight and faster spatiotemporal action localization architecture, which reduces the number of parameters by 21.76 million and achieves a speed of 39 fps on 16-frame input clips compared to the YOWO model. We test our model’s performance on three public datasets: UCF-Sports, JHMDB-21 and UCF101-24. Compared with the YOWO model, we improve frame-mAP (@IoU 0.5) by 17.09% and 7.15% on the UCF-Sports and JHMDB-21 datasets, and for video-mAP, we improve by 2.7%, 8.7% and 14.4% at IoU thresholds of 0.2, 0.5 and 0.75 on the JHMDB-21 dataset.

Check dams are widely used throughout the world to tackle soil and water loss. However, the frequency of extreme rainfall events has increased owing to global climate change and the main structure of check dam is gradually aging, which lead to an increase in the failure risk of check dams. Thus, it is necessary to carry out the study on failure risk diagnosis and assessment of check dams. In the study, machine learning algorithms (ML), including random forests (RF), support vector machine (SVM), and logistic regression (LR), were used to integrate the environmental and engineering factors and then assess the risk of check dam failure due to the “7.26” rainstorm on July 26, 2017, in the Chabagou watershed, located in the hilly-gully region of the Loess Plateau, China. To verify the generalizability of the model in this study, these models were used for the Wangmaogou catchment north of the Loess Plateau. The accuracy assessment by the receiver operating characteristic (ROC) curve indicated that the RF model with an area under the ROC curve (AUC) greater than 0.89 was the most precise model and had a higher generalization ability. In addition, the model dataset was relatively small and easy to obtain, which make the risk modeling of check dam failure in the study has the potential for application in other regions. In the RF model, each factor selected was confirmed to be important, and the importance values for engineering factors were generally higher than those for the environmental factors. The risk map of check dam failure in the RF model indicated that 56.34% of check dams in the study area had very high and high risks of dam failure under high-intensity rainfall in 2017. Based on the importance of factors and the risk map of check dam failure, the prevention and control measures for reducing the risk of check dam failure and promoting the construction of check dam are proposed. These proposals provide a scientific basis for the reinforcement of check dams and the future layout of check dams in the Chinese Loess Plateau.

Objective: To investigate the correlations of hip muscle composition with age and body mass index (BMI) in middle-aged and elderly patients with hip fracture. Methods: In total of 175 patients aged 51~95 years with low-energy hip fracture who underwent hip computed tomography scans were divided into ＜80 and ≥80 year-old groups. Quantitative computed tomography (QCT) was performed to obtain the cross-sectional muscle area (CSMA), total adipose area (TAA), intra-muscular adipose area (IMAA), subcutaneous adipose area (SAA) and muscle fat infiltration (MFI) of the hip. An independent t-test was used to compare the differences between gender and groups, and correlation analysis was used to determine the relationships between hip muscle fat area and age and BMI. Results: The hip TAA, SAA and MFI of men were significantly lower than those of women (t=−2.356, −2.550, −3.090), and CSMA was significantly higher than that of women. The CSMA in the ＜80 year-old age group was higher than that of the ≥80 year-old age group, and the MFI was lower than that found in the ≥80 years age old group. After adjusting for BMI, the age of men and women positively correlated with IMAA and MFI (r=0.445 and 0.612 for men, r=0.202 and 0.390 for women), and negatively correlated with CSMA (r=−0.673 for men, r=−0.428 for women). After adjusting for age, the BMI of men positively correlated with TAA, IMAA, SAA, and CSMA (r=0.430, 0.491, 0.389, 0.623), whereas the BMI of women positively correlated with TAA, IMAA, SAA, CSMA, and MFI (r=0.510, 0.389, 0.478, 0.295, 0.296). Conclusion: In middle-aged and elderly patients with hip fragility fractures, men had more muscle and less total fat than those in women, but similar intermuscular fat to that of women. Hip muscle mass decreased and intermuscular fat increased with age. Generally, higher BMI, correlated with more muscle and fat. However, MFI in men was not associated with BMI.

Abstract Venture capital (VC) significantly contributes to the development of regional economies and fosters innovation. Analyzing the factors that influence VC investments holds key importance. This study employs two methods to ascertain the relative significance of different factors at the city level: the Lindeman, Merenda, and Gold (LMG) approach in multiple linear regression (MLR) and variable importance in random forest (RF) machine learning. The findings reveal that several factors, including economy, finance, innovation, location, and policy, significantly influence VC investments. Both the MLR and RF models highlight the preeminence of economic and financial variables, followed closely by the city’s potential for innovation. Moreover, spatial heterogeneity exists in the importance of these variables. In the economically developed and densely populated eastern regions of China, the financial environment of cities emerges as the most crucial, whereas in the central and western regions, the economy and innovation, respectively, take precedence. This research contributes to a deeper understanding of the distribution of VC investments and offers valuable insights for the development of regional policies.

Affected by the insufficient information of single baseline observation data, the three-stage method assumes the Ground-to-Volume Ratio (GVR) to be zero so as to invert the vegetation height. However, this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion. Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR. Nevertheless, the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm. To this end, we propose a new step-by-step inversion method applied to the multi-baseline observations. Firstly, an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data, and the regularized estimates of model parameters are obtained by regularization method. Then, the reliable estimates of GVR are determined by the MSE (mean square error) analysis of each regularized parameter estimation. Secondly, the estimated GVR is used to extracts the pure volume coherence, and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation. The experimental results show that the new method can improve the vegetation height inversion result effectively. The inversion accuracy is improved by 26% with respect to the three-stage method and the conventional solution of multi-baseline. All of these have demonstrated the feasibility and effectiveness of the new method.

Motor dysfunction and muscle atrophy are typical symptoms of patients with spinal cord injury (SCI). Exercise training is a conventional physical therapy after SCI, but exercise intervention alone may have limited efficacy in reducing secondary injury and promoting nerve regeneration and functional remodeling. Our previous research found that intramedullary pressure after SCI is one of the key factors affecting functional prognosis. It has been reported that GsMTx-4, a specific blocker of the mechanosensitive ion channels Piezo1, can protect the integrity of the neuromuscular junction and promote nerve regeneration, and thus has the potential as a therapeutic agent for SCI. In this study, we observed the combined and separate therapeutic effect of GsMTx-4 and exercise on the structure of the soleus muscle and motor function in rats with SCI. At 42 days post-injury, compared with SCI rats, the Basso-Beattie-Bresnahan score (P = 0.0007) and Gait Symmetry (P = 0.0002) were significantly improved after combination therapy. On histology of rat soleus muscle, compared with SCI rats, the combined treatment significantly increased the wet weight ratio, muscle fiber cross-sectional area and acetylcholinesterase (all P

Abstract Carbapenem‐resistant Acinetobacter baumannii (CRAB) has become a major threat in the treatment of bacterial infection, and immunotherapy in a non‐antibiotic‐dependent manner is an effective way to overcome CRAB infection. However, the role of the innate immune response in CRAB infection is poorly understood. Here, it is reported that CRAB infection induced a cytosolic DNA‐sensing signaling pathway and significant IFN‐β production in mice post‐CRAB infection. The knockout of STING reduced bacterial burden, the production of inflammatory cytokines, and lung injury in mice post CRAB infection. The cytosolic DNA sensor cyclic GMP‐AMP synthase (cGAS) and the adaptor protein stimulator of interferon genes (STING) are required for CRAB‐induced IFN‐β expression in macrophages. Intriguingly, CRAB utilized outer membrane vesicles (OMVs) to transport outer membrane protein 38 (OMP38) into mitochondria, triggering mitochondrial DNA (mtDNA) release into the cytosol through the mitochondrial permeability transition pore (mPTP) and activating the cGAS‐STING signaling. Finally, epigallocatechin gallate (EGCG) is demonstrated to block the activation of the cGAS‐STING pathway and ameliorate CRAB‐induced excessive inflammatory response. These results demonstrated that the early innate immune response to CRAB infection is activated in a cGAS‐STING‐dependent manner, which could be a potential therapeutic target for CRAB infection.

Abstract Persistent H. pylori infection triggers the repair program of the mucosa, such as spasmolytic polypeptide‐expressing metaplasia (SPEM). However, the mechanism underlying the initiation of SPEM in gastric tissues by H. pylori remains unclear. Here, an increase in telomerase reverse transcriptase (TERT) protein expression is observed in chief cells upon infection with cagA‐positive H. pylori. Tert knockout significantly ameliorated H. pylori‐induced SPEM and single‐cell RNA sequencing demonstrated that the Wnt/β‐Catenin pathway is suppressed in gastric cells with Tert knockout. Mechanism study revealed that CagA elevated TERT abundance by disrupting the interaction between TERT and its novel E3 ligase, SYVN1. Interestingly, Nitazoxanide effectively relieved SPEM via inhibition of the Wnt/β‐Catenin signaling in vivo. This results clarified the mechanism underlying which CagA activated the TERT/Wnt/β‐Catenin pathway, thus promoting the dedifferentiation of chief cells and the occurrence of SPEM in gastric mucosa. This highlights a molecular basis for targeting CagA‐activated Wnt signaling in chief cells for the treatment of gastric precancerous lesions.

The popularity of roasted pork among Chinese consumers is largely attributed to its rich aroma profile. However, the suitability of different pork species for roasting remains uncertain. In this study, the effect of various pork species on the aroma profiles of roasted pork was systematically investigated using gas chromatography-olfactometry-mass spectrometry (GC-O-MS). The results indicated that the roasted pork from five different species exhibited rich roasty, meaty, and fatty notes. Fifty-two aroma compounds were identified, with seven key odorants, including pyrazines and aldehydes, being recognized as primary contributors to the aroma. The orthogonal partial least squares discriminant analysis (OPLS-DA) effectively distinguished the aroma profiles of the five pork species. Twelve aroma compounds displayed variable importance in projection (VIP) scores exceeding 1, with butyrolactone being identified as a potential biomarker. Among the species, Tibetan and Min pigs were found to be the most suitable for roasting, with Min pigs showing particular promise.

Abstract Background The RNA N 6-methyladenosine (m6A) modification has become an essential hotspot in epigenetic modulation. Serine–arginine protein kinase 1 (SRPK1) is associated with the pathogenesis of various cancers. However, the m6A modification of SRPK1 and its association with the mechanism of in lung adenocarcinoma (LUAD) remains unclear. Methods Western blotting and polymerase chain reaction (PCR) analyses were carried out to identify gene and protein expression. m6A epitranscriptomic microarray was utilized to the assess m6A profile. Loss and gain-of-function assays were carried out elucidate the impact of METTL3 and SRPK1 on LUAD glycolysis and tumorigenesis. RNA immunoprecipitation (RIP), m6A RNA immunoprecipitation (MeRIP), and RNA stability tests were employed to elucidate the SRPK1’s METTL3-mediated m6A modification mechanism in LUAD. Metabolic quantification and co-immunoprecipitation assays were applied to investigate the molecular mechanism by which SRPK1 mediates LUAD metabolism. Results The epitranscriptomic microarray assay revealed that SRPK1 could be hypermethylated and upregulated in LUAD. The main transmethylase METTL3 was upregulated and induced the aberrant high m6A levels of SRPK1. Mechanistically, SRPK1’s m6A sites were directly methylated by METTL3, which also stabilized SRPK1 in an IGF2BP2-dependent manner. Methylated SRPK1 subsequently promoted LUAD progression through enhancing glycolysis. Further metabolic quantification, co-immunoprecipitation and western blot assays revealed that SRPK1 interacts with hnRNPA1, an important modulator of PKM splicing, and thus facilitates glycolysis by upregulating PKM2 in LUAD. Nevertheless, METTL3 inhibitor STM2457 can reverse the above effects in vitro and in vivo by suppressing SRPK1 and glycolysis in LUAD. Conclusion It was revealed that in LUAD, aberrantly expressed METTL3 upregulated SRPK1 levels via an m6A-IGF2BP2-dependent mechanism. METTL3-induced SRPK1 fostered LUAD cell proliferation by enhancing glycolysis, and the small-molecule inhibitor STM2457 of METTL3 could be an alternative novel therapeutic strategy for individuals with LUAD.

Abstract Background As a common disabling disease, irreversible neuronal death due to spinal cord injury (SCI) is the root cause of functional impairment; however, the capacity for neuronal regeneration in the developing spinal cord tissue is limited. Therefore, there is an urgent need to investigate how defective neurons can be replenished and functionally integrated by neural regeneration; the reprogramming of intrinsic cells into functional neurons may represent an ideal solution. Methods A mouse model of transection SCI was prepared by forceps clamping, and an adeno-associated virus (AAV) carrying the transcription factors NeuroD1 and Neurogenin-2(Ngn2) was injected in situ into the spinal cord to specifically overexpress these transcription factors in astrocytes close to the injury site. 5-bromo-2´-deoxyuridine (BrdU) was subsequently injected intraperitoneally to continuously track cell regeneration, neuroblasts and immature neurons marker expression, neuronal regeneration, and glial scar regeneration. In addition, immunoprotein blotting was used to measure the levels of transforming growth factor-β (TGF-β) pathway-related protein expression. We also evaluated motor function, sensory function, and the integrity of the blood-spinal cord barrier(BSCB). Results The in situ overexpression of NeuroD1 and Ngn2 in the spinal cord was achieved by specific AAV vectors. This intervention led to a significant increase in cell regeneration and the proportion of cells with neuroblasts and immature neurons cell properties at the injury site(p 0.05), although the treated mice had more sensory sensitivity and greater voluntary motor ability in open field than the non-intervention mice. We observed significant repair of the BSCB at the center of the injury site (p

Abstract The generation of alkyl radical from C(sp3)−H substrates via hydrogen atom abstraction represents a desirable yet underexplored strategy in alkylation reaction since involving common concerns remain adequately unaddressed, such as the harsh reaction conditions, limited substrate scope, and the employment of noble metal- or photo-catalysts and stoichiometric oxidants. Here, we utilize the synergistic strategy of photoredox and hydrogen atom transfer (HAT) catalysis to accomplish a general and practical functionalization of unactived C(sp3)−H centers with broad reaction scope, high functional group compatibility, and operational simplicity. A combination of validation experiments and density functional theory reveals that the N-centered radicals, generated from free N − H bond in a stepwise electron/proton transfer event, are the key intermediates that enable an intramolecular 1,5-HAT or intermolecular HAT process for nucleophilic carbon-centered radicals formation to achieve heteroarylation, alkylation, amination, cyanation, azidation, trifluoromethylthiolation, halogenation and deuteration. The practical value of this protocol is further demonstrated by the gram-scale synthesis and the late-stage functionalization of natural products and drug derivatives.

Computer-aided diagnosis systems play a crucial role in the diagnosis and early detection of breast cancer. However, most current methods focus primarily on the dual-view analysis of a single breast, thereby neglecting the potentially valuable information between bilateral mammograms. In this paper, we propose a Four-View Correlation and Contrastive Joint Learning Network (FV-Net) for the classification of bilateral mammogram images. Specifically, FV-Net focuses on extracting and matching features across the four views of bilateral mammograms while maximizing both their similarities and dissimilarities. Through the Cross-Mammogram Dual-Pathway Attention Module, feature matching between bilateral mammogram views is achieved, capturing the consistency and complementary features across mammograms and effectively reducing feature misalignment. In the reconstituted feature maps derived from bilateral mammograms, the Bilateral-Mammogram Contrastive Joint Learning module performs associative contrastive learning on positive and negative sample pairs within each local region. This aims to maximize the correlation between similar local features and enhance the differentiation between dissimilar features across the bilateral mammogram representations. Our experimental results on a test set comprising 20% of the combined Mini-DDSM and Vindr-mamo datasets, as well as on the INbreast dataset, show that our model exhibits superior performance in breast cancer classification compared to competing methods.

Angioimmunoblastic T-cell lymphoma (AITL) is an aggressive subtype of peripheral T-cell lymphoma (PTCL) characterized by its T-follicular helper (TFH) phenotype. Relapsed and refractory disease is common in AITL and often associated with a poor prognosis. The presence of epigenetic abnormalities, immune dysregulation, hyperinflammation and active angiogenesis in AITL offers potential targets for histone deacetylase (HDAC) inhibitors and immunomodulatory drugs (IMiDs). Herein, we present a case of AITL with multiple relapses over a decade. Following intensive chemotherapy and autologous stem cell transplantation (ASCT), the patient relapsed with extensive nodal and extranodal involvement, particularly pulmonary lesions, and subsequently pursued chemo-free treatments. Initially, the patient exhibited a remarkable response to single-agent chidamide, the first oral HDAC inhibitor. Soon after developing resistance to chidamide, continuous treatment with lenalidomide led to an impressive sustained complete remission lasting 64 months, followed by a diminished response for an additional 11 months. Genetic profiling of the patient revealed mutations in KMT2D and ARID1A, along with chromosomal aberrations such as del(5q). Notably, genes commonly mutated in AITL, including RHOA, TET2, DNMT3A, and IDH2, were absent in this case. A review of the literature highlights the heterogeneous genomic landscape of AITL and the diversity of treatment options available, underscoring the importance of tailored approaches to overcome resistance and improve outcomes in this distinct lymphoma subtype.

Background: All-trans retinoic acid (ATRA) and arsenic trioxide (ATO) have revolutionized the treatment of acute promyelocytic leukemia (APL). However, the management of high-risk APL has not been conclusively established. The optimal dosage of anthracycline in the induction has long been debated when ATO is added. Objectives: To explore the management of high-risk APL regarding the optimal dosage of anthracycline in the induction and the predicators of prognosis. Design: This was a retrospective study in the real-world setting. Methods: High-risk APL patients defined as white blood cell (WBC) greater than 10 × 10 9 /L who received ATO-based induction regimens were included. Data on clinical characteristics, treatment regimens, and prognosis including early death (ED) and overall survival (OS) were collected from medical records. Risk factors of ED and OS were analyzed. Results: This research included a total of 130 participants. Fifty (38.5%) patients received ATO+ATRA dual induction plus standard-dose anthracycline (ATO + ATRA + stDNR). Fifty-nine (45.4%) patients received ATO + ATRA with consecutive low-dose anthracycline (ATO + ATRA + ldDNR). Twenty-one (16.2%) patients were treated with ATO and various chemotherapies (ATO + others). Compared with the other two groups, the ATO + ATRA + stDNR group had the lowest ED rate of 4.0% (10.2% and 52.4%, respectively; p

Resistance to sorafenib, an effective first-line treatment for advanced hepatocellular carcinoma (HCC), greatly compromised the prognosis of patients. The extracellular matrix is one of the most abundant components of the tumor microenvironment. Beyond acting as a physical barrier, it remains unclear whether cell interactions and signal transduction mediated by the extracellular matrix contribute to sorafenib resistance. With the analysis of primary HCC organoid RNA-seq data combined with in vivo and in vitro experiments validation, we discovered that fibronectin extra domain A (FN-EDA) derived from cancer-associated fibroblasts played a critical role in sorafenib resistance. Mechanistically, FN-EDA stimulates the up-regulation of the key one-carbon metabolism enzyme SHMT1 in HCC cells via the TLR4/NF-κB signaling pathway, thereby countering the oxidative stress induced by sorafenib. Moreover, we reinforced the clinical significance of our discoveries by conducting in vivo assays with an immunodeficiency subcutaneous xenograft tumor model, which was established using primary cancer-associated fibroblasts derived from clinical HCC tissues, and through the analysis of HCC samples obtained from The Cancer Genome Atlas (TCGA) database. Our findings suggest that targeting the FN-EDA/SHMT1 pathway could be a potential strategy to improve sorafenib responsiveness in HCC patients.

The recent advances in mass spectrometry (MS) technologies have enabled comprehensive lipid profiling in biological samples. However, the robustness and efficiency of MS-based lipidomics is compromised by the complexity of biological samples. High-field asymmetric waveform ion mobility spectrometry (FAIMS) is a technology that can continuously transmit one type of ion, independent of the mass-to-charge ratio. Here we present the development and application of LC-FAIMS-MS/MS-based platform for untargeted lipidomics. We used 3 optimally balanced compensation voltages, i.e., 29 V, 34 V and 39 V, to analyze all subclasses of glycerophospholipids. The reproducibility of the method was evaluated using reference standards. The reproducibility of retention times ranged from 0.9% to 1.5% RSD; whereas RSD values of 5%–10% were observed for peak areas. More importantly, the coupling of a FAIMS device can significantly improve the robustness and efficiency. We exploited this NPLC-FAIMS-HRMS to analyze the serum lipid profiles in mice infected intranasally with Acinetobacter baumannii. The temporal profiles of serum lipids after A. baumannii inoculation were obtained for 4 h, 8 h, and 24 h. We found that nearly all ether PC and ether PE lipids were significantly decreased 8 h after inoculation. The resultant volcano plot illustrated the distribution of 28 increased and 28 decreased lipid species in mouse sera 24 h after inoculation. We also found that a single ether PE composition can comprise multiple isomeric structures, and the relative abundance of each isomer could be quantified using the newly developed NPLC-FAIMS-PRM method. We have demonstrated that the proposed LC-FAIMS-MS is a valuable platform for lipidomics.

IntroductionExtracellular vesicles (EVs) known for their pivotal role in intercellular communication through RNA delivery, hold paramount implications for understanding muscle phenotypic variations in diverse pig breeds.MethodsIn this study, we compared the mRNA expression profiles of longissimus dorsi muscles and muscle-derived extracellular vesicles (M-EVs), and also examined the diversity of enriched genes in M-EVs between weaned wild boars and commercial Large White pigs with respect to their numbers and biological functions.ResultsThe results of the study showed that the variation in the expression profiles of mRNAs between muscles and M-EVs was much greater than the variability between the respective breeds. Meanwhile, the enrichment trend of low-expressed genes (ranked

Objective: Bone tumors, known for their infrequent occurrence and diverse imaging characteristics, require precise differentiation into benign and malignant categories. Existing diagnostic approaches heavily depend on the laborious and variable manual delineation of tumor regions. Deep learning methods, particularly convolutional neural networks (CNNs), have emerged as a promising solution to tackle these issues. This paper introduces an enhanced deep-learning model based on AlexNet to classify femoral bone tumors accurately. Methods: This study involved 500 femoral tumor patients from July 2020 to January 2023, with 500 imaging cases (335 benign and 165 malignant). A CNN was employed for automated classification. The model framework encompassed training and testing stages, with 8 layers (5 Conv and 3 FC) and ReLU activation. Essential architectural modifications included Batch Normalization (BN) after the first and second convolutional filters. Comparative experiments with various existing methods were conducted to assess algorithm performance in tumor staging. Evaluation metrics encompassed accuracy, precision, sensitivity, specificity, F-measure, ROC curves, and AUC values. Results: The analysis of precision, sensitivity, specificity, and F1 score from the results demonstrates that the method introduced in this paper offers several advantages, including a low feature dimension and robust generalization (with an accuracy of 98.34 %, sensitivity of 97.26 %, specificity of 95.74 %, and an F1 score of 96.37). These findings underscore its exceptional overall detection capabilities. Notably, when comparing various algorithms, they generally exhibit similar classification performance. However, the algorithm presented in this paper stands out with a higher AUC value (AUC=0.848), signifying enhanced sensitivity and more robust specificity. Conclusion: This study presents an optimized AlexNet model for classifying femoral bone tumor images based on convolutional neural networks. This algorithm demonstrates higher accuracy, precision, sensitivity, specificity, and F1-score than other methods. Furthermore, the AUC value further confirms the outstanding performance of this algorithm in terms of sensitivity and specificity. This research makes a significant contribution to the field of medical image classification, offering an efficient automated classification solution, and holds the potential to advance the application of artificial intelligence in bone tumor classification.

Objective: This study aims to explore the application of radiographic imaging and image recognition algorithms, particularly AlexNet and ResNet, in classifying malignancies for spinal bone tumors. Methods: We selected a cohort of 580 patients diagnosed with primary spinal osseous tumors who underwent treatment at our hospital between January 2016 and December 2023, whereby 1532 images (679 images of benign tumors, 853 images of malignant tumors) were extracted from this imaging dataset. Training and validation follow a ratio of 2:1. All patients underwent X-ray examinations as part of their diagnostic workup. This study employed convolutional neural networks (CNNs) to categorize spinal bone tumor images according to their malignancy. AlexNet and ResNet models were employed for this classification task. These models were fine-tuned through training, which involved the utilization of a database of bone tumor images representing different categories. Results: Through rigorous experimentation, the performance of AlexNet and ResNet in classifying spinal bone tumor malignancy was extensively evaluated. The models were subjected to an extensive dataset of bone tumor images, and the following results were observed. AlexNet: This model exhibited commendable efficiency during training, with each epoch taking an average of 3 s. Its classification accuracy was found to be approximately 95.6 %. ResNet: The ResNet model showed remarkable accuracy in image classification. After an extended training period, it achieved a striking 96.2 % accuracy rate, signifying its proficiency in distinguishing the malignancy of spinal bone tumors. However, these results illustrate the clear advantage of AlexNet in terms of proficiency despite a lower classification accuracy. The robust performance of the ResNet model is auspicious when accuracy is more favored in the context of diagnosing spinal bone tumor malignancy, albeit at the cost of longer training times, with each epoch taking an average of 32 s. Conclusion: Integrating deep learning and CNN-based image recognition technology offers a promising solution for qualitatively classifying bone tumors. This research underscores the potential of these models in enhancing the diagnosis and treatment processes for patients, benefiting both patients and medical professionals alike. The study highlights the significance of selecting appropriate models, such as ResNet, to improve accuracy in image recognition tasks.

Triple-negative breast cancer (TNBC) is characterized by high invasion, prone metastasis, frequent recurrence and poor prognosis. Unfortunately, the curative effects of current clinical therapies, including surgery, radiotherapy, chemotherapy and immunotherapy, are still limited in patients with TNBC. In this study, we showed that the heterogeneous expression at the protein level and subcellular location of mesothelin (MSLN), a potential target for chimeric antigen receptor-T (CAR-T) cell therapy in TNBC, which is caused by acidification of the tumor microenvironment, may be the main obstacle to therapeutic efficacy. Alkalization culture or sodium bicarbonate administration significantly promoted the membrane expression of MSLN and enhanced the killing efficiency of MSLN-CAR-T cells both in vitro and in vivo, and the same results were also obtained in other cancers with high MSLN expression, such as pancreatic and ovarian cancers. Moreover, mechanistic exploration revealed that the attenuation of autophagy-lysosome function caused by microenvironmental alkalization inhibited the degradation of MSLN. Hence, alkalization of the microenvironment improves the consistency and high expression of the target antigen MSLN and constitutes a routine method for treating diverse solid cancers via MSLN-CAR-T cells.

Coupled grey and green infrastructure (CGGI) is increasingly recognized as a viable approach to sustainable urban stormwater management. This study evaluates CGGI and grey infrastructure (GREI)-only schemes with various degree of centralization of the layout (DCL) in addressing urban flood and drainage issues in a historical and cultural district (HCD) which typically consists of high impervious surfaces, dense urban structures, and fragile heritage buildings. Yongqing Fang Community in Guangzhou, China, was selected as a case study in which the performance of the Grey-only and CGGI schemes are evaluated and compared. The results obtained indicated that the CGGI scheme was more advantageous in terms of cost-effectiveness and scalability, yielding potential savings of $30,500 to $163,400. Moreover, the fully decentralized layout of the two schemes could result in cost savings of 29.0% and 29.6%, respectively, over the fully centralized layout. However, CGGI shows marginally lower adaptability in response to extreme rainfall events compared to that of GREI-only solutions. Technical resilience (Tech-R) of GREI-only scored higher by 0.1% to 0.8%, 0.5% to 3.5%, and 0.7% to 4.8% for 10-year, 50-year, and 100-year rainfall scenarios, respectively. Nonetheless, CGGI schemes demonstrated superior adaptability in structural failure scenarios, and reduced surface overflow by 22.6%, 19.0%, and 18.4% compared to GREI for the same scenarios. In both the CGGI and GREI-only schemes, decentralized layouts are likely to outperform centralized layouts in both extreme rainfall events and in failure scenarios. These findings underscore the importance of decentralized layout of the drainage infrastructure which could enhance the hydrological performance of integrated drainage infrastructures, offering insights for due considerations in designing multi-objective infrastructures for urban flood mitigation in HCDs.

Summary: The stop signal is produced in response to negative experiences at the food source and inhibits honey bee (Apis mellifera) waggle dancing. Here, we present a protocol for measuring the effects of an inhibitory signal associated with danger on honey bee dopamine levels. We describe steps for observing honey bee colonies, training them with artificial nectar, and simulating hornet attacks. We then detail procedures for recording waggle dancing and stop signals and measuring brain dopamine levels during different treatments.For complete details on the use and execution of this protocol, please refer to Dong et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The indiscriminate evolution of urban configurations aggravates flood vulnerabilities, threatening sustainable urban expansion. Present methodologies fall short in supplying urban planners with flood mitigative strategies centered on urban configuration facets. Leveraging the power of the XGBoost algorithm, this study posits an advanced optimization schema, adroitly balancing the dual objectives of mitigating urban flooding and enhancing economic growth, with minimal disruption to established urban layouts. Shenzhen serves as the investigative ground, where the model displays exceptional accuracy, resilience, and interpretability in predicting Pluvial Flooding Susceptibility (PFS) and Economic Contribution (EC). Model interpretation divulges the profound influence of three-dimensional urban configuration elements, primarily the Building Congestion Degree, on PFS and EC. Pareto solution exploration for multi-objective optimization unveils the ideal urban configuration interval. To minimize PFS while maximizing EC, the research suggests pertinent measures: augmenting vegetation density, regulating the impervious coverage ratio within 50–70%, limiting two- and three-dimensional building density thresholds, and moderately escalating urban drainage network density. Additionally, it encourages a comprehensive appreciation of function-oriented land usage and intrinsic site topographical characteristics to reconcile varied urban development goals during planning. By fusing data-derived insights with multi-objective optimization, this research anticipates influencing urban planning models, thus enhancing decision-making related to urban configuration and fostering flood-resilient, sustainable, and economically prosperous urban habitats.

Studies on the pathophysiology of shoulder contracture and development of interventions have greatly benefited from the use of animal models. This narrative review comprehensively analyzes research on established rat model of shoulder contracture and new treatment approaches. This review evaluated existing literature on the available techniques for inducing contracture models, assessed these models, conducted pathological analyses, and explored their application in developing new treatment interventions. Our review highlights the usefulness of different rat shoulder contracture models, including external immobilization, internal immobilization, and intra-articular injection models, each with varying levels of success. Pathological analyses have demonstrated similarities to the human condition. The effective models have been instrumental in developing new treatment interventions, including recombinant human relaxin-2, platelet-rich plasma, collagenase clostridium histolyticum, and peroxisome proliferator-activated receptor-γ agonists. Therefore, rat shoulder contracture models serve as valuable tools for researchers to establish an effective animal model foundation for investigating the etiology and potential treatment.

Chilled chicken is inevitably contaminated by microorganisms during slaughtering and processing, resulting in spoilage. Cutting parts of chilled chicken, especially wings, feet, and other skin-on products, are abundant in collagen, which may be the primary target for degradation by spoilage microorganisms. In this work, a total of 17 isolates of spoilage bacteria that could secrete both collagenase and lipase were determined by raw-chicken juice agar (RJA) method, and the results showed that 7 strains of Serratia, Aeromonas, and Pseudomonas could significantly decompose the collagen ingredients. The gelatin zymography showed that Serratia liquefaciens (F5) and Pseudomonas saponiphila (G7) had apparent degradation bands around 50 kDa, and Aeromonas veronii (G8) and Aeromonas salmonicida (H8) had a band around 65 and 95 kDa, respectively. The lipase and collagenase activities were detected isolate-by-isolate, with F5 showing the highest collagenase activity. For spoilage ability on meat in situ, F5 performed strongest in spoilage ability, indicated by the total viable counts, total volatile basic nitrogen content, sensory scores, lipase, and collagenase activity. This study provides a theoretical basis for spoilage heterogeneity of strains with high-producing collagenase in meat.

Abstract Background Remnant cholesterol (RC) and nonhigh-density lipoprotein cholesterol (nonHDL-C) are key risk factors for atherosclerotic cardiovascular disease (ASCVD), with apolipoprotein B (apoB) and lipoprotein(a) [Lp(a)] also contributing to its residual risk. However, real-world population-based evidence regarding the impact of current clinical LDL-C-centric lipid-lowering therapy (LLT) on achieving RC and nonHDL-C goals, as well as on modifying residual CVD risk factors is limited. Methods This prospective observational study enrolled 897 CVD patients from September, 2020 to July, 2021. All participants had previously received low-/moderate-intensity LLT and were discharged with either low-/moderate-intensity LLT or high-intensity LLT. After a median follow-up of 3 months, changes in RC, nonHDL-C, and other biomarkers were assessed. Multivariate logistic regression was performed to analyze the impact of the LLT on goal attainment. Results Among all patients, 83.50% transitioned to high-intensity LLT from low or moderate. After follow-up, the high-intensity group saw significantly greater reductions in RC (-20.51% vs. -3.90%, P = 0.025), nonHDL-C (-25.12% vs. 0.00%, P

Abstract Background Postoperative cognitive dysfunction (POCD) is a common neurological complication of anesthesia and surgery in aging individuals. Neuroinflammation has been identified as a hallmark of POCD. However, safe and effective treatments of POCD are still lacking. Itaconate is an immunoregulatory metabolite derived from the tricarboxylic acid cycle that exerts anti-inflammatory effects by activating the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. In this study, we investigated the effects and underlying mechanism of 4-octyl itaconate (OI), a cell-permeable itaconate derivative, on POCD in aged mice. Methods A POCD animal model was established by performing aseptic laparotomy in 18-month-old male C57BL/6 mice under isoflurane anesthesia while maintaining spontaneous ventilation. OI was intraperitoneally injected into the mice after surgery. Primary microglia and neurons were isolated and treated to lipopolysaccharide (LPS), isoflurane, and OI. Cognitive function, neuroinflammatory responses, as well as levels of gut microbiota and their metabolites were evaluated. To determine the mechanisms underlying the therapeutic effects of OI in POCD, ML385, an antagonist of Nrf2, was administered intraperitoneally. Cognitive function, neuroinflammatory responses, endogenous neurogenesis, neuronal apoptosis, and Nrf2/extracellular signal-related kinases (ERK) signaling pathway were evaluated. Results Our findings revealed that OI treatment significantly alleviated anesthesia/surgery-induced cognitive impairment, concomitant with reduced levels of the neuroinflammatory cytokines IL-1β and IL-6, as well as suppressed activation of microglia and astrocytes in the hippocampus. Similarly, OI treatment inhibited the expression of IL-1β and IL-6 in LPS and isoflurane-induced primary microglia in vitro. Intraperitoneal administration of OI led to alterations in the gut microbiota and promoted the production of microbiota-derived metabolites associated with neurogenesis. We further confirmed that OI promoted endogenous neurogenesis and inhibited neuronal apoptosis in the hippocampal dentate gyrus of aged mice. Mechanistically, we observed a decrease in Nrf2 expression in hippocampal neurons both in vitro and in vivo, which was reversed by OI treatment. We found that Nrf2 was required for OI treatment to inhibit neuroinflammation in POCD. The enhanced POCD recovery and promotion of neurogenesis triggered by OI exposure were, at least partially, mediated by the activation of the Nrf2/ERK signaling pathway. Conclusions Our findings demonstrate that OI can attenuate anesthesia/surgery-induced cognitive impairment by stabilizing the gut microbiota and activating Nrf2 signaling to restrict neuroinflammation and promote neurogenesis. Boosting endogenous itaconate or supplementation with exogenous itaconate derivatives may represent novel strategies for the treatment of POCD.

In this study, the lactic acid bacteria from the feces of Uygur mother-infant pairs in Hotan, Xinjiang, was isolated and identified. The bacteriostatic performance of the isolates for diarrhea pathogenic bacteria was assessed, and the probiotic properties were investigated in vitro. According to the groEL gene sequencing, 113 isolates of lactic acid bacteria representing 15 species were isolated from 19 mother-infant pairs, with Ligilactobacillus salivarius and Companilactobacillus farciminis being the dominant species, and 29 strains had inhibition effect on all the 6 pathogenic bacteria were screened from all strains. Among the strains with antibacterial activity, Companilactobacillus farciminis HTb36X-2 had the highest survival rate of 69.50% after being treated with simulated gastrointestinal juices for 4 h. Limosilactobacillus reuteri HTb34X5-6 and HTb34X5-1 had the highest auto-aggregation and hydrophobicity, with the hydrophobicity of 31.67% and 29.00%, and auto-aggregation of 81.32% and 70.91%, respectively. Combined with antibiotic resistance and carbohydrate metabolism of all 29 strains, a total of 7 strains of Companilactobacillus farciminis HTb36X-2, Lacticaseibacillus paracasei HTb6X-6, Lacticaseibacillus casei 17X-4, 17X-5, Limosilactobacillus reuteri HTb34X-5-1, HTb34X-5-6, Limosilactobacillus fermentum 10D12-2 were screened as potential probiotics, which might take a foundation for the development of probiotic and products with anti-diarrhea function.

Abstract The construction of super large section (SLS) shallow buried tunnels involves challenges related to their large span, high flat rate, and complex construction process. Selecting an appropriate excavation method is crucial for ensuring stability, controlling costs, and managing the construction timeline. This study focuses on the selection of excavation methods and the mechanical responses of SLS tunnels in different types of surrounding rock. The research is based on the Yangjiashan tunnel project in Zhejiang Province, China, which is a four-line highway tunnel with a span of 21.3 m. Three sequential excavation methods were proposed and simulated using the three-dimensional finite difference method: the “upper first and lower later” side drift (SD) method, the central diaphragm method, and the top heading and bench (HB) method. The mechanical response characteristics of tunnel construction under these methods were investigated, including rock deformation, rock pressure, and the internal forces acting on the primary support. By comparing the performance of the three construction methods in rock masses of Grades III to V, the study aimed to determine the optimal construction method for SLS tunnels considering factors such as safety, cost, and schedule. Field tests were conducted to evaluate the effectiveness of the optimized construction scheme. The results of the field monitoring indicated that the “upper first and lower later” SD method in Grade V rock mass and the HB method in Grade III to IV rock mass are feasible and cost-effective under certain conditions. The research findings provide valuable insights for the design and construction of SLS tunnels in complex conditions, serving as a reference for engineers and project managers.