Efficient Dynamic Phase Splitting Driven by Centrifugal Force for CO2 Capture from Flue Gas using Biphasic Solvents.

Carbon dioxide (CO2) chemisorption using biphasic solvents has been regarded as a promising approach, but challenges remain in achieving efficient dynamic phase-splitting during practical implementation. To address this, the centrifugal force was innovatively adopted to enhance the coalescence and separation of immiscible fine droplets within the biphasic solvent. The comprehensive evaluation demonstrates that centrifugal phase-splitting shows outstanding separation efficiency (>95%) and excellent applicability for various solvents. Correlation analysis reveals a strong relationship between the rich phase's viscosity, lean phase's residual CO2, and the phase separation efficiency. The time-profile behavior of immiscible droplets, observed through microscope images of phase-splitting, enables the estimation of the growth and coalescence rates of the discrete phase. Industrial-scale process simulation for technical and economic analysis confirms that the total capture cost ($ 42.5/t CO2) can be reduced by ∼22% with the use of biphasic solvents and a centrifugal separator compared to conventional methods. This study introduces a fresh perspective on polarity-induced cluster generation and coagulation-induced separation, offering an effective solution to address the challenges associated with dynamic phase-splitting in biphasic solvents during practical applications.

Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging.

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI. We reveal two distinct types of features in multi-modality imaging, namely intra- and inter-modality features, and present a multi-sensor learning (MSL) framework to utilize the crossover inter-modality features for augmented multi-modality imaging. The MSL imaging approach breaks down the boundaries of traditional imaging modalities and allows for optimal hybridization of CT and MRI, which maximizes the use of sensory data. We showcase the effectiveness of our DMI strategy through synergetic CT-MRI brain imaging. The principle of DMI is quite general and holds enormous potential for various DMI applications across disciplines.

Genetic Structure and Genome-Wide Association Analysis of Growth and Reproductive Traits in Fengjing Pigs.

The Fengjing pig is one of the local pig breed resources in China and has many excellent germplasm characteristics. However, research on its genome is lacking. To explore the degree of genetic diversity of the Fengjing pig and to deeply explore its excellent traits, this study took Fengjing pigs as the research object and used the Beadchip Array Infinium iSelect-96|XT KPS_PorcineBreedingChipV2 for genotyping. We analyzed the genetic diversity, relatedness, inbreeding coefficient, and population structure within the Fengjing pig population. Our findings revealed that the proportion of polymorphic markers (PN) was 0.469, and the effective population size was 6.8. The observed and expected heterozygosity were 0.301 and 0.287, respectively. The G-matrix results indicated moderate relatedness within the population, with certain individuals exhibiting closer genetic relationships. The NJ evolutionary tree classified Fengjing boars into five family lines. The average inbreeding coefficient based on ROH was 0.318, indicating a high level of inbreeding. GWAS identified twenty SNPs significantly associated with growth traits (WW, 2W, and 4W) and reproductive traits (TNB and AWB). Notably, WNT8B, RAD21, and HAO1 emerged as candidate genes influencing 2W, 4W, and TNB, respectively. Genes such as WNT8B were verified by querying the PigBiobank database. In conclusion, this study provides a foundational reference for the conservation and utilization of Fengjing pig germplasm resources and offers insights for future molecular breeding efforts in Fengjing pigs.

Localization and recognition of human action in 3D using transformers.

Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization. Important baselines and evaluating metrics, as well as human evaluations, are carefully established on this benchmark. We also propose a strong baseline model, i.e., Localizing Actions with Transformers (LocATe), that jointly localizes and recognizes actions in a 3D sequence. The proposed LocATe shows superior performance on BABEL-TAL as well as on the large-scale PKU-MMD dataset, achieving state-of-the-art performance by using only 10% of the labeled training data. Our research could advance the development of more accurate and efficient systems for human behavior analysis, with potential applications in areas such as human-computer interaction and healthcare.

Virtual multiplexed immunofluorescence staining from non-antibody-stained fluorescence imaging for gastric cancer prognosis.

BACKGROUND: Multiplexed immunofluorescence (mIF) staining, such as CODEX and MIBI, holds significant clinical value for various fields, such as disease diagnosis, biological research, and drug development. However, these techniques are often hindered by high time and cost requirements. METHODS: Here we present a Multimodal-Attention-based virtual mIF Staining (MAS) system that utilises a deep learning model to extract potential antibody-related features from dual-modal non-antibody-stained fluorescence imaging, specifically autofluorescence (AF) and DAPI imaging. The MAS system simultaneously generates predictions of mIF with multiple survival-associated biomarkers in gastric cancer using self- and multi-attention learning mechanisms. FINDINGS: Experimental results with 180 pathological slides from 94 patients with gastric cancer demonstrate the efficiency and consistent performance of the MAS system in both cancer and noncancer gastric tissues. Furthermore, we showcase the prognostic accuracy of the virtual mIF images of seven gastric cancer related biomarkers, including CD3, CD20, FOXP3, PD1, CD8, CD163, and PD-L1, which is comparable to those obtained from the standard mIF staining. INTERPRETATION: The MAS system rapidly generates reliable multiplexed staining, greatly reducing the cost of mIF and improving clinical workflow. FUNDING: Stanford 2022 HAI Seed Grant; National Institutes of Health 1R01CA256890.

A thalamic nucleus reuniens-lateral septum-lateral hypothalamus circuit for comorbid anxiety-like behaviors in chronic itch.

Chronic itch often clinically coexists with anxiety symptoms, creating a vicious cycle of itch-anxiety comorbidities that are difficult to treat. However, the neuronal circuit mechanisms underlying the comorbidity of anxiety in chronic itch remain elusive. Here, we report anxiety-like behaviors in mouse models of chronic itch and identify γ-aminobutyric acid-releasing (GABAergic) neurons in the lateral septum (LS) as the key player in chronic itch-induced anxiety. In addition, chronic itch is accompanied with enhanced activity and synaptic plasticity of excitatory projections from the thalamic nucleus reuniens (Re) onto LS GABAergic neurons. Selective chemogenetic inhibition of the Re → LS circuit notably alleviated chronic itch-induced anxiety, with no impact on anxiety induced by restraint stress. Last, GABAergic neurons in lateral hypothalamus (LH) receive monosynaptic inhibition from LS GABAergic neurons to mediate chronic itch-induced anxiety. These findings underscore the potential significance of the Re → LS → LH pathway in regulating anxiety-like comorbid symptoms associated with chronic itch.

Auto-delineation of treatment target volume for radiation therapy using large language model-aided multimodal learning.

PURPOSE: Artificial intelligence (AI)-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiotherapy target volume. Our goal is to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches. METHODS AND MATERIALS: A vision-language model, termed Medformer, has been developed, employing the hierarchical vision transformer as its backbone, and incorporating large language models to extract text-rich features. The contextually embedded linguistic features are seamlessly integrated into visual features for language-aware visual encoding through the visual language attention module. Metrics, including Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to quantitatively evaluate the performance of our model. The evaluation was conducted on an in-house prostate cancer dataset and a public oropharyngeal carcinoma (OPC) dataset, totaling 668 subjects. RESULTS: Our Medformer achieved a DSC of 0.81 ± 0.10 versus 0.72 ± 0.10, IOU of 0.73 ± 0.12 versus 0.65 ± 0.09, and HD95 of 9.86 ± 9.77 mm versus 19.13 ± 12.96 mm for delineation of gross tumor volume (GTV) on the prostate cancer dataset. Similarly, on the OPC dataset, it achieved a DSC of 0.77 ± 0.11 versus 0.72 ± 0.09, IOU of 0.70 ± 0.09 versus 0.65 ± 0.07, and HD95 of 7.52 ± 4.8 mm versus 13.63 ± 7.13 mm, representing significant improvements (p < 0.05). For delineating the clinical target volume (CTV), Medformer achieved a DSC of 0.91 ± 0.04, IOU of 0.85 ± 0.05, and HD95 of 2.98 ± 1.60 mm, comparable to other state-of-the-art algorithms. CONCLUSIONS: Auto-delineation of the treatment target based on multimodal learning outperforms conventional approaches that rely purely on visual features. Our method could be adopted into routine practice to rapidly contour CTV/GTV.

Crisaborole combined with vitamin D demonstrates superior therapeutic efficacy over either monotherapy in mice with allergic contact dermatitis.

This study evaluated the therapeutic efficacy and underlying mechanisms of crisaborole combined with vitamin D in the treatment of allergic contact dermatitis. While crisaborole, a phosphodiesterase 4 inhibitor, and vitamin D analogs are commonly used in the treatment of atopic dermatitis, their combined therapeutic potential in allergic contact dermatitis (ACD) remains unexplored. Given their anti-inflammatory properties, we hypothesized that the combination of crisaborole and vitamin D could offer superior efficacy in mitigating the symptoms and underlying mechanisms of allergic contact dermatitis. In vitro, HaCaT cells stimulated with tumor necrosis factor-α and interferon-γ were treated with a combination of crisaborole and vitamin D, followed by cytokine expression analysis. In vivo, male C57BL/6 mice were divided into five groups and treated accordingly: blank control, dinitrochlorobenzene-induced model, crisaborole alone, vitamin D alone, and a combination of crisaborole and vitamin D. On day 14, dorsal skin and ear thickness were measured, followed by comprehensive pathological evaluations. In vivo and in vitro experiments showed that the expression levels of inflammatory factors were significantly lower in the DNCB + VD + Cri group than in the DNCB group. Histological analyses revealed that, compared with the DNCB group, the combined treatment group significantly reduced epidermal hyperkeratosis, improved epidermal transdermal water loss, decreased dermatitis scores, and diminished mast cell infiltration. Moreover, it lowered the expression levels of IL-6, IL-4, TNF-α, iNOS, IL-17, CC chemokine ligand 2 (CCL2), and CC chemokine receptor 2 (CCR2). CCL2 recognizes CCR2 and stimulates inflammatory cells, enhancing the inflammatory response. Increased CCL2 expression correlates with heightened inflammation and dendritic cell infiltration in ACD, while downregulation of CCL2 attenuates inflammation. Thus, the combined use of crisaborole and vitamin D demonstrates superior therapeutic efficacy over monotherapy in a mouse model of ACD. The combination of vitamin D and crisaborole significantly reduces inflammation and epidermal hyperkeratosis in a mouse model of allergic contact dermatitis, demonstrating superior therapeutic efficacy compared to either treatment alone. This suggests that the combined therapy could be a promising approach for the prevention and treatment of allergic contact dermatitis.

TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers.

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into medical image segmentation. However, a comprehensive understanding of Transformers' self-attention in U-Net components is lacking. TransUNet, first introduced in 2021, is widely recognized as one of the first models to integrate Transformer into medical image analysis. In this study, we present the versatile framework of TransUNet that encapsulates Transformers' self-attention into two key modules: (1) a Transformer encoder tokenizing image patches from a convolution neural network (CNN) feature map, facilitating global context extraction, and (2) a Transformer decoder refining candidate regions through cross-attention between proposals and U-Net features. These modules can be flexibly inserted into the U-Net backbone, resulting in three configurations: Encoder-only, Decoder-only, and Encoder+Decoder. TransUNet provides a library encompassing both 2D and 3D implementations, enabling users to easily tailor the chosen architecture. Our findings highlight the encoder's efficacy in modeling interactions among multiple abdominal organs and the decoder's strength in handling small targets like tumors. It excels in diverse medical applications, such as multi-organ segmentation, pancreatic tumor segmentation, and hepatic vessel segmentation. Notably, our TransUNet achieves a significant average Dice improvement of 1.06% and 4.30% for multi-organ segmentation and pancreatic tumor segmentation, respectively, when compared to the highly competitive nn-UNet, and surpasses the top-1 solution in the BrasTS2021 challenge. 2D/3D Code and models are available at https://github.com/Beckschen/TransUNet and https://github.com/Beckschen/TransUNet-3D, respectively.

Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy.

Radiation therapy (RT) is one of the most effective treatments for cancer, and its success relies on the accurate delineation of targets. However, target delineation is a comprehensive medical decision that currently relies purely on manual processes by human experts. Manual delineation is time-consuming, laborious, and subject to interobserver variations. Although the advancements in artificial intelligence (AI) techniques have significantly enhanced the auto-contouring of normal tissues, accurate delineation of RT target volumes remains a challenge. In this study, we propose a visual language model-based RT target volume auto-delineation network termed Radformer. The Radformer utilizes a hierarchical vision transformer as the backbone and incorporates large language models to extract text-rich features from clinical data. We introduce a visual language attention module (VLAM) for integrating visual and linguistic features for language-aware visual encoding (LAVE). The Radformer has been evaluated on a dataset comprising 2985 patients with head-and-neck cancer who underwent RT. Metrics, including the Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to evaluate the performance of the model quantitatively. Our results demonstrate that the Radformer has superior segmentation performance compared to other state-of-the-art models, validating its potential for adoption in RT practice.

Automated radiotherapy treatment planning guided by GPT-4Vision.

Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in large foundation models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, a fully automated treatment planning framework that harnesses prior radiation oncology knowledge encoded in multi-modal large language models, such as GPT-4Vision (GPT-4V) from OpenAI. GPT-RadPlan is made aware of planning protocols as context and acts as an expert human planner, capable of guiding a treatment planning process. Via in-context learning, we incorporate clinical protocols for various disease sites as prompts to enable GPT-4V to acquire treatment planning domain knowledge. The resulting GPT-RadPlan agent is integrated into our in-house inverse treatment planning system through an API. The efficacy of the automated planning system is showcased using multiple prostate and head & neck cancer cases, where we compared GPT-RadPlan results to clinical plans. In all cases, GPT-RadPlan either outperformed or matched the clinical plans, demonstrating superior target coverage and organ-at-risk sparing. Consistently satisfying the dosimetric objectives in the clinical protocol, GPT-RadPlan represents the first multimodal large language model agent that mimics the behaviors of human planners in radiation oncology clinics, achieving remarkable results in automating the treatment planning process without the need for additional training.

Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.

Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.

Adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems with unmeasurable states.

This paper addresses the adaptive fixed-time fuzzy containment control for uncertain nonlinear multiagent systems, where the states and nonlinear functions are not feasible for the controller design. To address the issue of unmeasurable states, a state observer is developed, and fuzzy logic systems are utilized to approximate unknown nonlinear functions. Under the framework of fixed-time Lyapunov function theory and cooperative control, an adaptive fixed-time fuzzy containment control protocol is derived via the adaptive backstepping and adding one power integrator method. The derived fixed-time containment controller can confirm that the closed-loop systems are practical fixed-time stable, which implies that all signals in the systems are bounded and all follower agents can converge to the convex hull formed by the leader agents within fixed-time in the presence of unmeasurable states and unknown nonlinear functions . Simulation examples are conducted to test the validity of the present control algorithm.

Role of cell metabolism in the pathophysiology of brain size-associated neurodevelopmental disorders.

Cell metabolism is a key regulator of human neocortex development and evolution. Several lines of evidence indicate that alterations in neural stem/progenitor cell (NPC) metabolism lead to abnormal brain development, particularly brain size-associated neurodevelopmental disorders, such as microcephaly. Abnormal NPC metabolism causes impaired cell proliferation and thus insufficient expansion of NPCs for neurogenesis. Therefore, the production of neurons, which is a major determinant of brain size, is decreased and the size of the brain, especially the size of the neocortex, is significantly reduced. This review discusses recent progress understanding NPC metabolism, focusing in particular on glucose metabolism, fatty acid metabolism and amino acid metabolism (e.g., glutaminolysis and serine metabolism). We provide an overview of the contributions of these metabolic pathways to brain development and evolution, as well as to the etiology of neurodevelopmental disorders. Furthermore, we discuss the advantages and disadvantages of various experimental models to study cell metabolism in the developing brain.

Mitochondrial endogenous substance transport-inspired nanomaterials for mitochondria-targeted gene delivery.

Mitochondrial genome (mtDNA) independent of nuclear gene is a set of double-stranded circular DNA that encodes 13 proteins, 2 ribosomal RNAs and 22 mitochondrial transfer RNAs, all of which play vital roles in functions as well as behaviors of mitochondria. Mutations in mtDNA result in various mitochondrial disorders without available cures. However, the manipulation of mtDNA via the mitochondria-targeted gene delivery faces formidable barriers, particularly owing to the mitochondrial double membrane. Given the fact that there are various transport channels on the mitochondrial membrane used to transfer a variety of endogenous substances to maintain the normal functions of mitochondria, mitochondrial endogenous substance transport-inspired nanomaterials have been proposed for mitochondria-targeted gene delivery. In this review, we summarize mitochondria-targeted gene delivery systems based on different mitochondrial endogenous substance transport pathways. These are categorized into mitochondrial steroid hormones import pathways-inspired nanomaterials, protein import pathways-inspired nanomaterials and other mitochondria-targeted gene delivery nanomaterials. We also review the applications and challenges involved in current mitochondrial gene editing systems. This review delves into the approaches of mitochondria-targeted gene delivery, providing details on the design of mitochondria-targeted delivery systems and the limitations regarding the various technologies. Despite the progress in this field is currently slow, the ongoing exploration of mitochondrial endogenous substance transport and mitochondrial biological phenomena may act as a crucial breakthrough in the targeted delivery of gene into mitochondria and even the manipulation of mtDNA.

Acetylation-dependent deubiquitinase USP26 stabilizes BAG3 to promote breast cancer progression.

Deubiquitylases (DUBs) have emerged as promising targets for cancer therapy due to their role in stabilizing substrate proteins within the ubiquitin machinery. Here, we identified ubiquitin-specific protease 26 (USP26) as an oncogene via screening prognostic DUBs in breast cancer. Through in vitro and in vivo experiments, we found that depletion of USP26 inhibited breast cancer cell proliferation and invasion, and suppressed tumor growth and metastasis in nude mice. Further investigation identified co-chaperone Bcl-2-associated athanogene 3 (BAG3) as the direct substrate of USP26, and ectopic expression of BAG3 partially reversed antitumor effect induced by USP26 knockdown. Mechanistically, the lysine acetyltransferase Tip60 targeted USP26 at K134 for acetylation, which enhanced USP26 binding affinity to BAG3, leading to BAG3 deubiquitination and increased protein stability. Importantly, we employed a structure-based virtual screening and discovered a drug-like molecule called 5813669 that targets USP26, destabilizing BAG3 and effectively mitigating tumor growth and metastasis in vivo. Clinically, high expression levels of USP26 were correlated with elevated BAG3 levels and poor prognosis in breast cancer patients. Overall, our findings highlight the critical role of USP26 in BAG3 protein stabilization and provide a promising therapeutic target for breast cancer.

Potential of Progressive and Disruptive Innovation-Driven Cost Reductions of Green Hydrogen Production.

Green hydrogen from water electrolysis is a key driver for energy and industrial decarbonization. The prediction of the future green hydrogen cost reduction is required for investment and policy-making purposes but is complicated due to a lack of data, incomplete accounting for costs, and difficulty justifying trend predictions. A new AI-assisted data-driven prediction model is developed for an in-depth analysis of the current and future levelized costs of green hydrogen, driven by both progressive and disruptive innovations. The model uses natural language processing to gather data and generate trends for the technological development of key aspects of electrolyzer technology. Through an uncertainty analysis, green hydrogen costs have been shown to likely reach the key target of <$2.5 kg-1 by 2030 via progressive innovations, and beyond this point, disruptive technological developments are required to affect significantly further decease cost. Additionally, the global distribution of green hydrogen costs has been calculated. This work creates a comprehensive analysis of the levelized cost of green hydrogen, including the important balance of plant components, both now and as electrolyzer technology develops, and offers a likely prediction for how the costs will develop over time.

Synergistic effect of mesenchymal stem cell-derived extracellular vesicle and miR-137 alleviates autism-like behaviors by modulating the NF-κB pathway.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder predominant in childhood. Despite existing treatments, the benefits are still limited. This study explored the effectiveness of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) loaded with miR-137 in enhancing autism-like behaviors and mitigating neuroinflammation. Utilizing BTBR mice as an autism model, the study demonstrated that intranasal administration of MSC-miR137-EVs ameliorates autism-like behaviors and inhibits pro-inflammatory factors via the TLR4/NF-κB pathway. In vitro evaluation of LPS-activated BV2 cells revealed that MSC-miR137-EVs target the TLR4/NF-κB pathway through miR-137 inhibits proinflammatory M1 microglia. Moreover, bioinformatics analysis identified that MSC-EVs are rich in miR-146a-5p, which targets the TRAF6/NF-κB signaling pathway. In summary, the findings suggest that the integration of MSC-EVs with miR-137 may be a promising therapeutic strategy for ASD, which is worthy of clinical adoption.

Commissioning of a novel PET-Linac for biology-guided radiotherapy (BgRT).

BACKGROUND: Biology-guided radiotherapy (BgRT) is a novel radiotherapy delivery technique that utilizes the tumor itself to guide dynamic delivery of treatment dose to the tumor. The RefleXion X1 system is the first radiotherapy system developed to deliver SCINTIX® BgRT. The X1 is characterized by its split arc design, employing two 90-degree positron emission tomography (PET) arcs to guide therapeutic radiation beams in real time, currently cleared by FDA to treat bone and lung tumors. PURPOSE: This study aims to comprehensively evaluate the capabilities of the SCINTIX radiotherapy delivery system by evaluating its sensitivity to changes in PET contrast, its adaptability in the context of patient motion, and its performance across a spectrum of prescription doses. METHODS: A series of experimental scenarios, both static and dynamic, were designed to assess the SCINTIX BgRT system's performance, including an end-to-end test. These experiments involved a range of factors, including changes in PET contrast, motion, and prescription doses. Measurements were performed using a custom-made ArcCHECK insert which included a 2.2 cm spherical target and a c-shape structure that can be filled with a PET tracer with varying concentrations. Sinusoidal and cosine4 motion patterns, simulating patient breathing, was used to test the SCINTIX system's ability to deliver BgRT during motion-induced challenges. Each experiment was evaluated against specific metrics, including Activity Concentration (AC), Normalized Target Signal (NTS), and Biology Tracking Zone (BTZ) bounded dose-volume histogram (bDVH) pass rates. The accuracy of the delivered BgRT doses on ArcCHECK and EBT-XD film were evaluated using gamma 3%/2 mm and 3%/3 mm analysis. RESULTS: In static scenarios, the X1 system consistently demonstrated precision and robustness in SCINTIX dose delivery. The end-to-end delivery to the spherical target yielded good results, with AC and NTS values surpassing the critical thresholds of 5 kBq/mL and 2, respectively. Furthermore, bDVH analysis consistently confirmed 100% pass rates. These results were reaffirmed in scenarios involving changes in PET contrast, emphasizing the system's ability to adapt to varying PET avidities. Gamma analysis with 3%/2 mm (10% dose threshold) criteria consistently achieved pass rates > 91.5% for the static tests. In dynamic SCINTIX delivery scenarios, the X1 system exhibited adaptability under conditions of motion. Sinusoidal and cosine4 motion patterns resulted in 3%/3 mm gamma pass rates > 87%. Moreover, the comparison with gated stereotactic body radiotherapy (SBRT) delivery on a conventional c-arm Linac resulted in 93.9% gamma pass rates and used as comparison to evaluate the interplay effect. The 1 cm step shift tests showed low overall gamma pass rates of 60.3% in ArcCHECK measurements, while the doses in the PTV agreed with the plan with 99.9% for 3%/3 mm measured with film. CONCLUSIONS: The comprehensive evaluation of the X1 radiotherapy delivery system for SCINTIX BgRT demonstrated good agreement for the static tests. The system consistently achieved critical metrics and delivered the BgRT doses per plan. The motion tests demonstrated its ability to co-localize the dose where the PET signal is and deliver acceptable BgRT dose distributions.

Glutamatergic neurons in the paraventricular nucleus of the hypothalamus participate in the regulation of visceral pain induced by pancreatic cancer in mice.

Background: Visceral pain induced by pancreatic cancer seriously affects patients' quality of life, and there is no effective treatment, because the mechanism of its neural circuit is unknown. Therefore, the aim of this study is to explore the main neural circuit mechanism regulating visceral pain induced by pancreatic cancer in mice. Methods: The mouse model of pancreatic cancer visceral pain was established on C57BL/6N mice by pancreatic injection of mPAKPC-luc cells. Abdominal mechanical hyperalgesia and hunch score were performed to assess visceral pain; the pseudorabies virus (PRV) was used to identify the brain regions innervating the pancreas; the c-fos co-labeling method was used to ascertain the types of activated neurons; in vitro electrophysiological patch-clamp technique was used to record the electrophysiological activity of specific neurons; the calcium imaging technique was used to determine the calcium activity of specific neurons; specific neuron destruction and chemogenetics methods were used to explore whether specific neurons were involved in visceral pain induced by pancreatic cancer. Results: The PRV injected into the pancreas was detected in the paraventricular nucleus of the hypothalamus (PVN). Immunofluorescence staining showed that the majority of c-fos were co-labeled with glutamatergic neurons in the PVN. In vitro electrophysiological results showed that the firing frequency of glutamatergic neurons in the PVN was increased. The calcium imaging results showed that the calcium activity of glutamatergic neurons in the PVN was enhanced. Both specific destruction of glutamatergic neurons and chemogenetics inhibition of glutamatergic neurons in the PVN alleviated visceral pain induced by pancreatic cancer. Conclusions: Glutamatergic neurons in the PVN participate in the regulation of visceral pain induced by pancreatic cancer in mice, providing new insights for the discovery of effective targets for the treatment of pancreatic cancer visceral pain.