Effect of Acoustically Responsive Droplet-based Low-intensity Histotripsy on Canine Prostate.

OBJECTIVE: Low-intensity histotripsy (LIH) is a novel and safe technique for tissue ablation. This study aimed to explore the effects of LIH on canine prostate tissue and identify the degree of acute injury to the gland. METHODS: We constructed and evaluated two types of acoustically responsive droplet (ARD) emulsions using either perfluoropentane (PFP) with a lipid shell or perfluoromethyl-cyclopentane (PFMCP) with lauromacrogol (L) injection. Twenty beagles were assigned to four experimental groups: ultrasound (US) + PFP (n = 6), US + PFMCP-L (n = 6), PFMCP-L (n = 5) and PFP (n = 3). The ARDs were injected transcutaneously and transabdominally into normal canine prostates under US-guided imaging. Subsequently, focused therapeutic US was employed to induce acoustic droplet vaporization and bubble cloud cavitation. The mechanical damage to canine prostate tissue was evaluated using gross and histological examination. RESULTS: Gross specimens showed that the injured area was dark brown. Hematoxylin and eosin-stained tissue sections of the damage zone showed significant cavity formation and interstitial edema. The total tissue damage scores in the US + PFP group were compared to those of the other three experimental groups. No statistically significant differences were observed in the extent of tissue damage and total scores among the US + PFMCP-L, PFMCP-L and PFP groups. CONCLUSION: We achieved significant mechanical tissue damage in the canine prostate using PFP ARD-based LIH that proved to be superior to that using PFMCP ARDs with LIH.

Sub-picometer level all-solid-state narrow linewidth single-frequency Pr3+:LiYF4 laser in the near-infrared spectral region.

We report an all-solid-state near-infrared single-frequency (single longitudinal mode, SLM) Pr3+:LiYF4 (Pr:YLF) laser with the spectral linewidth at the sub-picometer level. The SLM lasers with center wavelengths of 868 and 907 nm are realized in Pr:YLF crystal for the first time to the best of our knowledge. The maximum output powers of SLM lasers at 868 and 907 nm are 102  and 213mW, corresponding to the narrowest spectral linewidths of 82 MHz (0.21 pm) and 94 MHz (0.26 pm), respectively. At the maximum output power, the beam quality factors in the x and y directions are measured as 1.25 and 1.16 at 868 nm and 1.21 and 1.13 at 907 nm, respectively. The output power stabilities of the 868 and 907 nm SLM lasers are calculated as 1.39% and 0.87%, respectively. The successful realization of 868 and 907 nm all-solid-state SLM lasers makes up for the gap that the Pr:YLF SLM lasers developed in the past are focused on the visible region, enriches the types of near-infrared (NIR) SLM lasers, and can provide practical applications in biomedicine, cold atom physics, and optical atom manipulation.

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study.

OBJECTIVES: To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation. METHODS: From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application. RESULTS: Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145). CONCLUSIONS: ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

Wavelength-switchable watt-level continuous wave near-infrared Pr3+:LiYF4 lasers pumped by an InGaN laser diode.

We report a high-performance wavelength-switchable near-infrared Pr3+:LiYF4 (Pr:YLF) laser by InGaN laser diode (LD) pumping. The 895, 922, and 924 nm lasers with low emission cross sections in the Pr:YLF crystal have been successfully realized using a birefringent filter Lyot as well as designing and optimizing optical thin films and the laser resonant cavity. The maximum output powers of the 895, 922, and 924 nm lasers are 2.01, 1.92, and 1.95 W, respectively. As far as we know, these are the highest power for Pr:YLF lasers at 895, 922, and 924 nm so far. The beam quality M x2 and M y2 factors are measured to be 1.85 and 1.71 at 895 nm, 1.94 and 1.67 at 922 nm, and 1.76 and 1.60 at 924 nm, respectively. The laser output power fluctuates within ±3%. In addition, the transmittance of the Lyot is theoretically calculated to achieve laser wavelength switching. The successful realization of the wavelength-switchable watt-level continuous wave near-infrared Pr:YLF laser can provide many practical applications in biomedicine and other fields.

Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study.

BACKGROUND: Microsatellite instability (MSI) is associated with treatment response and prognosis in patients with rectal cancer (RC). However, intratumoral heterogeneity limits MSI testing in patients with RC. The authors developed a subregion radiomics model based on multiparametric MRI to preoperatively assess high-risk subregions with MSI and predict the MSI status of patients with RC. METHODS: This retrospective study included 475 patients (training cohort, 382; external test cohort, 93) with RC from two participating hospitals between April 2017 and June 2023. In the training cohort, subregion radiomic features were extracted from multiparametric MRI, which included T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. MSI-related subregion radiomic features, classical radiomic features, and clinicoradiological variables were gathered to build five predictive models using logistic regression. Kaplan-Meier survival analysis was conducted to explore the prognostic information. RESULTS: Among the 475 patients [median age, 64 years (interquartile range, IQR: 55-70 years); 304 men and 171 women], the prevalence of MSI was 11.16% (53/475). The subregion radiomics model outperformed the classical radiomics and clinicoradiological models in both training [area under the curve (AUC)=0.86, 0.72, and 0.59, respectively] and external test cohorts (AUC=0.83, 0.73, and 0.62, respectively). The subregion-clinicoradiological model combining clinicoradiological variables and subregion radiomic features performed the optimal, with AUCs of 0.87 and 0.85 in the training and external test cohorts, respectively. The 3-year disease-free survival rate of MSI groups predicted based on the model was higher than that of the predicted microsatellite stability groups in both patient cohorts (training, P =0.032; external test, P =0.046). CONCLUSIONS: The authors developed and validated a model based on subregion radiomic features of multiparametric MRI to evaluate high-risk subregions with MSI and predict the MSI status of RC preoperatively, which may assist in individualized treatment decisions and positioning for biopsy.

Effects of Various Nectar and Pollen Plants on the Survival, Reproduction, and Predation of Neoseiulus bicaudus.

Neoseiulus bicaudus is a predatory mite species that could potentially be used for the biological control of spider mites and thrips. Floral resources can provide excellent habitats and abundant nutrients for natural enemies. The objective of this experiment was to evaluate the effects of eight floral resources on the longevity, fecundity, and predation ability of N. bicaudus. Among the considered plants, Cnidium monnieri led to the highest longevity (24 days) and fecundity (13.8 eggs) of N. bicaudus, while Tagetes erecta resulted in the lowest longevity (7 days) and fecundity (0.1 eggs) observed in the predatory mites. By comparing the effects of three nectar and pollen plants on the predation of predatory mites, it was observed that N. bicaudus still exhibited a type II functional response to Tetranychus turkestani. In the presence of pollen, the predation efficacy (a/Th) of N. bicaudus exhibited a lower value, compared to that in the absence of pollen (Control: a/Th = 24.00). When pollen was supplied, the maximum consumption (1/Th) of predatory mites was higher than in its absence (Control: 1/Th = 9.90 d-1), with the highest value obtained in the presence of B. officinalis pollen (B. officinalis: 1/Th = 17.86 d-1). The influence coefficient of predation of N. bicaudus on T. turkestani in the presence of pollen was compared in the presence of three nectar and pollen plants: Cnidium monnieri, Centaurea cyanus, and Borago officinalis. At low prey densities, the influence coefficient of C. cyanus exceeded that of B. officinalis, and the overall influence coefficient values were negative (i.e., the presence of pollen reduced predatory mite feeding on T. turkestani). They exhibited similar values at high prey densities, and all of the influence coefficient values were close to 0 (i.e., the presence of pollen had no effect on predatory mite feeding on T. turkestani). The findings revealed that diverse plant species exert differential impacts on N. bicaudus, with some influencing its lifespan and others affecting its reproductive capabilities. Furthermore, the presence of nectar and pollen plants had a significant impact on predatory mite feeding on T. turkestani at low prey densities; however, this effect diminished as the prey density increased. Therefore, we recommend planting C. monnieri, C. cyanus, and B. officinalis in the field to ensure an ample population of predatory mites. The obtained results hold significant implications for the utilization of nectar and pollen plants in eco-friendly pest management strategies within agricultural contexts.

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network.

With the development of various applications, such as recommendation systems and social network analysis, graph data have been ubiquitous in the real world. However, graphs usually suffer from being absent during data collection due to copyright restrictions or privacy-protecting policies. The graph absence could be roughly grouped into attribute-incomplete and attribute-missing cases. Specifically, attribute-incomplete indicates that a portion of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that all attribute vectors of partial nodes are missing. Although various graph imputation methods have been proposed, none of them is custom-designed for a common situation where both types of graph absence exist simultaneously. To fill this gap, we develop a novel graph imputation network termed revisiting initializing then refining (RITR), where both attribute-incomplete and attribute-missing samples are completed under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structure matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first end-to-end unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on six datasets have verified that our methods consistently outperform the existing state-of-the-art competitors. Our source code is available at https://github.com/WxTu/RITR.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

BACKGROUND: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

Augmentation of tumour perfusion by ultrasound and microbubbles: A preclinical study.

BACKGROUND: Hypoperfusion and the resulting hypoxia in solid tumours are critical causes of treatment resistance. Ultrasound-stimulated microbubbles (USMB) enhance tumour perfusion in a mechanism named the "sononeoperfusion" effect, which may relieve tumour hypoperfusion and hypoxia. The aim of this study was to determine the optimal mechanical index (MI) and therapeutic ultrasound exposure time for the sononeoperfusion effect and preliminarily explore the mechanism of sononeoperfusion and its effect on tumours. METHODS: A total of 155 mice bearing MC38 tumours were included in this study. A modified diagnostic ultrasound and microbubbles (Zhifuxian) was used for USMB treatment. Tumour perfusion was evaluated by contrast-enhanced ultrasound (CEUS) and Hoechst 33342. The therapeutic pulse was operated with MIs of 0.1 to 0.5. The ultrasound exposure time was set from 150 s to 600 s. Endothelial nitric oxide synthase (eNOS) inhibition and NO, ATP, and phospho-eNOS (p-eNOS) detection were performed to explore the mechanisms of sononeoperfusion. Hypoxia-inducible factor-1α (HIF-1α) and tumour oxygen partial pressure (pO2) represent hypoxic tumour conditions. RESULTS: Tumour perfusion was increased after USMB treatment at MIs of 0.1-0.4 and ultrasound exposure times of 150 s to 600 s, with optimal augmentation achieved at an MI of 0.3 and ultrasound exposure time of 450 s. The mean fluorescence intensity of Hoechst 33342 after USMB treatment was stronger than that of the control group. Biochemical assays showed a significant increase in ATP, p-eNOS and NO after USMB treatment. PO2 in tumour tissue increased significantly after USMB treatment and was maintained for more than 20 min. CONCLUSIONS: The best sononeoperfusion effect was obtained with an MI of 0.3 and an ultrasound exposure time of 450 s. The effect is most likely related to NO and ATP increases. The sononeoperfusion effect might be a novel way to ameliorate tumour hypoperfusion and hypoxia.

AI-algorithm-assisted 895-nm praseodymium laser emitting sub-100-fs pulses.

Praseodymium (Pr) lasers have achieved outstanding pico- and sub-picosecond pulsations covering the near-infrared (NIR) and visible spectral range in recent years. However, it has been a stagnant task for more than two decades to leapfrog into the sub-100 femtosecond (fs) regime as the Pr gain bandwidths are too narrow for their major transition lines. Although the wide tunability at the NIR bands in the Pr:YLF crystals has been explored, the spectral tails in these transitions suffer severely from weak gains for mode locking, combined with the intricate dispersion control to achieve transform-limit formation. In this work, we target the Pr:YLF's 895-nm line with a specially designed edge-pass filter to balance the gain bandwidth and transitional strength. By deploying a symmetric dispersion scheme and tuning with the soft actor-critic artificial intelligence (AI) algorithm, we have achieved the pulse duration down to sub-100-fs in a Pr laser for the first time. This work also enriches the AI-assisted methodology for ultrafast solid-state laser realizations.