Automatic construction of coronary artery tree structure based on vessel blood flow tracking.

We sought to propose an innovative vessel blood flow tracking (VBFT) method to extract coronary artery tree (CAT) and to assess the effectiveness of this VBFT versus the single-frame method. Construction of a CAT from a segmented artery is the basis of artificial intelligence-aided angiographic diagnosis. However, construction of a CAT using a single frame remains challenging, due to bifurcations and overlaps in two-dimensional angiograms. Overall, 13,222 angiograms, including 28,539 vessels, were retrospectively collected from 3275 patients and were then annotated. Coronary arteries were automatically segmented by a previously established deep neural networks (DNNs), and the skeleton lines were then extracted from segmentation images to construct CAT using the single-frame method and the VBFT method. Additionally, 1322 angiograms with 2201 vessels were used to test these two methods. Compared to the single-frame method, the VBFT method can significantly improve the accuracy of CAT as (84.3% vs. 72.3%; p < 0.001). Overlap (OV) was higher in the VBFT group than that in the Single-Frame group (91.1% vs. 87.5%; p < 0.001). The VBFT method significantly reduced the incidence of the lack of branching (7.30% vs. 13.9%, p < 0.001), insufficient length (6.70% vs. 11.0%, p < 0.001), and redundant branches (1.60% vs. 3.10%, p < 0.001). The VBFT method improved the extraction of a CAT structure, which will facilitate the development of artificial intelligence-aided angiographic diagnosis. Cardiologists can efficiently diagnose CAD using this method.

[A Thyroid Ultrasound Image-based Artificial Intelligence Model for Diagnosis of Central Compartment Lymph Node Metastasis in Papillary Thyroid Carcinoma].

Objective To establish an artificial intelligence model based on B-mode thyroid ultrasound images to predict central compartment lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods We retrieved the clinical manifestations and ultrasound images of the tumors in 309 patients with surgical histologically confirmed PTC and treated in the First Medical Center of PLA General Hospital from January to December in 2018.The datasets were split into the training set and the test set.We established a deep learning-based computer-aided model for the diagnosis of CLNM in patients with PTC and then evaluated the diagnosis performance of this model with the test set. Result The accuracy,sensitivity,specificity,and area under receiver operating characteristic curve of our model for predicting CLNM were 80%,76%,83%,and 0.794,respectively. Conclusion Deep learning-based radiomics can be applied in predicting CLNM in patients with PTC and provide a basis for therapeutic regimen selection in clinical practice.

Pyruvate kinase M2 modification by a lipid peroxidation byproduct acrolein contributes to kidney fibrosis.

Renal fibrosis is a hallmark of diabetic nephropathy (DN) and is characterized by an epithelial-to-mesenchymal transition (EMT) program and aberrant glycolysis. The underlying mechanisms of renal fibrosis are still poorly understood, and existing treatments are only marginally effective. Therefore, it is crucial to comprehend the pathophysiological mechanisms behind the development of renal fibrosis and to generate novel therapeutic approaches. Acrolein, an α-,ß-unsaturated aldehyde, is endogenously produced during lipid peroxidation. Acrolein shows high reactivity with proteins to form acrolein-protein conjugates (Acr-PCs), resulting in alterations in protein function. In previous research, we found elevated levels of Acr-PCs along with kidney injuries in high-fat diet-streptozotocin (HFD-STZ)-induced DN mice. This study used a proteomic approach with an anti-Acr-PC antibody followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to identify several acrolein-modified protein targets. Among these protein targets, pyruvate kinase M2 (PKM2) was found to be modified by acrolein at Cys358, leading to the inactivation of PKM2 contributing to the pathogenesis of renal fibrosis through HIF1α accumulation, aberrant glycolysis, and upregulation of EMT in HFD-STZ-induced DN mice. Finally, PKM2 activity and renal fibrosis in DN mice can be reduced by acrolein scavengers such as hydralazine and carnosine. These results imply that acrolein-modified PKM2 contributes to renal fibrosis in the pathogenesis of DN.

Dry powder pharmaceutical biologics for inhalation therapy.

Therapeutic biologics such as genes, peptides, proteins, virus and cells provide clinical benefits and are becoming increasingly important tools in respiratory medicine. Pulmonary delivery of therapeutic biologics enables the potential for safe and effective treatment option for respiratory diseases due to high bioavailability while minimizing absorption into the systemic circulation, reducing off-target toxicity to other organs. Development of inhalable powder formulation requires stabilization of complex biological materials, and each type of biologics may present unique challenges and require different formulation strategy combined with manufacture process to ensure biological and physical stabilities during production and over shelf-life. This review examines key formulation strategies for stabilizing proteins, nucleic acids, virus (bacteriophages) and bacterial cells in inhalable powders. It also covers characterization methods used to assess physicochemical properties and aerosol performance of the powders, biological activity and structural integrity of the biologics, and chemical analysis at the nanoscale. Furthermore, the review includes manufacture technologies which are based on lyophilization and spray-drying as they have been applied to manufacture Food and Drug Administration (FDA)-approved protein powders. In perspective, formulation and manufacture of inhalable powders for biologic are highly challenging but attainable. The key requirements are the stability of both the biologics and the powder, along with the powder dispersibility. The formulation to be developed depends on the manufacture process as it will subject the biologics to different stresses (temperature, mechanical and chemical) which could lead to degradation by different pathways. Stabilizing excipients coupled with the suitable choice of process can alleviate the stability issues of inhaled powders of biologics.

Image Segmentation of Thyroid Nodule and Capsule for Diagnosing Central Compartment Lymph Node Metastasis.

Thyroid ultrasound (US) image segmentation is of great significance for both doctors and patients. However, it is a challenging task because of the low image quality, low contrast and complex background in each US image. In recent years, some researchers have done thyroid nodule segmentation tasks, but the results achieved are not particularly satisfactory. In this paper, we have broadened the targets of interest and included both thyroid nodules and capsules into our research scope. We propose a method that implements a C-MMDetection to detect and extract the region of interest (ROI), and a modified salient object detection network U2-RNet to segment nodules and capsules respectively. Experiments show that our method segments nodules and capsules in US images more effectively than other networks, which is very helpful for doctors to diagnose central compartment lymph node metastasis (CLNM).

Administration of dry powders during respiratory supports.

Inhaled drugs are routinely used for the treatment of respiratory-supported patients. To date, pressurized metered dose inhalers and nebulizers are the two platforms routinely employed in the clinical setting. The scarce utilization of the dry powder inhaler (DPI) platform is partly due to the lack of in vivo data that proves optimal delivery and drug efficacy are achievable. Additionally, fitting a DPI in-line to the respiratory circuit is not as straightforward as with the other aerosol delivery platforms. Importantly, there is a common misconception that the warm and humidified inspiratory air in respiratory supports, even for a short exposure, will deteriorate powder formulation compromising its delivery and efficacy. However, some recent studies have dispelled this myth, showing successful delivery of dry powders through the humidified circuit of respiratory supports. Compared with other aerosol delivery devices, the use of DPIs during respiratory supports possesses unique advantages such as rapid delivery and high dose. In this review, we presented in vitro studies showing various setups employing commercial DPIs and effects of ventilator parameters on the aerosol delivery. Inclusion of novel DPIs was also made to illustrate characteristics of an ideal inhaler that would give high lung dose with low powder deposition loss in tracheal tubes and respiratory circuits. Clinical trials are urgently needed to confirm the benefits of administration of dry powders in ventilated patients, thus enabling translation of powder delivery into practice.

Training and validation of a deep learning architecture for the automatic analysis of coronary angiography.

BACKGROUND: In recent years, the use of deep learning has become more commonplace in the biomedical field and its development will greatly assist clinical and imaging data interpretation. Most existing machine learning methods for coronary angiography analysis are limited to a single aspect. AIMS: We aimed to achieve an automatic and multimodal analysis to recognise and quantify coronary angiography, integrating multiple aspects, including the identification of coronary artery segments and the recognition of lesion morphology. METHODS: A data set of 20,612 angiograms was retrospectively collected, among which 13,373 angiograms were labelled with coronary artery segments, and 7,239 were labelled with special lesion morphology. Trained and optimised by these labelled data, one network recognised 20 different segments of coronary arteries, while the other detected lesion morphology, including measures of lesion diameter stenosis as well as calcification, thrombosis, total occlusion, and dissection detections in an input angiogram. RESULTS: For segment prediction, the recognition accuracy was 98.4%, and the recognition sensitivity was 85.2%. For detecting lesion morphologies including stenotic lesion, total occlusion, calcification, thrombosis, and dissection, the F1 scores were 0.829, 0.810, 0.802, 0.823, and 0.854, respectively. Only two seconds were needed for the automatic recognition. CONCLUSIONS: Our deep learning architecture automatically provides a coronary diagnostic map by integrating multiple aspects. This helps cardiologists to flag and diagnose lesion severity and morphology during the intervention.

Overcoming challenges for development of amorphous powders for inhalation.

INTRODUCTION: Amorphous powder formulations exist in marketed dry-powder inhaler (DPI) products and they will continue to increase. However, amorphous powders are inherently unstable and prone to recrystallize with the aerosol performance reduced if not handled properly. AREAS COVERED: In this review, we described the occurrence of amorphous materials for inhalation resulting from the production process along with major issues and challenges, followed by risk mitigation strategies for amorphous inhalation powders, including protective packaging, processes for minimization of amorphous contents, use of substances with a high glass transition temperature, coating or surface treatment of the powders and co-formulations of drugs. Specific examples were included for illustration of these strategies, and in particular, emphasis was placed on the use of hydrophobic excipients such as leucine and stearates, and co-amorphous glass systems of two drugs or a drug and an excipient. EXPERT OPINION: Researchers have been striving to overcome many problems associated with developing and delivering amorphous powders for inhalation. A combination of two or more de-risking approaches covered in this review may help circumvent instability of amorphous inhalation powders and maintain aerosol performance during drug delivery and storage.

Spray drying lactose from organic solvent suspensions for aerosol delivery to the lungs.

Lactose is widely used as an approved excipient for dry powder inhaler (DPI) products. Spray drying technique is a rapid method for converting a liquid feed into inhalable dried particles. However, spray-dried (SD) lactose powders produced from solutions are mostly amorphous and particularly unstable when exposed to moisture. In the present study, we explored the use of spray drying suspensions containing crystalline lactose particles in an organic solvent, and investigated the physicochemical properties of the resulting powders. The solution formulation was spray dried as a control. Two conditioned crystalline lactose samples were used for suspension formulations: Lactohale (LH) 300 lactose and jet-milled (JM) lactose micronized from LH300. The suspension formulations each contained 12 mg/ml suspended crystalline lactose particles (either LH300 or JM lactose) in isopropyl alcohol. The solution formulation contained 60 mg/ml lactose in water. The SD powders were stored under 25 °C/60% RH and 40 °C/75% RH for 3 months. The particulate properties and in vitro dispersion performance were examined at various time points. The SD lactose obtained from solution recrystallized and was no longer dispersible after 1-day storage at both storage conditions. The suspension SD JM lactose powder showed deterioration in the particulate properties and dispersibility over time, but more gradually. In contrast, the SD LH300 powder was stable, with its particulate properties and dispersion performance (FPF: ~12%) remaining the same after 3-months storage at 25 °C/60% RH. The SD LH300 stored at 40 °C/75% RH showed no change in particulate properties, but the FPF decreased over 3 months. Overall, SD lactose powders obtained from suspension demonstrated superior stability performance compared to SD lactose obtained from solution.

A new hypothesis to investigate bioequivalence of pharmaceutical inhalation products.

BACKGROUND: This short communication reports a new hypothesis regarding bioequivalence of inhalation products which can potentially provide a reliable means to compare pharmaceutical aerosol formulations and inhalers. METHODS: Available methods regarding the bioequivalence studies, inhaled drugs and advantages of exhaled breath condensate (EBC) samples were reviewed to develop this hypothesis. RESULTS: It is postulated that two inhalation products providing the same drug concentrations in airway lining fluid (ALF) could be considered bioequivalent. The use of EBC tests which reflect ALF composition can be recommended as an alternative to current testing methods for consideration of bioequivalence. CONCLUSION: The methods based on EBC analysis can potentially be applied to bioequivalence study of inhalation products and could reflect drug concentration in ALF. However, experimental studies would be necessary to support or refute this hypothesis on the novel application of EBC to bioequivalence in the future. Graphical abstract In vitro (cascade impactor) and In vivo (EBC concentration) corrolation for inhaled drugs.

A Study of the Measurement of Surface and Interfacial Tension by the Maximum Liquid Drop Volume Method.

The maximum liquid drop volume (nu(max)) is measured using a back-suction technique with a micrometer syringe piston. The residual amount of liquid on the tip was determined and the tension data calculated from the theoretical correction factors are well in accordance with those from the falling drop volume (nu(f)). Copyright 2001 Academic Press.

A Study of the Measurement of Surface and Interfacial Tension by the Maximum Liquid Drop Volume Method.

The maximum liquid drop volume (v(max)) is measured by using a back-suction micrometer syringe piston technique. Different very viscous liquids are measured by (v(max)) and (v(f)) methods to observe the effect of viscosity on tension measurement. No apparent viscosity effect was observed in surface tension data obtained by using Harkins-Brown factors and the theoretical correction factors in the viscosity range 5.9-100,900 mP. Copyright 2001 Academic Press.

Studies on the Binary Coalescence Model.

The jumping coalescence phenomenon between two separate water drops under microgravitation was observed for the first time. Two suspended water drops separated by quite a distance (0.558 mm) coalesce in a certain time (158 s) without any extra forces. The coalescence driving force within the water drops should be responsible for the jumping coalescence phenomenon. Copyright 2001 Academic Press.