Low-dose-rate induces more severe cognitive impairment than high-dose-rate in rats exposed to chronic low-dose γ-radiation

BackgroundOwing to the long penetration depth of gamma (γ)-rays, individuals working in ionizing radiation environments are chronically exposed to low-dose γ-radiation, resulting in cognitive changes. Dose rate significantly affects radiation-induced biological effects; however, its role in chronic low-dose γ-irradiation-induced cognitive impairment remains unclear. We aimed to investigate whether chronic low-dose γ-irradiation at low-dose-rate (LDR) could induce cognitive impairment and to compare the cognitive alteration caused by chronic low-dose γ-irradiation at LDR and high-dose-rate (HDR).MethodsThe rats were exposed to γ-irradiation at a LDR of 6 mGy/h and a HDR of 20 mGy/h for 30 days (5 h/day). Functional imaging was performed to assess the brain inflammation and blood–brain barrier (BBB) destruction of rats. Histological and immunofluorescence analyses were used to reveal the neuron damage and the activation of microglia and astrocytes in the hippocampus. RNA sequencing was conducted to investigate changes in gene expression in hippocampus.ResultsThe rats in the LDR group exhibited more persistent cognitive impairment than those in the HDR group. Furthermore, irradiated rats showed brain inflammation and a compromised BBB. Histologically, the number of hippocampal neurons were comparable in the LDR group but were markedly decreased in the HDR. Additionally, activated M1-like microglia and A1-like astrocytes were observed in the hippocampus of rats in the LDR group; however, only M1-like microglia were activated in the HDR group. Mechanistically, the PI3K–Akt signaling pathway contributed to the different cognitive function change between the LDR group and HDR group.ConclusionCompared with chronic low-dose γ-irradiation at HDR, LDR induced more severe cognitive impairment which might involve PI3K/Akt signaling pathway

18F- FDG PET/CT helps differentiate autoimmune pancreatitis from pancreatic cancer

Abstract Background 18F-FDG PET/CT could satisfactorily show pancreatic and extra-pancreatic lesions in AIP, which can be mistaken for pancreatic cancer (PC). This study aimed to identify 18F-FDG PET/CT findings that might differentiate AIP from PC. Methods FDG-PET/CT findings of 26 AIP and 40 PC patients were reviewed. Pancreatic and extra-pancreatic lesions related findings, including maximum standardized uptake values (SUVmax) and patterns of FDG uptake, were identified and compared. Results All 26 patients with AIP had increased pancreatic FDG uptake. Focal abnormal pancreatic FDG activities were found in 38/40 (95.00%) PC patients, while longitudinal were found in 18/26 (69.23%) AIP patients. SUVmax was significantly different between AIP and PC, both in early and delayed PET/CT scans (p < 0.05). AUCs were 0.700 (early SUVmax), 0.687 (delayed SUVmax), 0.683 (early lesions/liver SUVmax), and 0.715 (delayed lesion/liver SUVmax). Bile duct related abnormalities were found in 12/26 (46.15%) AIP and 10/40 (25.00%) PC patients, respectively. Incidentally, salivary and prostate gland SUVmax in AIP patients were higher compared with those of PC patients (p < 0.05). In males,an inverted “V” shaped high FDG uptake in the prostate was more frequent in AIP than PC patients (56.00%, 14/25 vs. 5.71%, 2/35). Increased FDG activity in extra-pancreatic bile duct was present in 4/26 of AIP patients, while was observed in none of the PC patients. Only in AIP patients, both diffuse pancreatic FDG accumulation and increased inverted “V” shaped FDG uptake in the prostate could be found simultaneously. Conclusions 18F-FDG PET/CT findings might help differentiate AIP from PC

⁶⁸Ga-HBED-CC-WL-12 PET in Diagnosing and Differentiating Pancreatic Cancers in Murine Models

Positron emission tomography (PET) has been proven as an important technology to detect the expression of programmed death ligand 1 (PD-L1) non-invasively and in real time. As a PD-L1 inhibitor, small peptide WL12 has shown great potential in serving as a targeting molecule to guide PD-L1 blockade therapy in clinic. In this study, WL12 was modified with HBED-CC to label 68Ga in a modified procedure, and the biologic properties were evaluated in vitro and in vivo. 68Ga-HBED-CC-WL12 showed good stability in saline and can specifically target PD-L1-positive cells U87MG and PANC02. In PANC02-bearing mice, 68Ga-HBED-CC-WL12 showed fast permeation in subcutaneous tumors within 20 min (SUVmax 0.37) and was of higher uptake in 90 min (SUVmax 0.38). When compared with 18F-FDG, 68Ga-FAPI-04, and 68Ga-RGD, 68Ga-HBED-CC-WL12 also demonstrated great image quality and advantages in evaluating immune microenvironment. This study modified the 68Ga-labeling procedure of WL12 and obtained better biologic properties and further manifested the clinical potential of 68Ga-HBED-CC-WL12 for PET imaging and guiding for immunotherapy

Course Intelligent Brain Model Based on Crowd Intelligence

The development of artificial intelligence in education promotes the reform of teaching methods in the direction of intelligence and individuation. In this paper, the programming course is taken as an example to propose a curriculum intelligent brain model for open source swarm intelligence based on knowledge graph, and the bootstrapping framework is introduced to try to make the intelligent brain track the frontier like human beings and study several courses vertically. It studies the knowledge of subgraphs fusion of open-source software resources and domain semantics as well as the mining method of potential relationship, so that the intelligent brain can digest knowledge like human, and get through the course horizontally. Finally, knowledge discovery and natural representation based on knowledge graph enable intelligent brain to discover knowledge and solve problems just like human. This study provides new ideas, strategies, and application paths for the construction of knowledge graph based on big data and the integration of heterogeneous knowledge graph

18F- FDG PET/CT helps differentiate autoimmune pancreatitis from pancreatic cancer

Abstract Background 18F-FDG PET/CT could satisfactorily show pancreatic and extra-pancreatic lesions in AIP, which can be mistaken for pancreatic cancer (PC). This study aimed to identify 18F-FDG PET/CT findings that might differentiate AIP from PC. Methods FDG-PET/CT findings of 26 AIP and 40 PC patients were reviewed. Pancreatic and extra-pancreatic lesions related findings, including maximum standardized uptake values (SUVmax) and patterns of FDG uptake, were identified and compared. Results All 26 patients with AIP had increased pancreatic FDG uptake. Focal abnormal pancreatic FDG activities were found in 38/40 (95.00%) PC patients, while longitudinal were found in 18/26 (69.23%) AIP patients. SUVmax was significantly different between AIP and PC, both in early and delayed PET/CT scans (p < 0.05). AUCs were 0.700 (early SUVmax), 0.687 (delayed SUVmax), 0.683 (early lesions/liver SUVmax), and 0.715 (delayed lesion/liver SUVmax). Bile duct related abnormalities were found in 12/26 (46.15%) AIP and 10/40 (25.00%) PC patients, respectively. Incidentally, salivary and prostate gland SUVmax in AIP patients were higher compared with those of PC patients (p < 0.05). In males,an inverted “V” shaped high FDG uptake in the prostate was more frequent in AIP than PC patients (56.00%, 14/25 vs. 5.71%, 2/35). Increased FDG activity in extra-pancreatic bile duct was present in 4/26 of AIP patients, while was observed in none of the PC patients. Only in AIP patients, both diffuse pancreatic FDG accumulation and increased inverted “V” shaped FDG uptake in the prostate could be found simultaneously. Conclusions 18F-FDG PET/CT findings might help differentiate AIP from PC

Automatic Algorithm Programming Model Based on the Improved Morgan's Refinement Calculus

The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development, including specification generation, program refinement, and formal verification. However, the existing model has two flaws: incompleteness of program refinement and inadequate automation of formal verification. This paper proposes an automatic algorithm programming model based on the improved Morgan's refinement calculus. It extends the Morgan's refinement calculus rules and designs the C++ generation system for realizing the complete process of refinement. Meanwhile, the automation tools VCG (Verification Condition Generator) and Isabelle are used to improve the automation of formal verification. An example of a stock's maximum income demonstrates the effectiveness of the proposed model. Furthermore, the proposed model has some relevance for automatic software generation

A Unified Strategy for Formal Derivation and Proof of Binary Tree Nonrecursive Algorithms

In the formal derivation and proof of binary tree algorithms, Dijkstra's weakest predicate method is commonly used. However, the method has some drawbacks, including a time-consuming derivation process, complicated loop invariants, and the inability to generate executable programs from the specification. This paper proposes a unified strategy for the formal derivation and proof of binary tree non-recursive algorithms to address these issues. First, binary tree problem solving sequences are decomposed into two types of recursive relations based on queue and stack, and two corresponding loop invariant templates are constructed. Second, high-reliability Apla (abstract programming language) programs are derived using recursive relations and loop invariants. Finally, Apla programs are converted automatically into C++ executable programs. Two types of problems with binary tree queue and stack recursive relations are used as examples, and their formal derivation and proof are performed to validate the proposed strategy's effectiveness. This strategy improves the efficiency and correctness of binary tree algorithm derivation