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

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

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

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

A Method to Deduce and Synthesize the Dafny Programs

We propose a systematic method to deduce and synthesize the Dafny programs. First, the specification of problem is described in strict mathematical language. Then, the derivation process uses program specification transformation technology to perform equivalent transformation. Furthermore, Dafny program is synthesized through the obtained recursive relationship and loop invariants. Finally, the functional correctness of Dafny program is automatically verified by Dafny verifier or online tool. Through this method, we deduce and synthesize Dafny programs for many typical problems such as the cube sum problem, the minimum (or maximum) contiguous subarray problems, several searching problems, several sorting problems, and so on. Due to space limitation, we only illustrate the development process of Dafny programs for two typical problems: the minimum contiguous subarray problem and the new local bubble sorting problem. It proves that our method can effectively improve the correctness and reliability of Dafny program developed. What’s more, we demonstrate the potential of the deductive synthesis method by developing a new local bubble Sorting program