Unravelling High Water Vapor-Induced Inhibitory Effects on Pt/Co3O4 Catalysts toward Benzene Oxidation.

Water vapor inevitably exists in the environment, which causes adverse impacts on many crucial chemical reactions. However, high water vapor of up to 10 vol %─relevant to a broad spectrum of industrial practices-for catalytic implications has been less investigated or neglected. As such, we explored an industry-relevant, humidity-highly sensitive benzene oxidation only in the presence of 10 vol % water vapor using the well-established Pt/Co3O4 catalysts, to bring such an important yet ignored topic to the forefront. Results revealed that Pt/Co3O4 catalysts possessing higher contents of Pt nanoparticles exhibited marked tolerance to water vapor interference. Under an incomplete benzene conversion condition, the input of 10 vol % water vapor indeed impaired the catalytic performance of Pt/Co3O4 catalyst significantly, which, in fact, was caused by the unfavorable formation of carboxylate species covering the catalyst's surface engendering irrecoverable activity loss, instead of the well-accepted water competitive adsorption. While such activity loss can be restored by elevating the reaction to a higher temperature. This study helps us to understand the compromised catalytic activity caused by high humidity, urging the systematic evaluation of well-established catalyst systems in high water vapor-contained conditions and pressing the development of water-tolerant catalysts for real-life application consideration.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

[Relationship between short-chain fatty acids in the gingival crevicular fluid and periodontitis of stage â ¢ or â £].

OBJECTIVE: To analyze the concentration of formic acid, propionic acid and butyric acid in gingival crevicular fluid (GCF) of patients with stages Ⅲ and Ⅳ periodontitis, and their relationship with periodontitis. METHODS: The study enrolled 37 systemically healthy patients with periodontitis and 19 healthy controls who visited Department of Periodontology, Peking University School and Hospital of Stomatology from February 2008 to May 2011. Their GCFs were collected from the mesial-buccal site of one molar or incisor in each quadrant. Periodontal clinical parameters, including plaque index(PLI), probing depth(PD), bleeding index(BI), and attachment loss(AL). Concentrations of formic acid, propionic acid and butyric acid in the supernatant of the GCFs were analyzed by high-performance capillary electrophoresis (HPCE). The prediction ability of formic acid, propionic acid and butyric acid with the risk of periodontitis and the differences between grade B and grade C periodontitis were analyzed. RESULTS: In this study, 32 patients with stage Ⅲ and 5 patients with stage Ⅳ were enrolled, including 9 patients with grade B and 28 patients with grade C. Clinical periodontal variables in the patients with periodontitis were significantly higher than those in the control group (P<0.001). Formic acid was significantly lower in periodontitis than that in the control group [5.37 (3.39, 8.49) mmol/L vs. 12.29 (8.35, 16.57) mmol/L, P<0.001]. Propionic acid and butyric acid in periodontitis were significantly higher than those in the control group: Propionic acid, 10.23 (4.28, 14.90) mmol/L vs. 2.71 (0.00, 4.25) mmol/L, P < 0.001; butyric acid, 2.63 (0.47, 3.81) mmol/L vs. 0.00 (0.00, 0.24) mmol/L, P<0.001. There was no significant difference in formic acid, propionic acid and butyric acid concentrations between grade B and grade C periodontitis (P>0.05). Propionic acid and butyric acid in the deep pocket were significantly higher than in the shallow pocket, while the concentration of formic acid decreased with the increase of PD. Propionic acid (OR=1.51, 95%CI: 1.29-1.75) and butyric acid (OR=3.72, 95%CI: 1.93-7.17) were risk factors for periodontitis, while formic acid (OR=0.87, 95%CI: 0.81-0.93) might be a protective factor for periodontitis. Propionic acid (AUC=0.852, 95%CI: 0.805－0.900), butyric acid (AUC=0.889, 95%CI: 0.841－0.937), f (formic acid, AUC=0.844, 95%CI: 0.793－0.895) demonstrated a good predictive capacity for the risk of periodontitis. CONCLUSION: The concentration of formic acid decrease in the GCF of periodontitis patients, which is a protective factor for periodontitis, its reciprocal have good predictive capacity. However, propionic acid and butyric acid increase, which are risk factors for periodontitis and have good predictive capacity. The concentration of formic acid, propionic acid, and butyric acid vary with probing depth, but there is no significant difference between grade B and grade C periodontitis.

Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Background Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard. Materials and Methods US images of thyroid nodules with pathologic results were retrospectively collected from a tertiary referral hospital between July 2022 and December 2022 and used to evaluate malignancy diagnoses generated by three LLMs-OpenAI's ChatGPT 3.5, ChatGPT 4.0, and Google's Bard. Inter- and intra-LLM agreement of diagnosis were evaluated. Then, diagnostic performance, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), was evaluated and compared for the LLMs and three interactive approaches: human reader combined with LLMs, image-to-text model combined with LLMs, and an end-to-end convolutional neural network model. Results A total of 1161 US images of thyroid nodules (498 benign, 663 malignant) from 725 patients (mean age, 42.2 years ± 14.1 [SD]; 516 women) were evaluated. ChatGPT 4.0 and Bard displayed substantial to almost perfect intra-LLM agreement (κ range, 0.65-0.86 [95% CI: 0.64, 0.86]), while ChatGPT 3.5 showed fair to substantial agreement (κ range, 0.36-0.68 [95% CI: 0.36, 0.68]). ChatGPT 4.0 had an accuracy of 78%-86% (95% CI: 76%, 88%) and sensitivity of 86%-95% (95% CI: 83%, 96%), compared with 74%-86% (95% CI: 71%, 88%) and 74%-91% (95% CI: 71%, 93%), respectively, for Bard. Moreover, with ChatGPT 4.0, the image-to-text-LLM strategy exhibited an AUC (0.83 [95% CI: 0.80, 0.85]) and accuracy (84% [95% CI: 82%, 86%]) comparable to those of the human-LLM interaction strategy with two senior readers and one junior reader and exceeding those of the human-LLM interaction strategy with one junior reader. Conclusion LLMs, particularly integrated with image-to-text approaches, show potential in enhancing diagnostic medical imaging. ChatGPT 4.0 was optimal for consistency and diagnostic accuracy when compared with Bard and ChatGPT 3.5. © RSNA, 2024 Supplemental material is available for this article.

The long-term effect of periodontitis treatment on changes in blood inflammatory markers in patients with generalized aggressive periodontitis.

BACKGROUND: Periodontitis is characterized by local inflammatory conditions in the periodontium, its severe form has been associated with elevated systemic inflammatory markers. However, the long-term effects of periodontal inflammation control on systemic inflammatory markers are unclear. OBJECTIVE: This study aimed to investigate the long-term effects of periodontal therapy on the levels of peripheral venous blood inflammatory markers in patients with generalized aggressive periodontitis (GAgP), all of whom were now diagnosed as Stage III or IV Grade C periodontitis. METHODS: Patients with GAgP were consecutively recruited from April 2013 to August 2014 (T0). Active periodontal treatment (APT) was provided, and follow-ups were conducted over a 3- to 5-year period (T1). Clinical parameters were assessed and fasting venous blood was collected at T0 and T1. Complete blood cell counts were obtained, and biochemical analyses were performed to evaluate the levels of serum components. The correlations between probing depth (PD) and hematological parameters were analyzed. RESULTS: A total of 49 patients with GAgP completed APT and follow-ups. Probing depth (PD) reduced from 5.10 ± 1.07 mm at T0 to 3.15 ± 0.65 mm at T1. For every 1-mm reduction in PD after treatment, the neutrophil count, neutrophil-lymphocyte ratio, and total protein concentration were reduced by 0.33 × 109/L, 0.26, and 1.18 g/L, respectively. In contrast, the albumin/globulin ratio increased by 0.10. CONCLUSION: This study indicated that periodontal therapy may have beneficial effects on peripheral venous blood inflammatory markers in patients with GAgP during long-term observation.

[Histopathological characteristics of peri-implant soft tissue in reconstructed jaws with vascularized bone flaps].

OBJECTIVE: To analyze the histopathological characteristics of peri-implant soft tissue in reconstructed jaws and the changes after keratinized mucosa augmentation (KMA) with free gingival graft (FGG). METHODS: Twenty patients were enrolled in this study. Five patients of them, who were periodontal and systemic healthy and referred for crown lengthening before restoration with healthy keratinized gingiva collected were enrolled as healthy controls. 15 patients of them were with fibula or iliac bone flaps jaw reconstruction (10 with fibula flap and 5 with iliac flap), who were referred to FGG and implant exposures before restoration. Soft tissue was collected before FGG in reconstructed jaws, and in 5 patients (3 with fibula flap and 2 with iliac flap) 8 weeks after FGG if a second surgery was conducted. Histological analysis with hematoxylin-eosin stain and immunological analysis to interlukin-1 (IL-1), interlukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were performed. RESULTS: Thickness from the bottom of stratum basale to the top of stratum granulosum and thickness of keratinized layer in reconstructed jaws were significantly lower compared with that of natural healthy keratinized gingiva [0.27 (0.20, 0.30) mm vs. 0.36 (0.35, 0.47) mm, P<0.05; 16.49 (14.90, 23.37) µm vs. 26.37 (24.12, 31.53) µm, P<0.05]. In the reconstructed area, thickness from the bottom of stratum basale to the top of stratum granulosum increased after KMA with FGG [0.19 (0.16, 0.25) mm vs. 0.38 (0.25, 0.39) mm, P=0.059] and the thickness of keratinized layer significantly increased after KMA with FGG [16.42 (14.16, 22.35) µm vs. 28.57 (27.16, 29.14) µm, P<0.05], which was similar to that in the control group. Furthermore, the number of positive cells of IL-1, IL-6 and TNF-α significantly increased after KMA [0.67 (0.17, 8.93) vs. 11.00 (9.16, 18.00); 13.00 (8.50, 14.14) vs. 21.89 (15.00, 28.12); 0.22 (0.04, 0.63) vs. 2.83 (1.68, 5.00), respectively, P<0.05] as well as the average optical density value [0.15 (0.14, 0.17) vs. 0.18 (0.17, 0.21); 0.28 (0.26, 0.33) vs. 0.36 (0.33, 0.37); 0.23 (0.22, 0.29) vs. 0.30 (0.28, 0.42), respectively, P<0.05], which was similar to that in the healthy keratinized gingiva. CONCLUSION: The lack of rete pegs and inflammatory factors were common in soft tissue with jaw reconstruction. FGG can improve the quality of the epithelium and may improve the stability of the mucosa around implants.

Types and treatment of peri-implantitis bone defects / 口腔疾病防治

@#The reconstruction effect of peri-implantitis bone defects depends on their morphological characteristics. This paper reviews the morphological classification and treatment of peri-implantitis bone defects. A literature review shows that the morphological classification of bone defects in peri-implantitis includes morphology classification and clinical classification. At present, the Renvert classification is more commonly used in the clinic and is divided into four-wall bone pockets, three-wall bone pockets, two-wall bone pockets, one-wall bone pocket and dehiscence according to the number of bone walls. This has guiding significance in the treatment plan of peri-implantitis. The treatment of peri-implantitis depends on the severity of peri-implant bone defects. Peri-implantitis with mild bone defects is treated by nonsurgical treatment, peri-implantitis with severe bone defects is recommended to remove the implant, and peri-implantitis with moderate bone defects is further judged according to the shape of the bone defects. Four-wall bone pockets, three-wall bone pockets and dehiscence are mostly treated by bone regenerative surgery. For shallow two-wall bone pockets, one-wall bone pockets and horizontal bone resorption, bone resection is often used. However, most peri-implantitis has a variety of bone defect forms at the same time, which need to be treated with bone regenerative surgery and bone resection surgery.