CNN2

Last update 08 May 2025

Basic Info

Synonyms

calponin 2, Calponin H2, smooth muscle, Calponin-2

+ [2]

Introduction

Thin filament-associated protein that is implicated in the regulation and modulation of smooth muscle contraction. It is capable of binding to actin, calmodulin and tropomyosin. The interaction of calponin with actin inhibits the actomyosin Mg-ATPase activity.

100 Clinical Results associated with CNN2

100 Translational Medicine associated with CNN2

0 Patents (Medical) associated with CNN2

Literatures (Medical) associated with CNN2

01 May 2025·Gastrointestinal Endoscopy

A convolutional neural network–based system for identifying neuroendocrine neoplasms and multiple types of lesions in the pancreas using EUS (with videos)

Article

Author: Gao, Jun ; Li, Guang-Chao ; Ling, Ze-Le ; Liang, Xiao ; Chen, Chang-Xu ; Ma, Shi-Yang ; Chang, Le ; Li, Zhen ; Wang, Li-Mei ; Zhang, Xin-Xin ; Wang, Peng ; Zhang, Guo-Ming ; Zhong, Ning ; Song, Yi-Hao ; Ni, Jie-Kun

BACKGROUND AND AIMSEUS is sensitive in detecting pancreatic neuroendocrine neoplasm (pNEN). However, the endoscopic diagnosis of pNEN is operator-dependent and time-consuming because pNEN mimics normal pancreas and other pancreatic lesions. We intended to develop a convolutional neural network (CNN)-based system, named iEUS, for identifying pNEN and multiple types of pancreatic lesions using EUS.METHODSRetrospective data of 12,200 EUS images obtained from pNEN and non-pNEN pancreatic lesions, including pancreatic ductal adenocarcinoma (PDAC), autoimmune pancreatitis (AIP), and pancreatic cystic neoplasm (PCN), were used to develop iEUS, which was composed of a 2-category (pNEN or non-pNEN pancreatic lesions) classification model (CNN1) and a 4-category (pNEN, PDAC, AIP, or PCN) classification model (CNN2). Videos from consecutive patients were prospectively collected for a human-iEUS contest to evaluate the performance of iEUS.RESULTSFive hundred seventy-three patients were enrolled in this study. In the human-iEUS contest containing 203 videos, CNN1 and CNN2 showed an accuracy of 84.2% and 88.2% for diagnosing pNEN, respectively, which were significantly higher than that of novices (75.4%) and comparable with intermediate endosonographers (85.5%) and experts (85.5%). In addition, CNN2 showed an accuracy of 86.2%, 97.0%, and 97.0% for diagnosing PDAC, AIP, and PCN, respectively. With the assistance of iEUS, the sensitivity of endosonographers at all 3 levels in diagnosing pNEN has significantly improved (64.6% vs 44.8%, 87.5% vs 71.9%, and 74.0% vs 57.6%, respectively).CONCLUSIONSThe iEUS precisely diagnosed pNEN and other confusing pancreatic lesions and thus can assist endosonographers in achieving more accessible and accurate endoscopic diagnoses with EUS. (Clinical trial registration number: ChiCTR2100049697.).

01 Apr 2025·Autonomic Neuroscience

Quantitative serum proteomic analysis for biomarker discovery in post-COVID-19 postural orthostatic tachycardia syndrome (PC-POTS) patients

Article

Author: Hoke, Ahmet ; Chung, Tae ; Jeong, Seeun Judy ; Ryu, Taekyung ; Adler, Brittany L ; Na, Chan Hyun ; Lee, David C

Postural orthostatic tachycardia syndrome (POTS) is a chronic, debilitating condition that is characterized by an excessive increase in heart rate upon orthostatic challenge. Before the COVID-19 pandemic, POTS affected 0.5 % to 1 % of the U.S. population. Since the pandemic, the incidence has risen sharply, adding an estimated 6-7 million new cases in the U.S. Despite its importance, there is currently no reliable biomarker for POTS, leading to significant diagnostic delays. A major hurdle in identifying biomarkers is the heterogeneous nature of the syndrome. To address this, we focused on a homogeneous subgroup of post-COVID-19 POTS (PC-POTS) patients. We conducted quantitative proteomics on sera from 9 PC-POTS patients and 9 healthy controls, identifying 31 proteins with significantly different abundances in PC-POTS patients. Most elevated proteins were linked to actin filaments or immune functions/inflammation. Weighted Gene Co-Expression Network Analysis revealed module 7 (M7) correlated strongly with PC-POTS diagnosis and related traits. The key proteins in M7 included MTPN, TAGLN2, ADP-ribosylation factor 1, PDLIM1, PPIA, CNN2, LGALSL, TXN, TLN1, TUBA4A, IL4, TREML1, GP1BA, and, all highly correlated with these traits. Cell-type enrichment analysis revealed that M7 was highly associated with immune and neuronal cells. The main pathways identified in M7 included the integrin signaling pathway, blood coagulation, and glycolysis. These findings suggest that the key proteins in M7 could serve as biomarkers for PC-POTS. This study uses quantitative proteomics to identify potential biomarkers that differentiate PC-POTS patients from healthy controls, establishing a foundation for further research and validation.

01 Jan 2025·Journal of the World Federation of Orthodontists

Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study

Article

Author: Sanchez, Flavio Jose ; Rhee, Matthew ; Allareddy, Veerasathpurush ; Milani, Omid Halimi ; Cetin, Ahmet Enis ; Elnagar, Mohammed H

INTRODUCTIONThis article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment outcomes and informed decision-making.MATERIALS AND METHODSA retrospective study was conducted using patient data from the University of Illinois Chicago Department of Orthodontics. Data included demographic, clinical, and radiographic information from 115 anterior open bite patients who successfully completed their treatment. ML algorithms, including random forest, support vector machine, k-nearest neighbor, and convolutional neural networks (CNN), were trained on a subset of the data to predict treatment outcomes.RESULTSSignificant differences were observed in the percentages of males and females between the extraction and nonextraction groups and cephalometric variables between the two groups, which include maxillary depth, maxillary height, SN-palatal plane, facial angle, facial axis-Ricketts, FMA, total facial height, lower facial height, SNA, SNB, and SN-MP e ML algorithms examined consisted of CNN2, CNN1, and Random Forest, which demonstrated the highest accuracy rates (∼83%), while k-Nearest Neighbor had the lowest (∼73%). Key features influencing accuracy included crowding, SN-palatal plane, SNA, FMA, molar relation, and facial height measurements.CONCLUSIONSThe study's evaluation of AI algorithms showed that CNN2, CNN1, and random forest had an accuracy of approximately 83% in classifying extraction versus nonextraction cases. Notably, features such as U-crowding, L-crowding, SN-palatal plane, SNA, FMA, molar relation, total facial height, lower facial height, and facial axis-Ricketts were most influential in achieving accuracy rates comparable to traditional methods.

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