Neuroscience & Neoplaisa Artificial Intelligence Research Group
66 subscribers
17 photos
5 files
25 links
Our main focus at NAIRG is to provide a better understanding of neuroscience, neoplaisa and AI and their applications in medicine.

πŸ“¨ Contact Us:

πŸ“§ nairesearchgroup@gmail.com
πŸ“§ nairg.research@gmail.com

@DendriteDreamer
Download Telegram
πŸ“‹ Title: The antimicrobial and healing effect of Scrophularia striata Boiss hydroalcoholic extract on first-and second-grade pressure wounds in patients with brain and spinal cord injury: a randomized clinical trial. Evidence-Based Complementary and Alternative Medicine.


πŸ“₯ Link: https://www.hindawi.com/journals/ecam/2022/8522937/

@NAIRG_Channel
8522937.pdf
790.2 KB
πŸ“‹ Title: The antimicrobial and healing effect of Scrophularia striata Boiss hydroalcoholic extract on first-and second-grade pressure wounds in patients with brain and spinal cord injury: a randomized clinical trial. Evidence-Based Complementary and Alternative Medicine.


πŸ“₯ Link: https://www.hindawi.com/journals/ecam/2022/8522937/

@NAIRG_Channel
#NAIRG_Publication🧠

Aluminum phosphide (ALP) is among the most significant causes of brain toxicity and death in many countries. Curcumin (CUR), a major turmeric component, is a potent protective agent against many diseases, including brain toxicity. This study aimed to examine the probable protection potential of nanomicelle curcumin (nanomicelle-CUR) and its underlying mechanism in a rat model of ALP-induced brain toxicity.

πŸ“– PDF file of this article is available in NAIRG channel

πŸ“‹ Title: How can nanomicelle-curcumin modulate aluminum phosphide-induced neurotoxicity?: Role of SIRT1/FOXO3 signaling pathway


πŸ“₯ Link: https://www.aimspress.com/article/doi/10.3934/Neuroscience.2023005

πŸ“Ž doi10.3934/Neuroscience.2023005

πŸ“Ž Journal name: AIMS Neuroscience
2023, Volume 10, Issue 1: 56-74.


πŸ“Ž Authors:
Milad Khodavysi
Nejat Kheiripour
Hassan Ghasemi
Sara Soleimani-Asl
Ali Fathi Jouzdani
Mohammadmahdi Sabahi
Zahra Ganji
Zahra Azizi
Akram Ranjbar


πŸ“Ž Result: The study compared the MDA, TTG, TAC, and SOD levels of the ALP and CUR groups in brain tissue. MDA levels were significantly increased in the ALP group compared to the control group, while TTG levels were decreased in the ALP group. The ALP + CUR and ALP + nanomicelle-CUR groups had no significant difference in MDA levels compared to the control group. TAC levels were also significantly decreased in the ALP group compared to the control group, while the tissue TAC levels increased in the ALP + CUR and ALP + nanomicelle-CUR groups. The brain SOD level was significantly decreased in the ALP group compared to the control group, and the levels in the ALP + CUR and ALP + nanomicelle-CUR groups were not significantly different. The CUR and nanomicelle-CUR groups had no significant difference compared to the control group. The results suggest that the ALP group may have a higher SOD level than the CUR and nanomicelle-CUR groups.

@NAIRG_Channel
How_can_nanomicelle-curcumin_modulate_aluminum_pho.pdf
1.3 MB
πŸ“‹ Title: How can nanomicelle-curcumin modulate aluminum phosphide-induced neurotoxicity?: Role of SIRT1/FOXO3 signaling pathway

πŸ“₯ Link: https://www.aimspress.com/article/doi/10.3934/Neuroscience.2023005

@NAIRG_Channel
πŸ“‹ Title: How can nanomicelle-curcumin modulate aluminum phosphide-induced neurotoxicity?: Role of SIRT1/FOXO3 signaling pathway

πŸ“₯ Link: https://www.aimspress.com/article/doi/10.3934/Neuroscience.2023005

@NAIRG_Channel
#NAIRG_Publication🧠

🩻 We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs)


πŸ“– PDF file of this article is available in NAIRG channel

πŸ“‹Title: Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems


πŸ“₯ Link :
https://www.mdpi.com/2075-4418/13/10/1691

πŸ“Ž doi:
https://doi.org/10.3390/diagnostics13101691

πŸ“Ž Journal name:
Diagnostics 2023, 13(10), 1691;

πŸ“Ž Authors:
Mahdi Hosseinzadeh
Arman Gorji
Ali Fathi Jouzdani
Seyed Masoud Rezaeijo
Arman Rahmim
Mohammad R. Salmanpour

πŸ“Ž Result:
For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 Β± 3% and 65 Β± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 Β± 1% and 56 Β± 2%, respectively. For sole CFs, a higher performance of 77 Β± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 Β± 7%, with a hold-out testing performance of 59 Β± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF


@NAIRG_Channel
diagnostics-13-01691-v2.pdf
1.6 MB
πŸ“‹ Title :
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems

πŸ“₯ Link:
https://www.mdpi.com/2075-4418/13/10/1691#

@NAIRG_Channel
πŸ“‹ Title :
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems


πŸ“₯ Link:
https://www.mdpi.com/2075-4418/13/10/1691#

@NAIRG_Channel
πŸŽŠπŸŽ‰Happy New Year πŸŽ‰πŸŽŠ
❀4πŸ”₯1
#NAIRG_Publication🧠
#Conferences

🩻Although the use of hand-crafted radiomics features (RF) has shown significant promise to improve diagnostic, prognostic, and treatment response assessments, employing deep features (DF) extracted from deep learning (DL) algorithms merits significant investigation in lung cancer disease. We aimed to employ RFs vs. DFs to predict recurrence-free survival (RFS) and overall survival (OS) using machine learning (ML) techniques in lung cancer.


πŸ“‹ Title:

PET-CT Fusion Based Outcome Prediction in Lung Cancer using Deep and Handcrafted Radiomics Features and Machine Learning


πŸ“₯ Link :
https://jnm.snmjournals.org/content/64/supplement_1/P1196.abstract

πŸ“Ž Journal name:
Journal of Nuclear Medicine June 2023, 64 (supplement 1) P1196;

πŸ“Ž Authors:

Arman Gorji
Ali Fathi Jouzdani
Nima Sanati
Mahdi Hosseinzadeh
Ali Mahboubisarighieh
Seyed Masoud Rezaeijo
Mehdi Maghsudi
Sara Moore
Leung Bonnie
Carlos Uribe
Cheryl Ho
Arman Rahmim
Mohammad R Salmanpour

πŸ“Ž Result:
In RF framework, SR+ANOVA+MLP and VSMWLS+ANOVA+MLP resulted in MAEs of 506.7Β± 6.1 and 264.0Β±6.8 days to predict approximate death (OS) and approximate recurrence time (RFS) while we arrived at the lowest MSEs of 430.3Β±10.2 and 250.5Β±10.2 using DF supplied to LP_SR+ANOVA+MLP and CT+MRMR+ HistGB, respectively. In predictions of time to events such as OS and RFS, Wavelet+PCA+RadSF and NSCT_SR+PCA+RadSF linked with DFs obtained c-indexes of 0.59Β±0.03 and 0.61Β±0.01, while the highest c-indexes of 0.64 Β±0.03 and 0.61Β±0.05 were obtained by RFs followed by CBF+PCA+RadSF and VSMWLS+PCA+RadSF, respectively.

@NAIRG_Channel
#Conferences

πŸ“‹ Title :
PET-CT Fusion Based Outcome Prediction in Lung Cancer using Deep and Handcrafted Radiomics Features and Machine Learning

πŸ“₯ Link:
https://jnm.snmjournals.org/content/64/supplement_1/P1196.abstract

@NAIRG_Channel
#NAIRG_Publication🧠

πŸ“ Paraquat (PQ) is a nonselective herbicide that induces oxidative reactions and multiple-organ failure on exposure. Crocin, a carotenoid obtained from saffron, has demonstrated many therapeutic effects against neural conditions because of its antioxidant properties. In this study, 30 male Wistar rats were divided into 6 groups to evaluate the protective effects of crocin and crocin-loaded niosomes (NC) against PQ in the brain. The levels of total antioxidant capacity (TAC), lipid peroxidation (LPO), total thiol groups (TTG), superoxide dismutase (SOD), and catalase (CAT) activity were measured as the markers of redox status.

πŸ“– PDF file of this article is available in NAIRG channel

πŸ“‹ Title: Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats



πŸ“₯ Link :
https://www.degruyter.com/document/doi/10.1515/biol-2022-0468/html

πŸ“Ž doi
https://doi.org/10.1515/biol-2022-0468

πŸ“Ž Journal name:
Published by De Gruyter Open Access September 14, 2022

πŸ“Ž Authors:
Afsoon Daneshvar 
Ali Fathi Jouzdani 
Farzin Firozian 
Sara Soleimani Asl 
Mojdeh Mohammadi and Akram Ranjbar 

πŸ“Ž Result:
1)The LPO level revealed that PQ induced a significant increase in LPO in the brain compared to control (P < 0.0001).

2)Treatments with crocin or NC resulted in minor differences in TAC levels compared with control, which was not significant.

3) crocin had a significant effect on TTG compared to control (P < 0.01).

4) The assessment of CAT activity revealed a significant difference between the control and all other groups

5)The assessment of SOD activity revealed a significant difference between the control and PQ (P < 0.01) and NC (P < 0.001) groups.

6)Histological evaluations revealed that the PQ-induced rats displayed a significant decrease in cell density in the CA1 region of the hippocampus compared to normal , crocin and NC (P < 0.001) groups. Treatments with crocin (P < 0.01) and NC (P < 0.001) plus PQ led to an enhancement in cell density of CA1 compared to the PQ group. Although treatment with crocin or NC showed no difference in cell count, the bulk and nanoformulations of crocin presented different effect levels in PQ-toxicity rats . No difference was seen between the crocin and NC groups compared to the control group

@NAIRG_Channel
10.1515_biol-2022-0468.pdf
2 MB
πŸ“‹ Title:
Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats

πŸ“Ž doi: https://doi.org/10.1515/biol-2022-0468

@NAIRG_Channel
πŸ“‹ Title:
Neuroprotective effects of crocin and crocin-loaded niosomes against the paraquat-induced oxidative brain damage in rats

πŸ“Ž doi: https://doi.org/10.1515/biol-2022-0468

@NAIRG_Channel
#NAIRG_Publication🧠
#Conferences

🩻Although many studies have focused on handcrafted radiomics features (RF), a much more indepth study, as follows in this study, is necessary to explore usage of deep RFs extracted from deep learning (DL) algorithms to assess survival analysis. We enrolled 215 lung cancer patients with PET, CT, and clinical data from The Cancer Imaging Archive and the Vancouver General Hospital. This study aims to assess 3 approaches, including comparison of i) PET vs. CT images; ii) different region of interests (ROI) used for deep RF extraction; and iii) deep vs. handcrafted RFs in prediction of overall survival (OS; regression task to predict exact time-to-death and survival probability) by hybrid machine learning systems (HMLS), including Analysis of Variance (ANOVA) and principal component analysis (PCA) linked with regression algorithms (RA).

πŸ“‹ Title:


Region-of-Interest and Handcrafted vs. Deep Radiomics Feature Comparisons for Survival Outcome Prediction: Application to Lung PET/CT Imaging

πŸ“₯ Link :

https://ieeexplore.ieee.org/document/10338394

πŸ“Ž doi:
https://doi.org/10.1109/NSSMICRTSD49126.2023.10338394

πŸ“ŽConference:
2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD)


πŸ“Ž Authors:
A Gorji
M Hosseinzadeh
A Fathi Jouzdani
N Sanati
F Yousefi Rizi
S Moore
B Leung
C Ho
I Shiri
H Zaidi
A Rahmim
MR Salmanpour

πŸ“Ž Result:
In OS prediction, i) PET-based HMLSs outperformed CT-based HMLSs, ii) deep RFs extracted from the cropped images outperformed deep RFs extracted from other ROIs, receiving the highest mean absolute error (MAE) of 595Β±126 [range 36-5596 days] by ANOVA+support vector regression, and iii) deep RFs significantly outperformed handcrafted RFs. In survival probability prediction, i) PET-based HMLSs outperformed CT based HMLSs, ii) PET-based deep RFs extracted from the entire image outperformed deep RFs extracted from other ROIs, having the highest c-index of 0.65Β±0.05 through PCA+Random Survival Forest, and iii) deep RFs outperformed handcrafted RFs

@NAIRG_Channel
#Conferences

πŸ“‹ Title:
Region-of-Interest and Handcrafted vs. Deep Radiomics Feature Comparisons for Survival Outcome Prediction: Application to Lung PET/CT Imaging

πŸ“Ž doi:

https://doi.org/10.1109/NSSMICRTSD49126.2023.10338394


@NAIRG_Channel
πŸ‘2