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General General

Peripheral nerve injury elicits microstructural and neurochemical changes in the striatum and substantia nigra of a DYT-TOR1A mouse model with dystonia-like movements.

In Neurobiology of disease

The relationship between genotype and phenotype in DYT-TOR1A dystonia as well as the associated motor circuit alterations are still insufficiently understood. DYT-TOR1A dystonia has a remarkably reduced penetrance of 20-30%, which has led to the second-hit hypothesis emphasizing an important role of extragenetic factors in the symptomatogenesis of TOR1A mutation carriers. To analyze whether recovery from a peripheral nerve injury can trigger a dystonic phenotype in asymptomatic hΔGAG3 mice, which overexpress human mutated TorsinA, a sciatic nerve crush was applied. An observer-based scoring system as well as an unbiased deep-learning based characterization of the phenotype showed that recovery from a sciatic nerve crush leads to significantly more dystonia-like movements in hΔGAG3 animals compared to wt control animals, which persisted over the entire monitored period of 12 weeks. In the basal ganglia, the analysis of medium spiny neurons revealed a significantly reduced number of dendrites, dendrite length and number of spines in the naïve and nerve-crushed hΔGAG3 mice compared to both wt control groups indicative of an endophenotypical trait. The volume of striatal calretinin+ interneurons showed alterations in hΔGAG3 mice compared to the wt groups. Nerve injury related changes were found for striatal ChAT+, parvalbumin+ and nNOS+ interneurons in both genotypes. The dopaminergic neurons of the substantia nigra remained unchanged in number across all groups, however, the cell volume was significantly increased in nerve-crushed hΔGAG3 mice compared to naïve hΔGAG3 mice and wt littermates. Moreover, in vivo microdialysis showed an increase of dopamine and its metabolites in the striatum comparing nerve-crushed hΔGAG3 mice to all other groups. The induction of a dystonia-like phenotype in genetically predisposed DYT-TOR1A mice highlights the importance of extragenetic factors in the symptomatogenesis of DYT-TOR1A dystonia. Our experimental approach allowed us to dissect microstructural and neurochemical abnormalities in the basal ganglia, which either reflected a genetic predisposition or endophenotype in DYT-TOR1A mice or a correlate of the induced dystonic phenotype. In particular, neurochemical and morphological changes of the nigrostriatal dopaminergic system were correlated with symptomatogenesis.

Rauschenberger Lisa, Krenig Esther-Marie, Stengl Alea, Knorr Susanne, Harder Tristan H, Steeg Felix, Friedrich Maximilian U, Grundmann-Hauser Kathrin, Volkmann Jens, Ip Chi Wang

2023-Feb-28

Basal ganglia, DYT-TOR1A, Dopamine, Dystonia, Medium spiny neurons, Second-hit hypothesis

General General

The relationship between depressive symptoms and cognitive function in Alzheimer's disease: The mediating effect of amygdala functional connectivity and radiomic features.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : Depressive symptoms are common in Alzheimer's disease (AD) and are associated with cognitive function. Amygdala functional connectivity (FC) and radiomic features related to depression and cognition. However, studies have yet to explore the neural mechanisms underlying these associations.

METHODS : We enrolled eighty-two AD patients with depressive symptoms (ADD) and 85 healthy controls (HCs) in this study. We compared amygdala FC using the seed-based approach between ADD patients and HCs. The least absolute shrinkage and selection operator (LASSO) was used to select amygdala radiomic features. A support vector machine (SVM) model was constructed based on the identified radiomic features to distinguish ADD from HCs. We used mediation analyses to explore the mediating effects of amygdala radiomic features and amygdala FC on cognition.

RESULTS : We found that ADD patients showed decreased amygdala FC with posterior cingulate cortex, middle frontal gyrus (MFG), and parahippocampal gyrus involved in the default mode network compared to HCs. The area under the receiver operating characteristic curve (AUC) of the amygdala radiomic model was 0.95 for ADD patients and HCs. Notably, the mediation model demonstrated that amygdala FC with the MFG and amygdala-based radiomic features mediated the relationship between depressive symptoms and cognitive function in AD.

LIMITATIONS : This study is a cross-sectional study and lacks longitudinal data.

CONCLUSION : Our findings may not only expand existing biological knowledge of the relationship between cognition and depressive symptoms in AD from the perspective of brain function and structure but also may ultimately provide potential targets for personalized treatment strategies.

Du Yang, Yu Jie, Liu Manhua, Qiu Qi, Fang Yuan, Zhao Lu, Wei Wenjing, Wang Jinghua, Lin Xiang, Yan Feng, Li Xia

2023-Feb-28

“Alzheimers disease”, Amygdala functional connectivity, Depressive symptoms, Mediation analysis, Radiomic

General General

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach.

In Environmental pollution (Barking, Essex : 1987)

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Aniza Ria, Chen Wei-Hsin, Pétrissans Anelie, Hoang Anh Tuan, Ashokkumar Veeramuthu, Petrissans Mathieu

2023-Feb-28

Algal biowaste, Artificial intelligence (AI), Bioenergy, Lignocellulosic biowaste, Remediation, Valorization

Public Health Public Health

Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection.

In EBioMedicine

BACKGROUND : Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall and workload for simulated AI-radiologist reading.

METHODS : External validation of a commercially-available AI algorithm in a retrospective cohort of 108,970 consecutive mammograms from a population-based screening program, with ascertained outcomes (including interval cancers by registry linkage). Area under the ROC curve (AUC), sensitivity and specificity for AI were compared with radiologists who interpreted the screens in practice. CDR and recall were estimated for simulated AI-radiologist reading (with arbitration) and compared with program metrics.

FINDINGS : The AUC for AI was 0.83 compared with 0.93 for radiologists. At a prospective threshold, sensitivity for AI (0.67; 95% CI: 0.64-0.70) was comparable to radiologists (0.68; 95% CI: 0.66-0.71) with lower specificity (0.81 [95% CI: 0.81-0.81] versus 0.97 [95% CI: 0.97-0.97]). Recall rate for AI-radiologist reading (3.14%) was significantly lower than for the BSWA program (3.38%) (-0.25%; 95% CI: -0.31 to -0.18; P < 0.001). CDR was also lower (6.37 versus 6.97 per 1000) (-0.61; 95% CI: -0.77 to -0.44; P < 0.001); however, AI detected interval cancers that were not found by radiologists (0.72 per 1000; 95% CI: 0.57-0.90). AI-radiologist reading increased arbitration but decreased overall screen-reading volume by 41.4% (95% CI: 41.2-41.6).

INTERPRETATION : Replacement of one radiologist by AI (with arbitration) resulted in lower recall and overall screen-reading volume. There was a small reduction in CDR for AI-radiologist reading. AI detected interval cases that were not identified by radiologists, suggesting potentially higher CDR if radiologists were unblinded to AI findings. These results indicate AI's potential role as a screen-reader of mammograms, but prospective trials are required to determine whether CDR could improve if AI detection was actioned in double-reading with arbitration.

FUNDING : National Breast Cancer Foundation (NBCF), National Health and Medical Research Council (NHMRC).

Marinovich M Luke, Wylie Elizabeth, Lotter William, Lund Helen, Waddell Andrew, Madeley Carolyn, Pereira Gavin, Houssami Nehmat

2023-Feb-28

Artificial intelligence, Breast neoplasms, Diagnostic screening programs, Sensitivity and specificity

General General

Real-to-bin conversion for protein residue distances.

In Computational biology and chemistry

Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. Real values than bin probabilities could more naturally represent inter-residue distances, while the latter, via spline curves more naturally helps obtain differentiable objective functions than the former. Consequently, PSP methods that exploit predicted binned distances perform better than those that exploit predicted real-valued distances. To leverage the advantage of bin probabilities in getting differentiable objective functions, in this work, we propose techniques to convert real-valued distances into distance bin probabilities. Using standard benchmark proteins, we then show that our real-to-bin converted distances help PSP methods obtain three-dimensional structures with 4%-16% better root mean squared deviation (RMSD), template modeling score (TM-Score), and global distance test (GDT) values than existing similar PSP methods. Our proposed PSP method is named real to bin (R2B) inter-residue distance predictor, and its code is available from https://gitlab.com/mahnewton/r2b.

Rahman Julia, Newton M A Hakim, Hasan Md Al Mehedi, Sattar Abdul

2023-Feb-25

Binned distance, Protein Structure Prediction, Real-valued distance, Residue-residue distance

Surgery Surgery

High serum riboflavin is associated with the risk of sporadic colorectal cancer.

In Cancer epidemiology

BACKGROUND : Experimental results indicate that riboflavin is involved in tumorigenesis. Data regarding the relationship between riboflavin and colorectal cancer (CRC) are limited, and findings vary between observational studies.

DESIGN : This was a case-control retrospective study.

OBJECTIVE : This study aimed to evaluate the associations between serum riboflavin level and sporadic CRC risk.

METHODS : In total, 389 participants were enrolled in this study - including 83 CRC patients without family history and 306 healthy controls - between January 2020 and March 2021 at the Department of Colorectal Surgery and Endoscope Center at Xinhua Hospital, Shanghai Jiao Tong University School of Medicine. Age, sex, body mass index, history of polyps, disease conditions (e.g., diabetes), medications, and eight other vitamins were used as confounding factors. Adjusted smoothing spline plots, subgroup analysis, and multivariate logistic regression analysis were conducted to estimate the relative risk between serum riboflavin levels and sporadic CRC risk. After fully adjusting for the confounding factors, an increased risk of colorectal cancer was suggested for individuals with higher levels of serum riboflavin (OR = 1.08 (1.01, 1.15), p = 0.03) in a dose-response relationship.

CONCLUSIONS : Our results support the hypothesis that higher levels of riboflavin may play a role in facilitating colorectal carcinogenesis. The finding of high levels of circulating riboflavin in patients with CRC warrants further investigation.

Ma Yanhui, Huangfu Yuchan, Deng Lin, Wang Ping, Shen Lisong, Zhou Yunlan

2023-Feb-28

Circulating riboflavin, Nutrition, Risk factor, Sporadic colorectal cancer, Vitamin B2