The halal food market is globally growing along with the increased risk of adulteration. We proposed an amplification-free and mix-to-read CRISPR-Cas12-based nucleic acid analytical strategy allowing rapid identification and analysis of pork components, thus enriching the toolbox for ensuring halal food authenticity. We designed and optimized guide RNA (gRNA) targeting the pork cytochrome b (Cyt b) gene. gRNA allowed specific identification of the target Cyt b gene from pork components followed by activation of Cas12 protein to abundantly cleave single-stranded DNA probes with terminally labeled fluorophore and quencher groups, thus turning on fluorescence. The presence of the pork Cyt b gene thus can be mix-and-read- and only-one-step-detected, which may indicate the risk of halal food adulteration. The method allowed specific discrimination of pork meat from beef, mutton, and chicken and yielded a detection limit of 2.7 ng/μL of total DNA from pork meat. The reliability of the method was tested using the following processed meat products: halal foods beef luncheon meat and spiced beef and non-halal foods sausage and dried pork slices. The CRISPR-Cas12-based nucleic acid test strategy is promising for rapid food authentication.

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The objective of the study was to explore the potential value of plasma indicators for identifying amnesic mild cognitive impairment (aMCI) and determine whether levels of plasma indicators are related to the performance of cognitive function and brain tissue volumes. In total, 155 participants (68 aMCI patients and 87 health controls) were recruited in the present cross-sectional study. The levels of plasma amyloid-β (Aβ) 40, Aβ42, total tau (t-tau), and neurofilament light (NFL) were measured using an ultrasensitive quantitative method. Machine learning algorithms were performed for establishing an optimal model of identifying aMCI. Compared with healthy controls, Aβ40 and Aβ42 levels were lower and NFL levels were higher in plasma of aMCI patients with an exception of t-tau levels. In aMCI patients, the higher plasma Aβ40 levels were correlated with the impaired episodic memory and negative correlations were observed between plasma t-tau levels and global cognitive function and gray matter (GM) volume. In addition, the higher plasma NFL levels were correlated with reduced hippocampus volume and total GM volume of the left inferior and middle temporal gyrus. An integrated model included clinical features, hippocampus volume, and plasma Aβ42 and NFL and had the highest accuracy for detecting aMCI patients (accuracy, 74.2%). We demonstrated that plasma Aβ40, Aβ42, t-tau, and NFL may be useful to identify aMCI and correlate with cognitive decline and brain atrophy. Among these plasma indicators, Aβ42 and NFL are more valuable as key members of a peripheral biomarker panel to detect aMCI.

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Background circular RNAs (circRNAs) are expressed abundantly in the brain and are implicated in the pathophysiology of neuropsychiatric disease. However, the potential clinical value of circRNAs in major depressive disorder (MDD) remains unclear. Methods RNA sequencing was conducted in whole-blood samples in a discovery set (7 highly homogeneous MDD patients and 7 matched healthy controls [HCs]). The differential expression of circRNAs was verified in an independent validation set. The interventional study was conducted to assess the potential effect of the antidepressive treatment on the circRNA expression. Findings in the validation set, compared with 52 HCs, significantly decreased circFKBP8 levels (Diff: -0.24; [95% CI -0.39 ~ -0.09]) and significantly elevated circMBNL1 levels (Diff: 0.37; [95% CI 0.09 ~ 0.64]) were observed in 53 MDD patients. The expression of circMBNL1 was negatively correlated with 24-item Hamilton Depression Scale (HAMD-24) scores in 53 MDD patients. A mediation model indicated that circMBNL1 affected HAMD-24 scores through a mediator, serum brain-derived neurotrophic factor. In 53 MDD patients, the amplitude of low-frequency fluctuations in the right orbital part middle frontal gyrus was positively correlated with circFKBP8 and circMBNL1 expression. Furthermore, the interventional study of 53 MDD patients demonstrated that antidepressive treatment partly increased circFKBP8 expression and the change in expression of circFKBP8 was predictive of further reduced HAMD-24 scores. Interpretation whole-blood circFKBP8 and circMBNL1 may be potential biomarkers for the diagnosis of MDD, respectively, and circFKBP8 may show great potential for the antidepressive treatment.

Non-coding RNAs (ncRNAs), including microRNAs, circular RNAs, and long non-coding RNAs, are important regulators of normal biological processes and their abnormal expression may be involved in the pathogenesis of human diseases including depression. Multiple studies have demonstrated a significantly increased or reduced ncRNAs expression in depressed patients compared with healthy subjects and that antidepressant therapy can alter the aberrant expression of ncRNAs in depressed patients. Although the existing evidence is important, it is also mixed and a comprehensive review to guide an effective clinical translation is lacking. Focused on human research, this review summarizes clinical findings of ncRNAs in depression, including those in brain tissues and peripheral samples. We outlined the characteristics and functions of ncRNAs and highlighted their performance in the diagnosis and treatment of depression. Although their precise roles in depression remain uncertain, ncRNAs have shown potential value as biomarkers for diagnosis and therapy in depressed patients.

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Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R 2 = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.