Psychobiological regulation of plasma and saliva GDF15 dynamics in health and mitochondrial diseases.

GDF15 (growth differentiation factor 15) is a marker of cellular energetic stress linked to physical-mental illness, aging, and mortality. However, questions remain about its dynamic properties and measurability in human biofluids other than blood. Here, we examine the natural dynamics and psychobiological regulation of plasma and saliva GDF15 in four human studies representing 4,749 samples from 188 individuals. We show that GDF15 protein is detectable in saliva (8% of plasma concentration), likely produced by salivary glands secretory duct cells. Using a brief laboratory socio-evaluative stressor paradigm, we find that psychosocial stress increases plasma (+3.5-5.9%) and saliva GDF15 (+43%) with distinct kinetics, within minutes. Moreover, saliva GDF15 exhibits a robust awakening response, declining by ~40-89% within 30-45 minutes from its peak level at the time of waking up. Clinically, individuals with genetic mitochondrial OxPhos diseases show elevated baseline plasma and saliva GDF15, and post-stress GDF15 levels in both biofluids correlate with multi-system disease severity, exercise intolerance, and the subjective experience of fatigue. Taken together, our data establish that saliva GDF15 is dynamic, sensitive to psychological states, a clinically relevant endocrine marker of mitochondrial diseases. These findings also point to a shared psychobiological pathway integrating metabolic and mental stress.

Analysis of fatty acid composition and sensitivity to dietary n-3 PUFA intervention of mouse n-3 PUFA-enriched tissues/organs.

PURPOSE: n-3 polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA; C22:6 n3) and eicosapentaenoic acid (EPA; C20:5 n3), are of concern for their health-promoting effects such as anti-inflammatory, but the tissue selectivity for n-3 PUFA (i.e., which tissues and organs are rich in n-3 PUFA) is still not well known. In addition, it is unclear which tissues and organs are more sensitive to n-3 PUFA intervention. These unresolved issues have greatly hindered the exploring of the health benefits of n-3 PUFA. METHODS: Twenty-four 7-week-old male C57BL/6 J mice were assigned to the control, fish oil, DHA, and EPA groups. The last three groups were given a 4-week oral intervention of fatty acids in ethyl ester (400 mg/kg bw). The fatty acid profiles in 27 compartments were determined by gas chromatography. RESULTS: The proportion of long-chain n-3 PUFA (the total relative percentage of EPA, DPA n3, and DHA) was analyzed. Eight tissues and organs, including the brain (cerebral cortex, hippocampus, hypothalamus) and peripheral organs (tongue, quadriceps, gastrocnemius, kidney, and heart) were determined as being n-3 PUFA-enriched tissues and organs, owing to their high n-3 PUFA levels. The highest n-3 PUFA content was observed in the tongue for the first time. Notably, the content of linoleic acid (LA; C18:2 n6c) in peripheral organs was observed to be relatively high compared with that in the brain. Interestingly, the proportions of EPA in the kidney, heart, quadriceps, gastrocnemius, and tongue increased more markedly after the EPA intervention than after the DHA or fish oil intervention. As expected, the levels of proinflammatory arachidonic acid (AA; C20:4 n6) in the kidney, quadriceps, and tongue were markedly decreased after the three dietary interventions. CONCLUSION: Peripheral tissues and organs, including the tongue, quadriceps, gastrocnemius, kidney, and heart, besides the brain, showed obvious tissue selectivity for n-3 PUFA. In the whole body of mice, the tongue exhibits the strongest preference for n-3 PUFA, with the highest proportion of n-3 PUFA. Moreover, these peripheral tissues and organs, especially the kidney, are more sensitive to dietary EPA administration in comparison with the brain.

Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer.

BACKGROUND: The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). MATERIALS AND METHODS: Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student's t-test or Mann-Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. RESULTS: Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10-3 mm2/s vs. 1.193 ± 0.337 ×10-3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. CONCLUSION: APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.

Reduced field-of-view DWI­derived clinical-radiomics model for the prediction of stage in cervical cancer.

BACKGROUND: Pretreatment prediction of stage in patients with cervical cancer (CC) is vital for tailoring treatment strategy. This study aimed to explore the feasibility of a model combining reduced field-of-view (rFOV) diffusion-weighted imaging (DWI)-derived radiomics with clinical features in staging CC. METHODS: Patients with pathologically proven CC were enrolled in this retrospective study. The rFOV DWI with b values of 0 and 800 s/mm2 was acquired and the clinical characteristics of each patient were collected. Radiomics features were extracted from the apparent diffusion coefficient maps and key features were selected subsequently. A clinical-radiomics model combining radiomics with clinical features was constructed. The receiver operating characteristic curve was introduced to evaluate the predictive efficacy of the model, followed by comparisons with the MR-based subjective stage assessment (radiological model). RESULTS: Ninety-four patients were analyzed and divided into training (n = 61) and testing (n = 33) cohorts. In the training cohort, the area under the curve (AUC) of clinical-radiomics model (AUC = 0.877) for staging CC was similar to that of radiomics model (AUC = 0.867), but significantly higher than that of clinical model (AUC = 0.673). In the testing cohort, the clinical-radiomics model yielded the highest predictive performance (AUC = 0.887) of staging CC, even without a statistically significant difference when compared with the clinical model (AUC = 0.793), radiomics model (AUC = 0.846), or radiological model (AUC = 0.823). CONCLUSIONS: The rFOV DWI-derived clinical-radiomics model has the potential for staging CC, thereby facilitating clinical decision-making.

Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer.

BACKGROUND: The aim was to evaluate the feasibility of radiomics features based on diffusion-weighted imaging (DWI) at high b-values for grading bladder cancer and to compare the possible advantages of high-b-value DWI over the standard b-value DWI. METHODS: Seventy-four participants with bladder cancer were included in this study. DWI sequences using a 3 T MRI with b-values of 1000, 1700, and 3000 s/mm2 were acquired, and the corresponding ADC maps were generated, followed with feature extraction. Patients were randomly divided into training and testing cohorts with a ratio of 8:2. The radiomics features acquired from the ADC1000, ADC1700, and ADC3000 maps were compared between low- and high-grade bladder cancers by using the Wilcox analysis, and only the radiomics features with significant differences were selected. The least absolute shrinkage and selection operator method and a logistic regression were performed for the feature selection and establishing the radiomics model. A receiver operating characteristic (ROC) analysis was conducted to assess the diagnostic performance of the radiomics models. RESULTS: In the training cohorts, the AUCs of the ADC1000, ADC1700, and ADC3000 model for discriminating between low- from high-grade bladder cancer were 0.901, 0.920, and 0.901, respectively. In the testing cohorts, the AUCs of ADC1000, ADC1700, and ADC3000 were 0.582, 0.745, and 0.745, respectively. CONCLUSIONS: The radiomics features extracted from the ADC1700 maps could improve the diagnostic accuracy over those extracted from the conventional ADC1000 maps.

Demographic, signs and symptoms, imaging characteristics of 2126 patients with COVID-19 pneumonia in the whole quarantine of Wuhan, China.

OBJECTIVE: The accurate knowledge of demographic, signs and symptoms, imaging characteristics of coronavirus disease 2019 (COVID-19) is essential for the accurate management of these patients. However, the claims between the previous papers are not always consistent and may even contradict each other, for example, some claims the virus infects more men than women in Wuhan. In this large-scale cohort study, we aimed to update the demographic, signs and symptoms, imaging characteristics of patients with COVID-19 in the whole quarantine of Wuhan, China. METHODS: A cohort of 2126 patients with a diagnosis of COVID-19 pneumonia (confirmed by real-time reverse transcriptase-polymerase chain reaction, RT-PCR) who were admitted to one hospital in Wuhan were retrospectively enrolled. Data were collected between January 13, 2020, and April 8, 2020, the end of Wuhan quarantine. Demographic, signs and symptoms, imaging characteristics were analyzed. CT imaging characteristics associated with respiratory failure or death were identified. RESULTS: Of the 2126 patients with COVID-19, 1051 (49.44%) were men and 1075 (50.56%) were women, 1933 (90.92%) have fever and 1328 (62.46%) have dry cough. The mean age was 57.43 years of age (range 1-95). The CT imaging findings were bilateral pneumonia (1883[88.57%]), unilateral pneumonia (243[11.43%]), ground-glass opacity (GGO) or consolidation (1175[55.27%]), pleural effusion (69[3.25%]). Patients with respiratory failure or death were more likely to have pleural effusion on CT than patients without respiratory failure or death (p < 0.05). CONCLUSION: Men and women have been infected by SARS-CoV-2 in roughly equal numbers. Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death.