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Mitochondrial isolation has been practiced for decades, following procedures established by pioneers in the fields of molecular biology and biochemistry to study metabolic impairments and disease. Consistent mitochondrial quality is necessary to properly investigate mitochondrial physiology and bioenergetics; however, many different published isolation methods are available for researchers. Although different experimental strategies require different isolation methods, the basic principles and procedures are similar. This protocol details a method capable of extracting well-coupled mitochondria from a variety of tissue sources, including small animals and cells. The steps outlined include organ dissection, mitochondrial purification, protein quantification, and various quality control checks. The primary quality control metric used to identify high-quality mitochondria is the respiratory control ratio (RCR). The RCR is the ratio of the respiratory rate during oxidative phosphorylation to the rate in the absence of ADP. Alternative metrics are discussed. While high RCR values relative to their tissue source are obtained using this protocol, several steps can be optimized to suit the individual needs of researchers. This procedure is robust and has consistently resulted in isolated mitochondria with above-average RCR values across animal models and tissue sources.

Background: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival., Materials and Methods: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test., Results: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05)., Conclusion: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: To investigate the predictors of stroke-associated pneumonia (SAP) and poor functional outcome in patients with hyperacute cerebral infarction (HCI) by combining clinical factors, laboratory tests and neuroimaging features., Methods: We included 205 patients with HCI from November 2018 to December 2019. The diagnostic criterion for SAP was occurrence within 7 days of the onset of stroke. Poor outcome was defined as a functional outcome based on a 3-months MRS score >3. The relationship of demographic, laboratory and neuroimaging variables with SAP and poor outcome was investigated using univariate and multivariate analyses., Results: Fifty seven (27.8%) patients were diagnosed with SAP and 40 (19.5%) developed poor outcomes. A 2 DS 2 score (OR = 1.284; 95% CI: 1.048-1.574; P = 0.016), previous stroke (OR = 2.630; 95% CI: 1.122-6.163; P = 0.026), consciousness (OR = 2.945; 95% CI: 1.514-5.729; P < 0.001), brain atrophy (OR = 1.427; 95% CI: 1.040-1.959; P = 0.028), and core infarct volume (OR = 1.715; 95% CI: 1.163-2.528; P = 0.006) were independently associated with the occurrence of SAP. Therefore, we combined these variables into a new SAP prediction model with the C-statistic of 0.84 (95% CI: 0.78-0.90). Fasting plasma glucose (OR = 1.404; 95% CI: 1.202-1.640; P < 0.001), NIHSS score (OR = 1.088; 95% CI: 1.010-1.172; P = 0.026), previous stroke (OR = 4.333; 95% CI: 1.645-11.418; P = 0.003), SAP (OR = 3.420; 95% CI: 1.332-8.787; P = 0.011), basal ganglia-dilated perivascular spaces (BG-dPVS) (OR = 2.124; 95% CI: 1.313-3.436; P = 0.002), and core infarct volume (OR = 1.680; 95% CI: 1.166-2.420; P = 0.005) were independently associated with poor outcome. The C-statistic of the outcome model was 0.87 (95% CI: 0.81-0.94). Furthermore, the SAP model significantly improved discrimination and net benefit more than the A 2 DS 2 scale, with a C-statistic of 0.76 (95% CI: 0.69-0.83)., Conclusions: After the addition of neuroimaging features, the models exhibit good differentiation and calibration for the prediction of the occurrence of SAP and the development of poor outcomes in HCI patients. The SAP model could better predict the SAP, representing a helpful and valid tool to obtain a net benefit compared with the A 2 DS 2 scale., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yu, Xia, Tan, Xia, He, Sun, Wang, Song and Chen.)