Ferritin, a key regulator of iron homeostasis in macrophages, has been reported to confer host defenses against Mycobacterium tuberculosis (Mtb) infection. Nuclear receptor coactivator 4 (NCOA4) was recently identified as a cargo receptor in ferritin degradation. Here, we show that Mtb infection enhanced NCOA4-mediated ferritin degradation in macrophages, which in turn increased the bioavailability of iron to intracellular Mtb and therefore promoted bacterial growth. Of clinical relevance, the upregulation of FTH1 in macrophages was associated with tuberculosis (TB) disease progression in humans. Mechanistically, Mtb infection enhanced NCOA4-mediated ferritin degradation through p38/AKT1- and TRIM21-mediated proteasomal degradation of HERC2, an E3 ligase of NCOA4. Finally, we confirmed that NCOA4 deficiency in myeloid cells expedites the clearance of Mtb infection in a murine model. Together, our findings revealed a strategy by which Mtb hijacks host ferritin metabolism for its own intracellular survival. Therefore, this represents a potential target for host-directed therapy against tuberculosis.

Background Small cell lung cancer (SCLC) is a highly malignant cancer characterized by metastasis and an extremely poor prognosis. Although combined chemoimmunotherapy improves the prognosis of extensive-stage (ES)-SCLC, the survival benefits remain limited. Furthermore, no reliable biomarker is available so far to predict the treatment outcomes for chemoimmunotherapy. Methods This retrospective study included patients with ES-SCLC treated with first-line combined atezolizumab or durvalumab with standard chemotherapy between Janauray 1, 2019 and October 1, 2022 at five medical centers in China as the chemoimmunotherapy group. The patients were divided into one training cohort and two independent external validation cohorts. Additionally, we created a control group of ES-SCLC who was treated with first-line standard chemotherapy alone. The Radiomics Score was derived using machine learning algorithms based on the radiomics features extracted in the regions of interest delineated on the chest CT obtained before treatment. Cox proportional hazards regression analysis was performed to identify clinical features associated with therapeutic efficacy. The log-rank test, time-dependent receiver operating characteristic curve, and Concordance Index (C-index) were used to assess the effectiveness of the models. Results A total of 341 patients (mean age, 62±8.7 years) were included in our study. After a median follow-up time of 12.1 months, the median progression-free survival (mPFS) was 7.1 (95% CI 6.6 to 7.7) months, whereas the median overall survival (mOS) was not reached. The TNM stage, Eastern Cooperative Oncology Group performance status, and Lung Immune Prognostic Index showed significant correlations with PFS. We proposed a predictive model based on eight radiomics features to determine the risk of chemoimmunotherapy resistance among patients with SCLC (validation set 1: mPFS, 12.0 m vs 5.0 m, C-index=0.634; validation set 2: mPFS, 10.8 m vs 6.1 m, C-index=0.617). By incorporating the clinical features associated with PFS into the radiomics model, the predictive efficacy was substantially improved. Consequently, the low-progression-risk group exhibited a significantly longer mPFS than the high-progression-risk group in both validation set 1 (mPFS, 12.8 m vs 4.5 m, HR=0.40, p=0.028) and validation set 2 (mPFS, 9.2 m vs 4.6 m, HR=0.30, p=0.012). External validation set 1 and set 2 yielded the highest 6-month area under the curve and C-index of 0.852 and 0.820, respectively. Importantly, the integrated prediction model also exhibited considerable differentiation power for survival outcomes. The HR for OS derived from the low-progression-risk and high-progression-risk groups was 0.28 (95% CI 0.17 to 0.48) in all patients and 0.20 (95% CI 0.08 to 0.54) in validation set. By contrast, no significant differences were observed in PFS and OS, between high-progression-risk patients receiving chemoimmunotherapy and the chemotherapy cohort (mPFS, 5.5 m vs 5.9 m, HR=0.90, p=0.547; mOS, 14.5 m vs 13.7 m, HR=0.97, p=0.910). Conclusions The integrated clinical and radiomics model can predict the treatment outcomes in patients with ES-SCLC receiving chemoimmunotherapy, rendering a convenient and low-cost prognostic model for decision-making regarding patient management.

Background: This study aimed to develop and validate a model for mortality risk stratification of intensive care unit (ICU) patients with acute kidney injury (AKI) using the machine learning technique. Methods: Eligible data were extracted from the Medical Information Mart for Intensive Care (MIMIC-III) database. Calibration, discrimination, and risk classification for mortality prediction were evaluated using conventional scoring systems and the new algorithm. A 10-fold cross-validation was performed. The predictive models were externally validated using the eICU database and also patients treated at the Second People’s Hospital of Shenzhen between January 2015 to October 2018. Results: For the new model, the areas under the receiver operating characteristic curves (AUROCs) for mortality during hospitalization and at 28 and 90 days after discharge were 0.91, 0.87, and 0.87, respectively, which were higher than for the Simplified Acute Physiology Score (SAPS II) and Sequential Organ Failure Assessment (SOFA). For external validation, the AUROC was 0.82 for in-hospital mortality, higher than SOFA, SAPS II, and Acute Physiology and Chronic Health Evaluation (APACHE) IV in the eICU database, but for the 28- and 90-day mortality, the new model had AUROCs (0.79 and 0.80, respectively) similar to that of SAPS II in the SZ2 database. The reclassification indexes were superior for the new model compared with the conventional scoring systems. Conclusions: The new risk stratification model shows high performance in predicting mortality in ICU patients with AKI.

Volume rendering produces informative two-dimensional (2D) images from a 3-dimensional (3D) volume. It highlights the region of interest and facilitates a good comprehension of the entire data set. However, volume rendering faces a few challenges. First, a high-dimensional transfer function is usually required to differentiate the target from its neighboring objects with subtle variance. Unfortunately, designing such a transfer function is a strenuously trial-and-error process. Second, manipulating/visualizing a 3D volume with a traditional 2D input/output device suffers dimensional limitations. To address all the challenges, we design NUI-VR $$^2$$2 , a natural user interface-enabled volume rendering system in the virtual reality space. NUI-VR $$^2$$2 marries volume rendering and interactive image segmentation. It transforms the original volume into a probability map with image segmentation. A simple linear transfer function will highlight the target well in the probability map. More importantly, we set the entire image segmentation and volume rendering pipeline in an immersive virtual reality environment with a natural user interface. NUI-VR $$^2$$2 eliminates the dimensional limitations in manipulating and perceiving 3D volumes and dramatically improves the user experience.

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale. Herein, we used the BigBrain dataset to generate area and point-wise thickness measurements for all layers of the cerebellar cortex and for each lobule in particular. We found that the overall surface area of the cerebellar granular layer (including Purkinje cells) was 1,732 cm² and the molecular layer was 1,945 cm². The average thickness of the granular layer is 0.88 mm (± 0.83) and that of the molecular layer is 0.32 mm (± 0.08). The cerebellum (both granular and molecular layers) is thicker at the depth of the sulci and thinner at the crowns of the gyri. Globally, the granular layer is thicker in the lateral-posterior-inferior region than the medial-superior regions. The characterization of individual layers in the cerebellum achieved herein represents a stepping-stone for investigations interrelating structural and functional connectivity with cerebellar architectonics using neuroimaging, which is a matter of considerable relevance in basic and clinical neuroscience. Furthermore, these data provide templates for the construction of cerebellar topographic maps and the precise localization of structural and functional alterations in diseases affecting the cerebellum.