Drp1-Zip1 Interaction Regulates Mitochondrial Quality Surveillance System.

Mitophagy, a mitochondrial quality control process for eliminating dysfunctional mitochondria, can be induced by a response of dynamin-related protein 1 (Drp1) to a reduction in mitochondrial membrane potential (MMP) and mitochondrial division. However, the coordination between MMP and mitochondrial division for selecting the damaged portion of the mitochondrial network is less understood. Here, we found that MMP is reduced focally at a fission site by the Drp1 recruitment, which is initiated by the interaction of Drp1 with mitochondrial zinc transporter Zip1 and Zn2+ entry through the Zip1-MCU complex. After division, healthy mitochondria restore MMP levels and participate in the fusion-fission cycle again, but mitochondria that fail to restore MMP undergo mitophagy. Thus, interfering with the interaction between Drp1 and Zip1 blocks the reduction of MMP and the subsequent mitophagic selection of damaged mitochondria. These results suggest that Drp1-dependent fission provides selective pressure for eliminating "bad sectors" in the mitochondrial network, serving as a mitochondrial quality surveillance system.

Kidney Energetics and Cyst Burden in Autosomal Dominant Polycystic Kidney Disease: A Pilot Study.

RATIONALE & OBJECTIVE: In this pilot study, we hypothesized that autosomal dominant polycystic kidney disease (ADPKD) is characterized by impaired kidney oxidative metabolism that associates with kidney size and cyst burden. STUDY DESIGN: Cross-sectional study. SETTING & PARTICIPANTS: Twenty adults with ADPKD (age, 31±6 years; 65% women; body mass index [BMI], 26.8 [22.7-30.4] kg/m2; estimated glomerular filtration rate [eGFR, 2021 CKD-EPI creatinine], 103±18mL/min/1.73m2; height-adjusted total kidney volume [HTKV], 731±370mL/m; Mayo classifications 1B [5%], 1C [42%], 1D [21%], and 1E [32%]) and 11 controls in normal weight category (NWC) (age, 25±3 years; 45% women; BMI, 22.5 [21.7-24.2] kg/m2; eGFR, 113±15mL/min/1.73m2; HTKV, 159±31mL/m) at the University of Colorado Anschutz Medical Campus. PREDICTORS: ADPKD status (yes/no) and severity (Mayo classifications). OUTCOME: HTKV and cyst burden by magnetic resonance imaging, kidney oxidative metabolism, and perfusion by 11C-acetate positron emission tomography/computed tomography, insulin sensitivity by hyperinsulinemic-euglycemic clamps (presented as ratio of M-value of steady state insulin concentration [M/I]). ANALYTICAL APPROACH: For categorical variables, χ2/Fisher's exact tests, and for continuous variables t tests/Mann-Whitney U tests. Pearson correlation was used to estimate the relationships between variables. RESULTS: Compared with NWC individuals, the participants with ADPKD exhibited lower mean±SD M/I ratio (0.586±0.205 vs 0.424±0.171 [mg/kg lean/min]/(µIU/mL), P=0.04), lower median cortical perfusion (1.93 [IQR, 1.80-2.09] vs 0.68 [IQR, 0.47-1.04] mL/min/g, P<0.001) and lower median total kidney oxidative metabolism (0.17 [IQR, 0.16-0.19] vs. 0.14 [IQR, 0.12-0.15] min-1, P=0.001) in voxel-wise models excluding cysts. HTKV correlated inversely with cortical perfusion (r: -0.83, P < 0.001), total kidney oxidative metabolism (r: -0.61, P<0.001) and M/I (r: -0.41, P = 0.03). LIMITATIONS: Small sample size and cross-sectional design. CONCLUSIONS: Adults with ADPKD and preserved kidney function exhibited impaired renal perfusion and kidney oxidative metabolism across a wide range of cysts and kidney enlargements. FUNDING: Grants from government (National Institutes of Health, Centers for Disease Control and Prevention) and not-for-profit (JDRF) entities. TRIAL REGISTRATION: Registered at ClinicalTrials.gov with study numbers NCT04407481 and NCT04074668. PLAIN-LANGUAGE SUMMARY: In our study, we explored how a common genetic kidney condition, autosomal dominant polycystic kidney disease (ADPKD), relates to kidney metabolism. ADPKD leads to the growth of numerous cysts in the kidneys, which can impact their ability to work properly. We wanted to understand the kidneys' ability to process oxygen and blood flow in ADPKD. Our approach involved using advanced imaging techniques to observe kidney metabolism and blood flow in people with ADPKD compared with healthy individuals. We discovered that those with ADPKD had significant changes in kidney oxygen metabolism even when their kidney function was still normal. These findings are crucial as they provide deeper insights into ADPKD, potentially guiding future treatments to target these changes.

Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.

BACKGROUND. Timely and accurate interpretation of chest radiographs obtained to evaluate endotracheal tube (ETT) position is important for facilitating prompt adjustment if needed. OBJECTIVE. The purpose of our study was to evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) system for detecting ETT presence and position on chest radiographs in three patient samples from two different institutions. METHODS. This retrospective study included 539 chest radiographs obtained immediately after ETT insertion from January 1 to March 31, 2020, in 505 patients (293 men, 212 women; mean age, 63 years) from institution A (sample A); 637 chest radiographs obtained from January 1 to January 3, 2020, in 302 patients (157 men, 145 women; mean age, 66 years) in the ICU (with or without an ETT) from institution A (sample B); and 546 chest radiographs obtained from January 1 to January 20, 2020, in 83 patients (54 men, 29 women; mean age, 70 years) in the ICU (with or without an ETT) from institution B (sample C). A commercial DL-based AI system was used to identify ETT presence and measure ETT tip-to-carina distance (TCD). The reference standard for proper ETT position was TCD between greater than 3 cm and less than 7 cm, determined by human readers. Critical ETT position was separately defined as ETT tip below the carina or TCD of 1 cm or less. ROC analysis was performed. RESULTS. AI had sensitivity and specificity for identification of ETT presence of 100.0% and 98.7% (sample B) and 99.2% and 94.5% (sample C). AI had sensitivity and specificity for identification of improper ETT position of 72.5% and 92.0% (sample A), 78.9% and 100.0% (sample B), and 83.7% and 99.1% (sample C). At a threshold y-axis TCD of 2 cm or less, AI had sensitivity and specificity for critical ETT position of 100.0% and 96.7% (sample A), 100.0% and 100.0% (sample B), and 100.0% and 99.2% (sample C). CONCLUSION. AI identified improperly positioned ETTs on chest radiographs obtained after ETT insertion as well as on chest radiographs obtained of patients in the ICU at two institutions. CLINICAL IMPACT. Automated AI identification of improper ETT position on chest radiographs may allow earlier repositioning and thereby reduce complications.

Influence of Sphericity on Surface Termination of Icosahedral Colloidal Clusters.

Colloids self-organize into icosahedral clusters composed of a Mackay core and an anti-Mackay shell under spherical confinement to minimize the free energy. This study explores the variation of surface arrangements of colloids in icosahedral clusters, focusing on the determining factors behind the surface arrangement. To efficiently assemble particles in emulsion droplets, droplet-to-droplet osmotic extraction from particle-laden droplets to salt-containing droplets is used, where the droplets are microfluidically prepared to guarantee a high size uniformity. The icosahedral clusters are optimally produced during a 24-h consolidation period at a 0.04 m salt concentration. The findings reveal an increase in the number of particle layers from 10 to 15 in the icosahedral clusters as the average number of particles increases from 3300 to 11 000. Intriguingly, the number of layers in the anti-Mackay shells, or surface termination, appears to more strongly depend on the sphericity of the clusters than on the deviation in the particle count from an ideal icosahedral cluster. This result suggests that the sphericity of the outermost layer, formed by the late-stage rearrangement of particles to form an anti-Mackay shell near the droplet interface, may play a pivotal role in determining the surface morphology to accommodate a spherical interface.

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

BACKGROUND. Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. OBJECTIVE. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. METHODS. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs. The DL model was evaluated in a cohort of patients diagnosed with CAP during emergency department visits at the same institution from January 2020 to March 2020 (temporal test cohort [n = 947]) and in two additional cohorts from different institutions (external test cohort A [n = 467], January 2020 to December 2020; external test cohort B [n = 381], March 2019 to October 2021). AUCs were compared between the DL model and an established risk prediction tool based on the presence of confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age 65 years or older (CURB-65 score). The combination of CURB-65 score and DL model was evaluated with a logistic regression model. RESULTS. The AUC for predicting 30-day mortality was significantly larger (p < .001) for the DL model than for CURB-65 score in the temporal test set (0.77 vs 0.67). The larger AUC for the DL model than for CURB-65 score was not significant (p > .05) in external test cohort A (0.80 vs 0.73) or external test cohort B (0.80 vs 0.72). In the three cohorts, the DL model, in comparison with CURB-65 score, had higher (p < .001) specificity (range, 61-69% vs 44-58%) at the sensitivity of CURB-65 score. The combination of DL model and CURB-65 score, in comparison with CURB-65 score, yielded a significant increase in AUC in the temporal test cohort (0.77, p < .001) and external test cohort B (0.80, p = .04) and a nonsignificant increase in AUC in external test cohort A (0.80, p = .16). CONCLUSION. A DL-based model consisting of initial chest radiographs was predictive of 30-day mortality among patients with CAP with improved performance over CURB-65 score. CLINICAL IMPACT. The DL-based model may guide clinical decision-making in the care of patients with CAP.

Mohs Micrographic Surgery With Digital Pathology Improves Surgical Quality and Efficiency: A Retrospective Cohort Study.

BACKGROUND: Mohs micrographic surgery, involving pathology of the surgical margin, has the lowest recurrence rate for skin cancer. Moreover, because of technological advances, digital pathology systems are gradually being adopted in hospitals. Yongin Severance Hospital was the first hospital to construct a fully digitalized pathology system in Korea. OBJECTIVE: To evaluate the efficiency and characteristics of the digital pathology system for Mohs micrographic surgery. METHODS: The medical records of 80 patients with skin cancer who underwent Mohs micrographic surgery from March 2020 to August 2022 were analyzed for the number of frozen margins, number of stages, operation time, and recurrence rate to compare cases based on the pathology system. RESULTS: Overall, 23 and 57 patients were examined using the conventional and digital pathology systems, respectively. The mean number of final stages was 0.494 lower ( p -value = .008), the time from the previous to the next stage was 0.687-fold shorter ( p = .002), and the rate of switching from positive to negative margins was 1.990 times higher ( p = .044) in the digital than the conventional group. LIMITATIONS: Retrospective single-center experience; short follow-up time. CONCLUSION: Digital pathology reduces operative time and increases accuracy in Mohs micrographic surgery.

Cutaneous neurogenic inflammation mediated by TRPV1-NGF-TRKA pathway activation in rosacea is exacerbated by the presence of Demodex mites.

BACKGROUND: Rosacea is a common chronic inflammatory skin condition that is often refractory to treatment, with frequent relapses. Alterations in the skin immunological response and Demodex mite infestation are the primary aetiologic factors targeted for treatment. Transient receptor potential cation channel subfamily V member 1 (TRPV1) is a nociceptive cation channel that plays a role in cutaneous neurogenic pain and can be activated by various rosacea triggers. OBJECTIVES: We investigated the effects of TRPV1 modulation in rosacea, focussing on Demodex mite colonization and cutaneous neurogenic inflammation. METHODS: We examined mRNA expression levels according to Demodex population counts. An in vitro study using capsazepine as a TRPV1 antagonist was performed to assess the influence of TRPV1 in keratinocytes. A rosacea-like mouse model was generated by the injection of the 37-amino acid C-terminal cathelicidin peptide (LL37), and changes in the skin, dorsal root ganglion (DRG) and ears were examined. RESULTS: Increased Demodex mite population counts were associated with increased expression levels of TRPV1, tropomyosin receptor kinase A (TrkA) and nerve growth factor (NGF), and these levels could be reduced by capsazepine treatment in keratinocytes. In an in vivo study, the downstream effects of TRPV1 activation were investigated in the skin, DRG and ears of the rosacea-like mouse model. CONCLUSIONS: The findings of this study are instrumental for understanding the underlying causes of rosacea and could potentially lead to the development of new treatments targeting the NGF-TrkA-TRPV1 pathway. The identification of this pathway as a therapeutic target could represent a major breakthrough for rosacea research, potentially resulting in more effective and targeted rosacea treatments. This study contributes to an improved understanding of rosacea pathophysiology, which may lead to the development of more effective treatments in the future.

Assessing Nursing Sentiments on Nurse-Resident Physician Collaboration and Administrative Support in the Labor and Delivery Setting: Development and Validation of a Novel Instrument.

OBJECTIVE: To develop a survey instrument that: 1) investigates the dynamics of collaboration in multidisciplinary care units such as labor and delivery (L&D); and (2) uniquely determines the collaborative relationships between nurses and resident physicians. BACKGROUND: An effective interprofessional collaboration between healthcare providers is considered a critical component in delivering high-quality care to patients and lies at the root of ensuring positive patient outcomes. METHODS: Two samples of responses (n = 98) were collected from L&D nurses working in 2 hospitals: 1 with novel obstetrics and gynecology (OB/GYN) residency program and 1 with legacy OB/GYN residency program. Descriptive statistics and reliability statistics (Cronbach's α and item-total correlations) were calculated for each sample. RESULTS: Reliability analysis revealed strong internal consistency of the survey items in both samples even after completing single-item deletion analysis. CONCLUSIONS: Findings suggest that our 11-item instrument shows promise in effectively assessing nursing sentiments toward collaboration with resident physicians.

Evaluating the Efficacy of Hyperbaric Oxygen Therapy in Digital Frostbite.

Frostbite is a limb threatening, cold-induced tissue injury most commonly affecting the extremities. Hyperbaric oxygen therapy (HBOT) is a proposed adjunctive treatment for this condition, which acts by increasing cellular oxygen availability in damaged tissues. Currently, there is a lack of data regarding the effectiveness of HBOT. Therefore, the purpose of this study is to further the research as one of the largest retrospective comparative cohort studies to date. We evaluated the efficacy of HBOT in the treatment of digital frostbite compared to a non-HBOT-treated group, with a focus on amputation outcomes between each group. A multicenter retrospective cohort study was performed from January 2016 to August 2021 observing patients seen for frostbite. Amputation characteristics and encounter outcomes of patients treated with HBOT were compared to those in patients treated without HBOT. A one-to-one matching of HBOT-treated and non-HBOT-treated patients was also performed, followed by chi-square and Fisher's exact test statistical analysis. The results of the study found a low overall amputation rate of 5.2% across both cohorts. Comparison between groups identified no statistical difference between HBOT and non-HBOT groups regarding amputation characteristics through matched cohort analysis. However, an increased length of hospital stay in patients treated with HBOT (22.2 days) compared to the non-HBOT group (6.39 days) was identified. Based on this study, recommendations for future HBOT studies should evaluate the efficacy of HBOT for more severe cases of frostbite, with additional consideration for cost analysis studies.

Identification of Gut Microbiome Metabolites via Network Pharmacology Analysis in Treating Alcoholic Liver Disease.

Alcoholic liver disease (ALD) is linked to a broad spectrum of diseases, including diabetes, hypertension, atherosclerosis, and even liver carcinoma. The ALD spectrum includes alcoholic fatty liver disease (AFLD), alcoholic hepatitis, and cirrhosis. Most recently, some reports demonstrated that the pathogenesis of ALD is strongly associated with metabolites of human microbiota. AFLD was the onset of disease among ALDs, the initial cause of which is alcohol consumption. Thus, we analyzed the significant metabolites of microbiota against AFLD via the network pharmacology concept. The metabolites from microbiota were retrieved by the gutMGene database; sequentially, AFLD targets were identified by public databases (DisGeNET, OMIM). The final targets were utilized for protein-protein interaction (PPI) networks and signaling pathway analyses. Then, we performed a molecular docking test (MDT) to verify the affinity between metabolite(s) and target(s) utilizing the Autodock 1.5.6 tool. From a holistic viewpoint, we integrated the relationships of microbiota-signaling pathways-targets-metabolites (MSTM) using the R Package. We identified the uppermost six key targets (TLR4, RELA, IL6, PPARG, COX-2, and CYP1A2) against AFLD. The PPI network analysis revealed that TLR4, RELA, IL6, PPARG, and COX-2 had equivalent degrees of value (4); however, CYP1A2 had no associations with the other targets. The bubble chart showed that the PI3K-Akt signaling pathway in nine signaling pathways might be the most significant mechanism with antagonistic functions in the treatment of AFLD. The MDT confirmed that Icaritin is a promising agent to bind stably to RELA (known as NF-Κb). In parallel, Bacterium MRG-PMF-1, the PI3K-Akt signaling pathway, RELA, and Icaritin were the most significant components against AFLD in MSTM networks. In conclusion, we showed that the Icaritin-RELA complex on the PI3K-Akt signaling pathway by bacterial MRG-PMF-1 might have promising therapeutic effects against AFLD, providing crucial evidence for further research.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.

Co-Assembly of Colloids and Eumelanin Nanoparticles in Droplets for Structural Pigments with High Saturation.

Colloidal crystals have been used to develop structural colors. However, incoherent scattering causes the colors to turn whitish, reducing the color saturation. To overcome the problem, light-absorbing additives have been incorporated. Although various additives have been used, most of them are not compatible with a direct co-assembly with common colloids in aqueous suspensions. Here, the authors suggest eumelanin nanoparticles as a new additive to enhance the color chroma. Eumelanin nanoparticles are synthesized to have diameters of several nanometers by oxidative polymerization of precursors in basic solutions. The nanoparticles carry negative charges and do not weaken the electrostatic repulsion among same-charged polystyrene particles when they are added to aqueous suspensions. To prove the effectiveness of eumelanin as a saturation enhancer, the authors produce photonic balls through direct co-assembly of polystyrene and eumelanin using water-in-oil emulsion droplets, while varying the weight ratio of eumelanin to polystyrene. The high crystallinity of colloidal crystals is preserved for the ratio up to at least 1/50 as the eumelanin does not perturb the crystallization. The eumelanin effectively suppresses incoherent scattering while maintaining the strength of structural resonance at an optimum ratio, improving color chroma without compromising brightness.

Photonic Microbeads Templated by Oil-in-Oil Emulsion Droplets for High Saturation of Structural Colors.

Photonic microbeads containing crystalline colloidal arrays are promising as a key component of structural-color inks for various applications including printings, paintings, and cosmetics. However, structural colors from microbeads usually have low color saturation and the production of the beads requires delicate and time-consuming protocols. Herein, elastic photonic microbeads are designed with enhanced color saturation through facile photocuring of oil-in-oil emulsion droplets. Dispersions of highly-concentrated silica particles in elastomer precursors are microfluidically emulsified into immiscible oil to produce monodisperse droplets. The silica particles spontaneously form crystalline arrays in the entire volume of the droplets due to interparticle repulsion which is unperturbed by the diffusion of the surrounding oil whereas weakened for oil-in-water droplets. The crystalline arrays are permanently stabilized by photopolymerization of the precursor, forming elastic photonic microbeads. The microbeads are transferred into the refractive-index-matched biocompatible oil. The high crystallinity of colloidal arrays increases the reflectivity at stopband and the index matching reduces incoherent scattering at the surface of the microbeads, enhancing color saturation. The colors can be adjusted by mixing two distinctly colored microbeads. Also, low stiffness and high elasticity reduce foreign-body sensation and enhance fluidity, potentially serving as pragmatic structural colorants for photonic inks.

Treatment Response and Clinical Outcomes of Well-Differentiated High-Grade Neuroendocrine Tumors to Lutetium-177-DOTATATE.

INTRODUCTION: Lutetium-177 (177Lu)-DOTATATE received FDA approval in 2018 to treat somatostatin receptor-positive gastroenteropancreatic neuroendocrine tumors (NETs). Little data are available on response and outcomes for well-differentiated (WD) high-grade (HG) NETs treated with 177Lu-DOTATATE. MATERIALS AND METHODS: Patients with WD HG NETs treated with 177Lu-DOTATATE at MSK from 2018 to 2020 were identified. Demographics, response (RECIST 1.1), and progression-free survival (PFS) were determined. Next-generation sequencing (NGS) was performed in the archival tumor. RESULTS: Nineteen patients, all with progressive, heavily treated disease, were identified. Sites of tumor origin were: pancreas (74%), small bowel (11%), rectum (11%), and lung (5%); median Ki-67 was 32% (range 22-56). Thirteen patients (68%) completed all four 177Lu-DOTATATE cycles. Best response (N = 18 evaluable) was: 5/18 (28%) partial response, 8/18 (44%) stable disease, and 5/18 (28%) disease progression. Median PFS was 13.1 months (95% CI: 8.7-20.9). Most common treatment-related toxicities were thrombocytopenia (9 patients, 47%; G3/4, 1 patient, 5%), anemia (7 patients, 37%; G3/4, 2 patients, 11%), leukopenia (6 patients, 32%; G3/4, 0 patients), and liver function test elevation (4 patients, 21%; G3/4, 0 patients). NGS results were available from 13/19 tumors (68%). The most observed alterations were in MEN1 (6/13, 46%) and DAXX (4/13, 31%). No RB1 alterations identified. CONCLUSION: We observed a meaningful disease control rate of 72% during treatment of WD HG NETs with 177Lu-DOTATATE. In this heavily pre-treated population, more than half of patients received all four treatment cycles with toxicities largely bone marrow-related. As would be expected in WD NETs, the vast majority had alterations in chromatin remodeling genes and no RB1 alterations.

Autophagy inducers lead to transient accumulation of autophagosomes in Arabidopsis roots.

KEY MESSAGE: This study reveals that plant roots show a rapid termination of autophagy induction, offering a plant model for studying how excessive autophagy is deterred. In eukaryotes, autophagy is an intracellular mechanism that is important for recycling nutrients by degrading various macromolecules and organelles in vacuoles and lysosomes. Autophagy is induced when the nutrient supply to plant cells is limited. The protein kinase target of rapamycin (TOR) complex negatively regulates autophagy when nutrients are present in adequate amounts. The TOR inhibitor AZD8055 is an autophagy inducer that is useful for studying starvation-induced autophagy in plant cells. The mechanism by which AZD8055 increases the autophagic flux in plant cells has not been studied in detail. Here, we show that AZD8055-induced autophagy requires phosphatidylinositol 3-kinase activity and canonical AUTOPHAGY-RELATED (ATG) genes in Arabidopsis thaliana. Autophagic flux rapidly increased in seedlings treated with AZD8055. Unexpectedly, autophagy induction was transient in root cells and terminated earlier than in cotyledon cells. Transient induction is partly caused by a temporary effect of AZD8055 on phagophore initiation. These findings indicate a TOR-independent mechanism for terminating autophagy induction, thereby paving the way for elucidating how excess autophagy is prevented in plant roots.

Deep Learning Achieves Neuroradiologist-Level Performance in Detecting Hydrocephalus Requiring Treatment.

In large clinical centers a small subset of patients present with hydrocephalus that requires surgical treatment. We aimed to develop a screening tool to detect such cases from the head MRI with performance comparable to neuroradiologists. We leveraged 496 clinical MRI exams collected retrospectively at a single clinical site from patients referred for any reason. This diagnostic dataset was enriched to have 259 hydrocephalus cases. A 3D convolutional neural network was trained on 16 manually segmented exams (ten hydrocephalus) and subsequently used to automatically segment the remaining 480 exams and extract volumetric anatomical features. A linear classifier of these features was trained on 240 exams to detect cases of hydrocephalus that required treatment with surgical intervention. Performance was compared to four neuroradiologists on the remaining 240 exams. Performance was also evaluated on a separate screening dataset of 451 exams collected from a routine clinical population to predict the consensus reading from four neuroradiologists using images alone. The pipeline was also tested on an external dataset of 31 exams from a 2nd clinical site. The most discriminant features were the Magnetic Resonance Hydrocephalic Index (MRHI), ventricle volume, and the ratio between ventricle and brain volume. At matching sensitivity, the specificity of the machine and the neuroradiologists did not show significant differences for detection of hydrocephalus on either dataset (proportions test, p > 0.05). ROC performance compared favorably with the state-of-the-art (AUC 0.90-0.96), and replicated in the external validation. Hydrocephalus cases requiring treatment can be detected automatically from MRI in a heterogeneous patient population based on quantitative characterization of brain anatomy with performance comparable to that of neuroradiologists.

Oncogenic Role of Exosomal Circular and Long Noncoding RNAs in Gastrointestinal Cancers.

Circular RNAs (circRNAs) and long noncoding RNAs (lncRNAs) are differentially expressed in gastrointestinal cancers. These noncoding RNAs (ncRNAs) regulate a variety of cellular activities by physically interacting with microRNAs and proteins and altering their activity. It has also been suggested that exosomes encapsulate circRNAs and lncRNAs in cancer cells. Exosomes are then discharged into the extracellular environment, where they are taken up by other cells. As a result, exosomal ncRNA cargo is critical for cell-cell communication within the cancer microenvironment. Exosomal ncRNAs can regulate a range of events, such as angiogenesis, metastasis, immune evasion, drug resistance, and epithelial-to-mesenchymal transition. To set the groundwork for developing novel therapeutic strategies against gastrointestinal malignancies, a thorough understanding of circRNAs and lncRNAs is required. In this review, we discuss the function and intrinsic features of oncogenic circRNAs and lncRNAs that are enriched within exosomes.

Atorvastatin inhibits the proliferation of MKN45-derived gastric cancer stem cells in a mevalonate pathway-independent manner.

Gastric cancer stem cells (GCSCs) are a major cause of radioresistance and chemoresistance in gastric cancer (GC). Therefore, targeting GCSCs is regarded as a powerful strategy for the effective treatment of GC. Atorvastatin is a widely prescribed cholesterol-lowering drug that inhibits 3-hydroxy-3-methylglutaryl-coenzyme A reductase, a rate-limiting enzyme in the mevalonate pathway. The anticancer activity of atorvastatin, a repurposed drug, is being investigated; however, its therapeutic effect and molecular mechanism of action against GCSCs remain unknown. In this study, we evaluated the anticancer effects of atorvastatin on MKN45-derived GCSCs. Atorvastatin significantly inhibited the proliferative and tumorsphere-forming abilities of MKN45 GCSCs in a mevalonate pathway-independent manner. Atorvastatin induced cell cycle arrest at the G0/G1 phase and promoted apoptosis by activating the caspase cascade. Furthermore, atorvastatin exerted an antiproliferative effect against MKN45 GCSCs by inhibiting the expression of cancer stemness markers, such as CD133, CD44, integrin α6, aldehyde dehydrogenase 1A1, Oct4, Sox2, and Nanog, through the downregulation of ß-catenin, signal transducer and activator of transcription 3, and protein kinase B activities. Additionally, the combined treatment of atorvastatin and sorafenib, a multi-kinase targeted anticancer drug, synergistically suppressed not only the proliferation and tumorsphere formation of MKN45 GCSCs but also the in vivo tumor growth in a chick chorioallantoic membrane model implanted with MKN45 GCSCs. These findings suggest that atorvastatin can therapeutically eliminate GCSCs.

A putative exosporium lipoprotein GBAA0190 of Bacillus anthracis as a potential anthrax vaccine candidate.

BACKGROUND: Bacillus ancthracis causes cutaneous, pulmonary, or gastrointestinal forms of anthrax. B. anthracis is a pathogenic bacterium that is potentially to be used in bioterrorism because it can be produced in the form of spores. Currently, protective antigen (PA)-based vaccines are being used for the prevention of anthrax, but it is necessary to develop more safe and effective vaccines due to their prolonged immunization schedules and adverse reactions. METHODS: We selected the lipoprotein GBAA0190, a potent inducer of host immune response, present in anthrax spores as a novel potential vaccine candidate. Then, we evaluated its immune-stimulating activity in the bone marrow-derived macrophages (BMDMs) using enzyme-linked immunosorbent assay (ELISA) and Western blot analysis. Protective efficacy of GBAA0190 was evaluated in the guinea pig (GP) model. RESULTS: The recombinant GBAA0190 (r0190) protein induced the expression of various inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), and macrophage inflammatory protein-1α (MIP-1α) in the BMDMs. These immune responses were mediated through toll-like receptor 1/2 via activation of mitogen-activated protein (MAP) kinase and Nuclear factor-κB (NF-κB) pathways. We demonstrated that not only immunization of r0190 alone, but also combined immunization with r0190 and recombinant PA showed significant protective efficacy against B. anthracis spore challenges in the GP model. CONCLUSIONS: Our results suggest that r0190 may be a potential target for anthrax vaccine.

Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications.

Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the "-omics" disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas' role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.