Bioprinted 3D outer retina barrier uncovers RPE-dependent choroidal phenotype in advanced macular degeneration.

Age-related macular degeneration (AMD), a leading cause of blindness, initiates in the outer-blood-retina-barrier (oBRB) formed by the retinal pigment epithelium (RPE), Bruch's membrane, and choriocapillaris. The mechanisms of AMD initiation and progression remain poorly understood owing to the lack of physiologically relevant human oBRB models. To this end, we engineered a native-like three-dimensional (3D) oBRB tissue (3D-oBRB) by bioprinting endothelial cells, pericytes, and fibroblasts on the basal side of a biodegradable scaffold and establishing an RPE monolayer on top. In this 3D-oBRB model, a fully-polarized RPE monolayer provides barrier resistance, induces choriocapillaris fenestration, and supports the formation of Bruch's-membrane-like structure by inducing changes in gene expression in cells of the choroid. Complement activation in the 3D-oBRB triggers dry AMD phenotypes (including subRPE lipid-rich deposits called drusen and choriocapillaris degeneration), and HIF-α stabilization or STAT3 overactivation induce choriocapillaris neovascularization and type-I wet AMD phenotype. The 3D-oBRB provides a physiologically relevant model to studying RPE-choriocapillaris interactions under healthy and diseased conditions.

iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides.

With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as entry inhibitors (EIs) with distinct advantages over chemical counterparts. Despite this, a comprehensive analytical platform for characterizing these peptides and their effectiveness in blocking viral entry remains lacking. In this study, we introduce a groundbreaking in silico approach that leverages bioinformatics analysis and machine learning to characterize and identify novel VEIPs. Cross-validation results demonstrate the efficacy of a model combining sequence-based features in predicting VEIPs with high accuracy, validated through independent testing. Additionally, an EI type model has been developed to distinguish peptides specifically acting as Eis from AVPs with alternative activities. Notably, we present iDVEIP, a web-based tool accessible at http://mer.hc.mmh.org.tw/iDVEIP/, designed for automatic analysis and prediction of VEIPs. Emphasizing its capabilities, the tool facilitates comprehensive analyses of peptide characteristics, providing detailed amino acid composition data for each prediction. Furthermore, we showcase the tool's utility in identifying EIs against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes-eigenmodes-of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.

Understanding REM Sleep Behavior Disorder through Functional MRI: A Systematic Review.

Neuroimaging studies in rapid eye movement sleep behavior disorder (RBD) can inform fundamental questions about the pathogenesis of Parkinson's disease (PD). Across modalities, functional magnetic resonance imaging (fMRI) may be better suited to identify changes between neural networks in the earliest stages of Lewy body diseases when structural changes may be subtle or absent. This review synthesizes the findings from all fMRI studies of RBD to gain further insight into the pathophysiology and progression of Lewy body diseases. A total of 32 studies were identified using a systematic review conducted according to PRISMA guidelines between January 2000 to February 2024 for original fMRI studies in patients with either isolated RBD (iRBD) or RBD secondary to PD. Common functional alterations were detectable in iRBD patients compared with healthy controls across brainstem nuclei, basal ganglia, frontal and occipital lobes, and whole brain network measures. Patients with established PD and RBD demonstrated decreased functional connectivity across the whole brain and brainstem nuclei, but increased functional connectivity in the cerebellum and frontal lobe compared with those PD patients without RBD. Finally, longitudinal changes in resting state functional connectivity were found to track with disease progression. Currently, fMRI studies in RBD have demonstrated early signatures of neurodegeneration across both motor and non-motor pathways. Although more work is needed, such findings have the potential to inform our understanding of disease, help to distinguish between prodromal PD and prodromal dementia with Lewy bodies, and support the development of fMRI-based outcome measures of phenoconversion and progression in future disease modifying trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Bletilla striata Polysaccharide-/Chitosan-Based Self-Healing Hydrogel with Enhanced Photothermal Effect for Rapid Healing of Diabetic Infected Wounds via the Regulation of Microenvironment.

The management of diabetic ulcers poses a significant challenge worldwide, and persistent hyperglycemia makes patients susceptible to bacterial infections. Unfortunately, the overuse of antibiotics may lead to drug resistance and prolonged infections, contributing to chronic inflammation and hindering the healing process. To address these issues, a photothermal therapy technique was incorporated in the preparation of wound dressings. This innovative solution involved the formulation of a self-healing and injectable hydrogel matrix based on the Schiff base structure formed between the oxidized Bletilla striata polysaccharide (BSP) and hydroxypropyltrimethylammonium chloride chitosan. Furthermore, the introduction of CuO nanoparticles encapsulated in polydopamine imparted excellent photothermal properties to the hydrogel, which promoted the release of berberine (BER) loaded on the nanoparticles and boosted the antibacterial performance. In addition to providing a reliable physical protection to the wound, the developed hydrogel, which integrated the herbal components of BSP and BER, effectively accelerated wound closure via microenvironment regulation, including alleviated inflammatory reaction, stimulated re-epithelialization, and reduced oxidative stress based on the promising results from cell and animal experiments. These impressive outcomes highlighted their clinical potential in safeguarding the wound against bacterial intrusion and managing diabetic ulcers.

Pharmacological agents targeting drug-tolerant persister cells in cancer.

Current cancer therapy can be effective, but the development of drug resistant disease is the usual outcome. These drugs can eliminate most of the tumor burden but often fail to eliminate the rare, "Drug Tolerant Persister" (DTP) cell subpopulations in residual tumors, which can be referred to as "Persister" cells. Therefore, novel therapeutic agents specifically targeting or preventing the development of drug-resistant tumors mediated by the remaining persister cells subpopulations are needed. Since approximately ninety percent of cancer-related deaths occur because of the eventual development of drug resistance, identifying, and dissecting the biology of the persister cells is essential for the creation of drugs to target them. While there remains uncertainty surrounding all the markers identifying DTP cells in the literature, this review summarizes the drugs and therapeutic approaches that are available to target the persister cell subpopulations expressing the cellular markers ATP-binding cassette sub-family B member 5 (ABCB5), CD133, CD271, Lysine-specific histone demethylase 5 (KDM5), and aldehyde dehydrogenase (ALDH). Persister cells expressing these markers were selected as the focus of this review because they have been found on cells surviving following drug treatments that promote recurrent drug resistant cancer and are associated with stem cell-like properties, including self-renewal, differentiation, and resistance to therapy. The limitations and obstacles facing the development of agents targeting these DTP cell subpopulations are detailed, with discussion of potential solutions and current research areas needing further exploration.

[The Future of Nursing Education: Necessary Elements and Implementation Strategies for Learning Artificial Intelligence].

As populations age, average life expectancy increases and the complexity of diseases rises, leading to nursing care and healthcare systems facing severe challenges related to inadequate resources. Artificial intelligence (AI), including elements such as investigation, integration, learning, prediction, and decision-making, holds significant potential for application in clinical care not only to enhance care quality but also to help guide the future direction of healthcare. AI applications are already being increasingly utilized to improve the quality of clinical care and to streamline workflows. However, because nursing education has lagged behind in terms of adopting AI, greater attention must be given to training up nursing students with AI-related knowledge and application skills. AI technologies should be integrated into nursing curricula and clinical internships to adapt to the rapidly changing high-tech healthcare environment, enabling the more-effective use of AI technology in providing high-quality and safe nursing care.

Ginsenoside Rh2 augmented anti-PD-L1 immunotherapy by reinvigorating CD8+ T cells via increasing intratumoral CXCL10.

Profiting from the sustained clinical improvement and prolonged patient survival, immune checkpoint blockade of programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) axis has emerged as a revolutionary cancer therapy approach. However, the anti-PD-1/PD-L1 antibodies only achieve a clinical response rate of approximately 20%. Herein, we identified a novel combination strategy that Chinese medicine ginseng-derived ginsenoside Rh2 (Rh2) markedly improved the anti-cancer efficacy of anti-PD-L1 antibody in mice bearing MC38 tumor. Rh2 combined with anti-PD-L1 antibody (combo treatment) further triggered the infiltration, proliferation and activation of CD8+ T cells in the tumor microenvironment (TME). Depletion of CD8+ T cells by mouse CD8 blocking antibody abolished the anti-cancer effect of combo treatment totally. Mechanistically, combo treatment further increased the expression of CXCL10 through activating TBK1-IRF3 signaling pathway, explaining the increased infiltration of T cells. Employing anti- CXC chemokine receptor 3 (CXCR3) blocking antibody prevented the T cells infiltration and abolished the anti-cancer effect of combo treatment. Meanwhile, combo treatment increased the percentage of M1-like macrophages and raised the ratio of M1/M2 macrophages in TME. By comparing the anti-cancer effect of combo treatment among MC38, CT26 and 4T1 tumors, resident T cells were considered as a prerequisite for the effectiveness of combo treatment. These findings demonstrated that Rh2 potentiated the anti-cancer effect of PD-L1 blockade via promoting the T cells infiltration and activation, which shed a new light on the combination strategy to enhance anti-PD-L1 immunotherapy by using natural product Rh2.

Natural history of lung squamous cell brain metastases in patients treated with radiosurgery: a thirty-year experience at a tertiary medical center.

PURPOSE: In this study we report our 30-year experience in stereotactic radiosurgery (SRS) treatment of lung squamous cell carcinoma (LUSC) brain metastases (BMs). It will serve to provide detailed longitudinal outcomes and predictors of efficacy in treating LUSC-BMs with SRS. METHOD: We retrospectively reviewed 51 patients and 109 tumors treated with SRS at our center between 1993 and 2022. Patient demographics, PDL1 genotype, immunotherapy use and mortality cause were recorded. Radiological and clinical outcomes were followed at 1-3-month intervals post-SRS. Cox-regression analysis and Kaplan-Meier survival curves were performed in statistical analysis. RESULTS: We included 37 male and 14 female patients (median age 62.7 years at BM diagnosis). Median overall survival (OS) time was 6.9 months, 6-month OS rate was 62.1%, and Karnofsky performance scale (KPS) was the only independent predictor. Median time for local control maintenance was 7.6 months, 6-month local control rate was 69.1%, with TKI as the only independent predictor. Median time to distant failure was 5.13 months, 6-month distant failure rate was 51.1%, and factors with significant impact included gender (p = 0.002), presence of extracranial metastases (p < 0.001), use of immunotherapy(p < 0.001), PDL1 genotype (p = 0.034), and total intracranial metastases number (p = 0.008). However, no definitive benefits of immunotherapy were identified in patients with higher PDL1 mutational tumors. CONCLUSION: In this study we defined the natural history of disease progression and outcomes in SRS-treated LUSC-BM patients. We also identified predictors of OS and tumor control among these patients. The findings of this study will serve as a guide when counseling these patients for SRS.

What are the roles of eHealth literacy and empowerment in self-management in an eHealth care context? A cross-sectional study.

AIMS: To examine the relationship among eHealth literacy, empowerment and self-management and the mediating effects of empowerment in diabetic kidney disease (DKD) patients in the eHealthcare context. BACKGROUND: Self-management is an essential aspect of healthcare in delaying disease progression for DKD. In the eHealthcare era, health services providing self-management are transforming. The ability and confidence of patients to use eHealth services is a critical issue that impacts the effectiveness of self-management, but little is known about the role of eHealth literacy and empowerment in self-management. DESIGN: A cross-sectional study guided by the STROBE. METHODS: Overall, 127 Taiwanese patients were enrolled using convenience sampling. Data collection used structured questionnaires and chart reviews. Multiple regression was used to infer self-management predictors, and SPSS PROCESS macro and bootstrapping verified the mediating effects. RESULTS: Empowerment and eHealth literacy both showed significant positive correlations with self-management. Empowerment was the main predictor of self-management and had a complete mediating effect between eHealth literacy and self-management. CONCLUSION: Increasing patients' eHealth literacy can improve empowerment and prevent health inequality issues. Healthcare providers should consider improving patients' eHealth literacy to enhance their self-management. RELEVANCE TO CLINICAL PRACTICE: Healthcare service systems need to create user-friendly eHealthcare environments, and healthcare professionals can provide multifaceted instructions that fit patients' eHealth literacy levels to enhance their motivation and confidence in disease care, thus cultivating positive self-management behaviours. IMPACT: The popularity of eHealthcare services aimed at promoting self-management behaviours is increasing. However, the level of eHealth literacy is an essential factor that affects the effectiveness of self-management in the healthcare environment. In addition, empowerment is a major critical influence factor of self-management and a completely mediating variable between self-management and eHealth literacy. Consequently, healthcare providers should consider promoting patients' eHealth literacy to empower people using eHealthcare services for implementing self-management. REPORTING METHOD: The Strengthening the Reporting of Observational Studies in cross-sectional studies (STROBE) checklist was used to ensure comprehensive reporting. PATIENT OR PUBLIC CONTRIBUTION: Patients were diagnosed with DKD in the study hospital. Physicians and case managers transferred patients to research assistants who screened them for the inclusion criteria and invited them to participate in this study if they met the requirements. After participants signed informed consent, the research nurse encouraged participants to respond to the research questionnaire face to face.

A Berberine-Loaded Bletilla striata Polysaccharide Hydrogel as a New Medical Dressing for Diabetic Wound Healing.

The healing process of a diabetic wound (DW) is often impeded by a series of interrelated factors, including severe infection, persistent inflammation, and excessive oxidative stress. Therefore, it is particularly crucial to develop a medical dressing that can address these issues simultaneously. To this end, different ratios of Bletilla striata polysaccharide (BSP) and berberine (BER) were physically blended with Carbomer 940 (CBM940) to develop a composite hydrogel as a medical dressing. The BSP/BER hydrogel was characterized using SEM, FTIR, rheological testing and other techniques. The anti-inflammatory, antioxidant, and antibacterial properties of the hydrogel were evaluated using cell and bacterial models in vitro. A DW model of ICR mice was established to evaluate the effect of the hydrogel on DW healing in vivo. The hydrogel exhibited excellent biocompatibility and remarkable antibacterial, anti-inflammatory, and antioxidant properties. In addition, animal experiments showed that the BSP/BER hydrogel significantly accelerated wound healing in DW mice. Among the different formulations, the LBSP/BER hydrogel (2% BSP, mBER:mBSP = 1:40) demonstrated the most remarkable efficacy. In conclusion, the BSP/BER hydrogel developed exhibited immense properties and great potential as a medical dressing for the repair of DW, addressing a crucial need in clinical practice.

Design and evaluation of a remote synchronous gamified mathematics teaching activity that integrates multi-representational scaffolding and a mind tool for gamified learning.

Gamified learning is an instructional strategy that motivates students to learn, and the use of multiple representations assists learning by promoting students' thinking and advanced mathematical problem-solving skills. In particular, emergency distance learning caused by the COVID-19 pandemic may result in a lack of motivation and effectiveness in learning. This study designed an online gamified learning activity incorporating multi-representational scaffolding and compared the differences in the learning achievement and motivation for the gamified activity and general synchronous distance learning. In addition, for the group that conducted the gamified learning activity, we measured the participants' flow, anxiety, and emotion during the activity. A total of 36 high school students participated in the experiment. The results indicated that the gamified learning activity was not significantly effective in terms of enhancing learning achievement. In terms of learning motivation, a significant decrease in motivation was found for the group using general synchronous learning, while a significant increase in motivation was found for the group using synchronous gamified learning. This indicates that despite the negative impact of the pandemic on learning, gamified learning still enhances students' learning motivation. The results of flow, anxiety, and emotion showed that the participants had a positive and engaged experience. Participants provided feedback that the multi-representational scaffolding facilitates learning.

Phase-shifting algorithms with known and unknown phase shifts: comparison and hybrid.

The phase-shifting interferometry has been intensively studied for more than half a century, and is still actively investigated and improved for more demanding precision measurement requirements. A proper phase-shifting algorithm (PSA) for phase extraction should consider various error sources including (i) the phase-shift errors, (ii) the intensity harmonics, (iii) the non-uniform phase-shift distributions and (iv) the random additive intensity noise. Consequently, a large pool of PSAs has been developed, including those with known phase shifts (abbreviated as kPSA) and those with unknown phase shifts (abbreviated as uPSA). While numerous evaluation works have been done for the kPSAs, there are very few for the uPSAs, making the overall picture of the PSAs unclear. Specifically, there is a lack of (i) fringe pattern parameters' restriction analysis for the uPSAs and (ii) performance comparison within the uPSAs and between the uPSAs and the kPSAs. Thus, for the first time, we comprehensively evaluated the pre-requisites and performance of four representative uPSAs, the advanced iterative algorithm, the general iterative algorithm (GIA), the algorithm based on the principal component analysis and the algorithm based on VU factorization, and then compare the uPSAs with twelve benchmarking kPSAs. From this comparison, the demand for proper selection of a kPSA, and the restriction and attractive performance of the uPSAs are clearly depicted. Due to the outstanding performance of the GIA, a hybrid kPSA-GIA is proposed to boost the performance of a kPSA and relieve the fringe density restriction of the GIA.

Trajectories of symptom severity predicts quality of life change in newly diagnosis lymphoma survivors: An initial study.

OBJECTIVE: This study aimed to explore the subgroups of symptom severity and impact of their trajectories on quality of life in lymphoma survivors. METHODS: Secondary data were analysed from a prospective study with four-time measures: before treatment (T1), during treatment (T2), treatment completion (T3) and 10 weeks after treatment (T4). Data were analysed using descriptive statistics, group-based trajectory model and generalised estimation equation. RESULTS: Fifty nine of 61 participants completed three-time measure (mean age = 60.43 years, male-predominant). The changes in symptom severity over time were divided into two subgroups: slight-stable group (n = 54, 89%) and mild-fickle group (n = 7, 11%). Pain, tiredness and sleeping trouble were the predominant symptoms. The quality of life change in the slight-stable group was significantly better than that of the mild-fickle group (B = 13.35, SE = 3.53, p < 0.001). The overall quality of life at T2, T3 and T4 was better than it was at T1. CONCLUSION: The different trajectories of symptom severity significantly influenced quality of life changes in lymphoma survivors. Healthcare providers must be aware that there is a group of lymphoma survivors with relatively severe symptoms when newly diagnosed, compared to the opposite. More attention must be paid to this group, in addition to providing in-time symptom management.

Anti-Inflammatory Polyketides from an Alga-Derived Fungus Aspergillus ochraceopetaliformis SCSIO 41020.

A new linear polyketide, named aspormisin A (1), together with five known polyketides (2-6), were isolated from the alga-derived fungus Aspergillus ochraceopetaliformis SCSIO 41020. Their structures were elucidated through a detailed comprehensive spectroscopic analysis, as well as a comparison with the literature. An anti-inflammatory evaluation showed that compounds 2, 5, and 6 possessed inhibitory activity against the excessive production of nitric oxide (NO) and pro-inflammatory cytokines in LPS-treated RAW 264.7 macrophages in a dose-dependent manner without cytotoxicity. Further studies revealed that compound 2 was active in blocking the release of pro-inflammatory cytokines (IL-6, MCP-1, and TNF-α) induced by LPS both in vivo and in vitro. Our findings provide a basis for the further development of linear polyketides as promising anti-inflammatory agents.

Validity Testing and Cultural Adaptation of the eHealth Literacy Questionnaire (eHLQ) Among People With Chronic Diseases in Taiwan: Mixed Methods Study.

BACKGROUND: Advancements in digital technologies seek to promote health and access to services. However, people lacking abilities and confidence to use technology are likely to be left behind, leading to health disparities. In providing digital health services, health care providers need to be aware of users' diverse electronic health (eHealth) literacy to address their particular needs and ensure equitable uptake and use of digital services. To understand such needs, an instrument that captures users' knowledge, skills, trust, motivation, and experiences in relation to technology is required. The eHealth Literacy Questionnaire (eHLQ) is a multidimensional tool with 7 scales covering diverse dimensions of eHealth literacy. The tool was simultaneously developed in English and Danish using a grounded and validity-driven approach and has been shown to have strong psychometric properties. OBJECTIVE: This study aims to translate and culturally adapt the eHLQ for application among Mandarin-speaking people with chronic diseases in Taiwan and then undertake a rigorous set of validity-testing procedures. METHODS: The cross-cultural adaptation of the eHLQ included translation and evaluation of the translations. The measurement properties were assessed using classical test theory and item response theory (IRT) approaches. Content validity, known-group validity, and internal consistency were explored, as well as item characteristic curves (ICCs), item discrimination, and item location/difficulty. RESULTS: The adapted version was reviewed, and a recommended forward translation was confirmed through consensus. The tool exhibited good content validity. A total of 420 people with 1 or more chronic diseases participated in a validity-testing survey. The eHLQ showed good internal consistency (Cronbach α=.75-.95). For known-group validity, all 7 eHLQ scales showed strong associations with education. Unidimensionality and local independence assumptions were met except for scale 2. IRT analysis showed that all items demonstrated good discrimination (range 0.27-12.15) and a good range of difficulty (range 0.59-1.67) except for 2 items in scale 7. CONCLUSIONS: Using a rigorous process, the eHLQ was translated from English into a culturally appropriate tool for use in the Mandarin language. Validity testing provided evidence of satisfactory-to-strong psychometric properties of the eHLQ. The 7 scales are likely to be useful research tools for evaluating digital health interventions and for informing the development of health technology products and interventions that equitably suit diverse users' needs.

RAFI: Robust Authentication Framework for IoT-Based RFID Infrastructure.

The Internet of Things (IoT) is a future trend that uses the Internet to connect a variety of physical things with the cyber world. IoT technology is rapidly evolving, and it will soon have a significant impact on our daily lives. While the growing number of linked IoT devices makes our daily lives easier, it also puts our personal data at risk. In IoT applications, Radio Frequency Identification (RFID) helps in the automatic identification of linked devices, and the dataflow of the system forms a symmetry in communication between the tags and the readers. However, the security and privacy of RFID-tag-connected devices are the key concerns. The communication link is thought to be wireless or insecure, making the RFID system open to several known threats. In order to address these security issues, we propose a robust authentication framework for IoT-based RFID infrastructure. We use formal security analysis in the random oracle model, as well as information analysis to support the claim of secure communication. Regarding the desirable performance characteristics, we describe and analyze the proposed framework's performance and compare it to similar systems. According to our findings, the proposed framework satisfies all security requirements while also improving the communication.

Targeting Protein Translation in Melanoma by Inhibiting EEF-2 Kinase Regulates Cholesterol Metabolism though SREBP2 to Inhibit Tumour Development.

Decreasing the levels of certain proteins has been shown to be important for controlling cancer but it is currently unknown whether proteins could potentially be targeted by the inhibiting of protein synthesis. Under this circumstance, targeting protein translation could preferentially affect certain pathways, which could then be of therapeutic advantage when treating cancer. In this report, eukaryotic elongation factor-2 kinase (EEF2K), which is involved in protein translation, was shown to regulate cholesterol metabolism. Targeting EEF2K inhibited key parts of the cholesterol pathway in cancer cells, which could be rescued by the addition of exogenous cholesterol, suggesting that it is a potentially important pathway modulated by targeting this process. Specifically, targeting EEF2K significantly suppressed tumour cell growth by blocking mRNA translation of the cholesterol biosynthesis transcription factor, sterol regulatory element-binding protein (SREBP) 2, and the proteins it regulates. The process could be rescued by the addition of LDL cholesterol taken into the cells via non-receptor-mediated-uptake, which negated the need for SREBP2 protein. Thus, the levels of SREBP2 needed for cholesterol metabolism in cancer cells are therapeutically vulnerable by targeting protein translation. This is the first report to suggest that targeting EEF2K can be used to modulate cholesterol metabolism to treat cancer.

General iterative algorithm for phase-extraction from fringe patterns with random phase-shifts, intensity harmonics and non-uniform phase-shift distribution.

Advanced iterative algorithm (AIA) is a flexible and effective phase-shifting algorithm (PSA) which can extract phase from fringe patterns with random unknown phase-shifts, making it attractive in the scenarios where phase-shifts are unknown or not accurate. However, accuracy of AIA degrades when intensity harmonics and/or phase-shift non-uniformity are presented. To solve this problem, multiple PSAs have been proposed, but they restrict their fringe model in one way or another, and thus sacrifice the immunity to certain error source(s). In this paper, a general iterative algorithm (GIA) which adopts a most general fringe model is proposed. In GIA, the many unknowns in the fringe pattern model are divided into three groups including: (i) the fringe amplitudes, (ii) the phase and (iii) the phase-shifts related parameters, and alternatively optimized through univariate search technique group by group to improve accuracy and convergence. The Levenberg-Marquart method is used for the optimization of each group of unknowns due to its excellent accuracy and robustness. GIA is shown to have better accuracies than all of its relevant competitors through both a large number of simulations as well as real experiments with a Fizeau interferometer.

Benzo[a]pyrene induces fibrotic changes and impairs differentiation in lung stem cells.

Human activities have generated air pollution, with extremely small particles (PM 2.5, particulate matter less than 2.5 µm in diameter) and liquid droplets, which become a menace to human health. Among the pollutants, polycyclic aromatic hydrocarbons (PAHs), which enhance the risks of pulmonary dysfunction and cancer development, have been extensively studied. Numerous studies have addressed the effects of PAHs on the respiratory system, whereas the effects on lung stem/progenitor cells remain unknown. Here, we provide evidence that benzo[a]pyrene (BaP), a major toxic PAH, induces fibrotic changes with a loss of α-1,6-fucosylation in CD54+CD157+CD45- cells (lung stem cells). In studies with aryl hydrocarbon receptor (AHR) antagonist, we found that these effects by BaP are independent of the canonical AHR pathway. In addition, these BaP-induced fibrotic changes are reduced by TGF-ß antagonist, suggesting an alternative pathway of BaP toxicity is different from other PAH/AHR signaling pathways. Finally, it was observed that BaP impairs the spheroid formation and the podoplanin expression of CD54+CD157+CD45- cells, indicating that BaP suppresses the differentiation of lung stem cells. Taken together, our findings reveal specific BaP-induced injuries in CD54+CD157+CD45- cells.