A Poly(amino acid)-Based Nanomedicine Strategy: Telomere-Telomerase Axis Targeting and Magnetic Resonance Imaging in Hepatocellular Carcinoma Treatment.

Targeting telomere maintenance has emerged as a promising strategy for hepatocellular carcinoma (HCC) treatment. However, given the duality of the telomere-telomerase axis in telomere maintenance, a comprehensive strategy is urgently needed. Herein, we develop a poly(amino acid) (D-PAAs)-based strategy for spatiotemporal codelivery of telomerase inhibitor, BIBR1523, and AKT inhibitor, isobavachalcone. By leveraging D-PAAs' modifiability, we synthesize polymer-inhibitor conjugates (PB and PI) and a folic acid-decorated tumor-targeting vector (PF). These building blocks undergo micellization to fabricate a codelivery nanomedicine (P-BI@P-FA) by exploiting D-PAAs' noncovalent assembly. P-BI@P-FA improves the pharmacokinetics, tumor selectivity, and bioavailability of small molecule inhibitors and initiates a dual telomere-specific inhibition by combining telomerase deactivation with telomere disruption. Furthermore, a hybrid tumor-targeting magnetic nanosystem is designed using D-PAAs and manganese dioxide to showcase magnetic resonance imaging capacities. Our D-PAAs-based strategy addresses the pressing need for telomere-specific HCC treatment while allowing for diagnostic application, presenting a promising avenue for nanomedicine design.

Unlocking Direct Lithium Extraction in Harsh Conditions through Thiol-Functionalized Metal-Organic Framework Subnanofluidic Membranes.

Metal-organic framework (MOF) membranes with high ion selectivity are highly desirable for direct lithium-ion (Li+) separation from industrial brines. However, very few MOF membranes can efficiently separate Li+ from brines of high Mg2+/Li+ concentration ratios and keep stable in ultrahigh Mg2+-concentrated brines. This work reports a type of MOF-channel membranes (MOFCMs) by growing UiO-66-(SH)2 into the nanochannels of polymer substrates to improve the efficiency of MOF membranes for challenging Li+ extraction. The resulting membranes demonstrate excellent monovalent metal ion selectivity over divalent metal ions, with Li+/Mg2+ selectivity up to 103 since Mg2+ should overcome a higher energy barrier than Li+ when transported through the MOF pores, as confirmed by molecular dynamics simulations. Under dual-ion diffusion, as the Mg2+/Li+ mole ratio of the feed solution increases from 0.2 to 30, the membrane Li+/Mg2+ selectivity decreases from 1516 to 19, corresponding to the purity of lithium products between 99.9 and 95.0%. Further research on multi-ion diffusion that involves Mg2+ and three monovalent metal ions (K+, Na+, and Li+, referred to as M+) in the feed solutions shows a significant improvement in Li+/Mg2+ separation efficiency. The Li+/Mg2+ selectivity can go up to 1114 when the Mg2+/M+ molar concentration ratio is 1:1, and it remains at 19 when the ratio is 30:1. The membrane selectivity is also stable for 30 days in a highly concentrated solution with a high Mg2+/Li+ concentration ratio. These results indicate the feasibility of the MOFCMs for direct lithium extraction from brines with Mg2+ concentrations up to 3.5 M. This study provides an alternative strategy for designing efficient MOF membranes in extracting valuable minerals in the future.

Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) with Electronic Health Records.

BACKGROUND: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations. OBJECTIVE: We develop a Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) algorithm based on extensive unlabeled longitudinal Electronic Health Records (EHR) data augmented by a limited set of gold standard labels on the binary status information indicating whether the clinical event of interest occurred during the follow-up period. METHODS: The SeDDLeR algorithm calculates an individualized risk of developing future clinical events over time using each patient's baseline EHR features via the following steps: (1) construction of an initial EHR-derived surrogate as a proxy for the onset status; (2) deep learning calibration of the surrogate along gold-standard onset status; and (3) semi-supervised deep learning for risk prediction combining calibrated surrogates and gold-standard onset status. To account for missing onset time and heterogeneous follow-up, we introduce temporal kernel weighting. We devise a Gated Recurrent Units (GRUs) module to capture temporal characteristics. We subsequently assess our proposed SeDDLeR method in simulation studies and apply the method to the Massachusetts General Brigham (MGB) Biobank to predict type 2 diabetes (T2D) risk. RESULTS: SeDDLeR outperforms benchmark risk prediction methods, including Semi-parametric Transformation Model (STM) and DeepHit, with consistently best accuracy across experiments. SeDDLeR achieved the best C-statistics ( 0.815, SE 0.023; vs STM +.084, SE 0.030, P-value .004; vs DeepHit +.055, SE 0.027, P-value .024) and best average time-specific AUC (0.778, SE 0.022; vs STM + 0.059, SE 0.039, P-value .067; vs DeepHit + 0.168, SE 0.032, P-value <0.001) in the MGB T2D study. CONCLUSION: SeDDLeR can train robust risk prediction models in both real-world EHR and synthetic datasets with minimal requirements of labeling event times. It holds the potential to be incorporated for future clinical trial recruitment or clinical decision-making.

Combining the Tunnel Orbicularis Oculi Muscle Flap Technique With Skin Grafting for Enhanced Adhesion in Burn-Induced Ectropion Repair.

PURPOSE: Scar contracture of the eyelid following facial burns often has adverse consequences. Total cicatricial contracture often makes adjustment flap translation challenging to implement. Previously used upper and lower eyelid adhesion methods are ineffective for patients with severe cicatricial contracture, and ectropion can easily recur. This study aimed to retrospectively examine upper and lower eyelid adhesions using an orbicularis oculi muscle flap and verify its stability. METHODS: In patients with ectropion caused by severe scar contracture following head and face burns, we employed a tunnel orbicularis oculi muscle flap technique, which involved creating a tunnel between the skin and the tarsal plate of the eyelid, mobilizing the orbicularis oculi muscle, and rotating it into this tunnel to provide stable adhesion of the upper and lower eyelids. Full-thickness skin grafting was then performed. The eyelids were examined postoperatively to determine whether reoperation was necessary and to monitor for any potential complications. RESULTS: This study included 26 patients and 46 eyes. No accidental disconnection occurred after eyelid adhesion, which lasted for an average of 21.87 ± 10.08 months before the eyelid adhesion was cut open. No complications or adverse reactions occurred, and the adhesions did not break unexpectedly. CONCLUSIONS: Repairing eyelid ectropion with the tunnel orbicularis oculi muscle flap is a simple procedure that immediately creates tension against upper and lower eyelid contractures, providing long-term stable adhesion. This method avoids structural disorders, such as eyelid margin scarring, minimally influences surrounding tissues, and has few postoperative complications. It holds great value for repairing eyelid tissue defects and warrants further study.

Wideband receive-coil array design using high-impedance amplifiers for broadband decoupling.

PURPOSE: Multinuclear MRI/S is of increasing interest. Currently, most multinuclear receive array coils are constructed by nesting multiple single-tuned array coils or using switching elements to control the operating frequency, in which case more than one set of conventional isolation preamplifiers and associated decoupling circuits is required. These conventional configurations rapidly become complicated when greater numbers of channels or nuclei are needed. In this work, a novel coil decoupling mechanism is proposed to enable broadband decoupling for array coils with one set of preamplifiers. METHODS: Instead of using conventional isolation preamplifiers, a high-input impedance preamplifier is proposed to create broadband decoupling of the array elements. A matching network consisting of a single inductor-capacitor-capacitor multi-tuned network and a wire-wound transformer was used to interface the surface coil to the high-impedance preamplifier. To validate the concept, the proposed configuration was compared to the conventional preamplifier decoupling configuration on both bench and scanner. RESULTS: 2 The approach can provide more than 15dB decoupling over a range of 25MHz, covering the Larmor frequencies of 23 Na and 2 H at 4.7T. This multi-tuned prototype obtained 61% and 76% of the imaging SNR at 2 H and 23 Na respectively, 76 and 89% in a higher loading test phantom, when compared to the conventional single-tuned preamplifier decoupling configuration. CONCLUSION: With the multinuclear array operation and decoupling achieved using only one layer of array coil and preamplifiers, this work provides a simple approach of building high element-count arrays to enable accelerated imaging or SNR improvement from multiple nuclei.

Risk prediction with imperfect survival outcome information from electronic health records.

Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Angstrom-scale ion channels towards single-ion selectivity.

Artificial ion channels with ion permeability and selectivity comparable to their biological counterparts are highly desired for efficient separation, biosensing, and energy conversion technologies. In the past two decades, both nanoscale and sub-nanoscale ion channels have been successfully fabricated to mimic biological ion channels. Although nanoscale ion channels have achieved intelligent gating and rectification properties, they cannot realize high ion selectivity, especially single-ion selectivity. Artificial angstrom-sized ion channels with narrow pore sizes <1 nm and well-defined pore structures mimicking biological channels have accomplished high ion conductivity and single-ion selectivity. This review comprehensively summarizes the research progress in the rational design and synthesis of artificial subnanometer-sized ion channels with zero-dimensional to three-dimensional pore structures. Then we discuss cation/anion, mono-/di-valent cation, mono-/di-valent anion, and single-ion selectivities of the synthetic ion channels and highlight their potential applications in high-efficiency ion separation, energy conversion, and biological therapeutics. The gaps of single-ion selectivity between artificial and natural channels and the connections between ion selectivity and permeability of synthetic ion channels are covered. Finally, the challenges that need to be addressed in this research field and the perspective of angstrom-scale ion channels are discussed.

Semi-supervised approach to event time annotation using longitudinal electronic health records.

Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-n consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.

Lipid remodeling in response to methionine stress in MDA-MBA-468 triple-negative breast cancer cells.

Methionine (Met) is an essential amino acid and critical precursor to the cellular methyl donor S-adenosylmethionine. Unlike nontransformed cells, cancer cells have a unique metabolic requirement for Met and are unable to proliferate in growth media where Met is replaced with its metabolic precursor, homocysteine. This metabolic vulnerability is common among cancer cells regardless of tissue origin and is known as "methionine dependence", "methionine stress sensitivity", or the Hoffman effect. The response of lipids to Met stress, however, is not well-understood. Using mass spectroscopy, label-free vibrational microscopy, and next-generation sequencing, we characterize the response of lipids to Met stress in the triple-negative breast cancer cell line MDA-MB-468 and its Met stress insensitive derivative, MDA-MB-468res-R8. Lipidome analysis identified an immediate, global decrease in lipid abundances with the exception of triglycerides and an increase in lipid droplets in response to Met stress specifically in MDA-MB-468 cells. Furthermore, specific gene expression changes were observed as a secondary response to Met stress in MDA-MB-468, resulting in a downregulation of fatty acid metabolic genes and an upregulation of genes in the unfolded protein response pathway. We conclude that the extensive changes in lipid abundance during Met stress is a direct consequence of the modified metabolic profile previously described in Met stress-sensitive cells. The changes in lipid abundance likely results in changes in membrane composition inducing the unfolded protein response we observe.

Efficient metal ion sieving in rectifying subnanochannels enabled by metal-organic frameworks.

Biological ion channels have remarkable ion selectivity, permeability and rectification properties, but it is challenging to develop artificial analogues. Here, we report a metal-organic framework-based subnanochannel (MOFSNC) with heterogeneous structure and surface chemistry to achieve these properties. The asymmetrically structured MOFSNC can rapidly conduct K+, Na+ and Li+ in the subnanometre-to-nanometre channel direction, with conductivities up to three orders of magnitude higher than those of Ca2+ and Mg2+, equivalent to a mono/divalent ion selectivity of 103. Moreover, by varying the pH from 3 to 8 the ion selectivity can be tuned further by a factor of 102 to 104. Theoretical simulations indicate that ion-carboxyl interactions substantially reduce the energy barrier for monovalent cations to pass through the MOFSNC, and thus lead to ultrahigh ion selectivity. These findings suggest ways to develop ion selective devices for efficient ion separation, energy reservation and power generation.

RAB27A-dependent release of exosomes by liver cancer stem cells induces Nanog expression in their differentiated progenies and confers regorafenib resistance.

BACKGROUND AND AIM: Regorafenib is a potent multikinase inhibitor for the second-line targeted therapy against hepatocellular carcinoma (HCC); however, drug resistance is emerging in clinical settings. Although cancer stem cells (CSCs) are considered as key determinate of drug sensitivity, it remains unclear how CSCs may communicate with the differentiated counterparts (non-CSC) to dictate therapeutic efficacy. Therefore, we sought to investigate the regorafenib resistance mechanism of CSCs in HCC. METHODS: We used sphere formation and soft agar colony formation assays to evaluate the stemness capacity of cancer cells. Cell viability assay was performed to detect the sensitivity of cancer cells to regorafenib. Real-time quantitative polymerase chain reaction and western blot were used to analyze gene expression. Mouse xenograft tumor model was performed to assess Regorafenib sensitivity in vivo. RESULTS: Exosomes are highly enriched in CSC supernatant compared with that of non-CSC, and RAB27A mediates exosome secretion from CSCs to maintain stem-like phenotype and regorafenib insensitivity. Moreover, exosomes released by CSCs upregulate the expression of Nanog in non-CSC, while depleting Nanog sensitizes non-CSC to regorafenib in the presence of CSC exosomes. Consistently, analysis of TCGA datasets reveals that RAB27A expression tightly correlates with Nanog in HCC tissues. More importantly, depletion of RAB27A downregulates Nanog expression and sensitizes cancer cells to regorafenib in nude mice. CONCLUSIONS: Our findings suggest that CSCs release exosomes in a RAB27A-dependent manner to induce Nanog expression and regorafenib resistance in differentiated cells, targeting this exosome signaling between distinct cellular subsets may be a potential therapeutic strategy for HCC patients.

Association of hyperuricemia and hypertension phenotypes in hypertensive patients without uric acid lowering treatment.

BACKGROUND: Current study was to evaluate the association of hypertensive and hypertension phenotypes in hypertensive populations. METHODS: Patients with primary hypertension and without any uric acid (UA)-lowering treatment were enrolled. Baseline characteristics including office blood pressure (OBP), 24 h ambulatory blood pressure (ABP), and serum UA (SUA) were measured. According to SUA, patients were divided into normal SUA and hyperuricemia groups. Based on OBP and 24 h-ABP, hypertension phenotypes were classified as controlled hypertension (CH), white-coat uncontrolled hypertension (WCUH), masked uncontrolled hypertension (MUCH), and sustained uncontrolled hypertension (SUCH). RESULTS: Compared to patients with normal SUA (n = 336), patients with hyperuricemia (n = 284) were older and more likely to be men, obese, physically inactive, and have a higher prevalence of diabetes. C-reactive protein (CRP) level was higher in patients with hyperuricemia. The prevalence of CH, WCUH, and MUCH was similar between these two groups. However, the prevalence of SUCH was higher in patients with hyperuricemia than patients with normal SUA. Linear regression analysis indicated that increased SUA was significantly associated with 24 h-systolic BP and daytime-systolic BP. Normal SUA was served as the reference group, and presence of hyperuricemia was associated with higher odds of SUCH (odds ratio 1.46 and 95% confidence interval 1.27-1.93) after adjusted for potential covariates including age, male gender, obesity, diabetes, CRP, and antihypertensive drugs. CONCLUSION: In hypertensive patients without UA-lowering treatment, presence of hyperuricemia was associated with higher odds of SUCH. Future studies are needed to evaluate whether lowering SUA can help to improve 24 h-ABP control.

Kinetic Analysis of Lipid Metabolism in Breast Cancer Cells via Nonlinear Optical Microscopy.

Investigating the behavior of breast cancer cells via reaction kinetics may help unravel the mechanisms that underlie metabolic changes in tumors. However, obtaining human in vivo kinetic data is challenging because of difficulties associated with measuring these parameters. Nondestructive methods of measuring lipid content in live cells provide a novel approach to quantitatively model lipid synthesis and consumption. In this study, coherent Raman scattering microscopy was used to probe de novo intracellular lipid content. Combining nonlinear optical microscopy and Michaelis-Menten kinetics-based simulations, we isolated fatty acid synthesis/consumption rates and elucidated effects of altered lipid metabolism in T47D breast cancer cells. When treated with 17ß-estradiol, the lipid utilization in cancer cells jumped by twofold. Meanwhile, the rate of de novo lipid synthesis in cancer cells treated with 17ß-estradiol was increased by 42%. To test the model in extreme metabolic conditions, we treated T47D cells with etomoxir. Our kinetic analysis demonstrated that the rate of key enzymatic reactions dropped by 75%. These results underline the capability to probe lipid alterations in live cells with minimum interruption and to characterize lipid metabolism in breast cancer cells via quantitative kinetic models and parameters.

Sulfonated Sub-1-nm Metal-Organic Framework Channels with Ultrahigh Proton Selectivity.

Biological proton channels are sub-1-nm protein pores with ultrahigh proton (H+) selectivity over other ions. Inspired by biological proton channels, developing artificial proton channels with biological-level selectivity is of fundamental significance for separation science. Herein we report synthetic proton channels fabrication based on sulfonated metal-organic frameworks (MOFs), UiO-66-X, X = SAG, NH-SAG, (NH-SAG)2 (SAG: sulfonic acid groups), which have sub-1-nm windows and a high density of sulfonic acid groups mimicking natural proton channels. The ion conductance of UiO-66-X channels follows the sequence: H+ ≫ K+ > Na+> Li+, and the sulfonated UiO-66 derivative channels show proton selectivity much higher than that of the pristine UiO-66 channels. Particularly, the UiO-66-(NH-SAG)2 channels exhibit ultrahigh proton selectivities, H+/Li+ up to ∼100, H+/Na+ of ∼80, and H+/K+ of ∼70, which are ∼3 times of that of UiO-66-NH-SAG channels, and ∼15 times of that of UiO-66@SAG channels. The ultrahigh proton selectivity in the sulfonated sub-1-nm MOF channels is mainly attributed to the narrow window-cavity pore structure functionalized with nanoconfined high-density sulfonic acid groups that facilitate fast proton transport and simultaneously exclude other cations. Our work opens an avenue to develop functional MOF channels for selective ion conduction and efficient ion separation.

Microvesicles from malaria-infected red blood cells activate natural killer cells via MDA5 pathway.

Natural killer (NK) cells provide the first line of defense against malaria parasite infection. However, the molecular mechanisms through which NK cells are activated by parasites are largely unknown, so is the molecular basis underlying the variation in NK cell responses to malaria infection in the human population. Here, we compared transcriptional profiles of responding and non-responding NK cells following exposure to Plasmodium-infected red blood cells (iRBCs) and identified MDA5, a RIG-I-like receptor involved in sensing cytosolic RNAs, to be differentially expressed. Knockout of MDA5 in responding human NK cells by CRISPR/cas9 abolished NK cell activation, IFN-γ secretion, lysis of iRBCs. Similarly, inhibition of TBK1/IKKε, an effector molecule downstream of MDA5, also inhibited activation of responding NK cells. Conversely, activation of MDA5 by liposome-packaged poly I:C restored non-responding NK cells to lyse iRBCs. We further show that microvesicles containing large parasite RNAs from iRBCs activated NK cells by fusing with NK cells. These findings suggest that NK cells are activated through the MDA5 pathway by parasite RNAs that are delivered to the cytoplasm of NK cells by microvesicles from iRBCs. The difference in MDA5 expression between responding and non-responding NK cells following exposure to iRBCs likely contributes to the variation in NK cell responses to malaria infection in the human population.

Development and evaluation of a Novel RT-PCR system for reliable and rapid SARS-CoV-2 screening of blood donations.

BACKGROUND: The ongoing outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused great global concerns. In contrast to SARS, some SARS-CoV-2-infected people can be asymptomatic or have only mild nonspecific symptoms. Furthermore, there is evidence that SARS-CoV-2 may be infectious during an asymptomatic incubation period. With the discovery that SARS-CoV-2 can be detected in plasma or serum, blood safety is worthy of consideration. STUDY DESIGN AND METHODS: We developed a nucleic acid test (NAT) screening system for SARS-CoV-2 targeting nucleocapsid protein (N) and open reading frame 1ab (ORF 1ab) gene that could screen 5076 samples every 24 hours. The 2019 novel coronavirus RNA standard was used to evaluate linearity of standard curves. Diagnostic sensitivity and reproducibility were evaluated using artificial SARS-CoV-2. Specificity was evaluated with 61 other respiratory pathogens. Diagnostic performance was evaluated by testing two sputum and nine oropharyngeal swab specimens. The reverse transcription polymerase chain reaction (RT-PCR) assay was used to screen SARS-CoV-2 RNA in blood donor specimens collected during the outbreak of SARS-CoV-2 in Chengdu. RESULTS: Limits of detection of the SARS-CoV-2 RT-PCR assay for N and ORF 1ab gene were 12.5 and 27.58 copies/mL, respectively. Intra-assay and interassay for the SARS-CoV-2 RT-PCR assay based on cycle threshold were acceptably low. No cross-reactivity was observed with other respiratory virus and bacterial isolates. The overall agreement value between the SARS-CoV-2 RT-PCR assay and clinical diagnostic results was 100%. A total of 16 287 blood specimens collected from blood donors during SARS-CoV-2 surveillance were tested negative. CONCLUSIONS: A high-throughput NAT screening system was developed for SARS-CoV-2 screening of blood donations during the outbreak of SARS-CoV-2.

CDCP1 drives triple-negative breast cancer metastasis through reduction of lipid-droplet abundance and stimulation of fatty acid oxidation.

Triple-negative breast cancer (TNBC) is notoriously aggressive with high metastatic potential, which has recently been linked to high rates of fatty acid oxidation (FAO). Here we report the mechanism of lipid metabolism dysregulation in TNBC through the prometastatic protein, CUB-domain containing protein 1 (CDCP1). We show that a "low-lipid" phenotype is characteristic of breast cancer cells compared with normal breast epithelial cells and negatively correlates with invasiveness in 3D culture. Using coherent anti-Stokes Raman scattering and two-photon excited fluorescence microscopy, we show that CDCP1 depletes lipids from cytoplasmic lipid droplets (LDs) through reduced acyl-CoA production and increased lipid utilization in the mitochondria through FAO, fueling oxidative phosphorylation. These findings are supported by CDCP1's interaction with and inhibition of acyl CoA-synthetase ligase (ACSL) activity. Importantly, CDCP1 knockdown increases LD abundance and reduces TNBC 2D migration in vitro, which can be partially rescued by the ACSL inhibitor, Triacsin C. Furthermore, CDCP1 knockdown reduced 3D invasion, which can be rescued by ACSL3 co-knockdown. In vivo, inhibiting CDCP1 activity with an engineered blocking fragment (extracellular portion of cleaved CDCP1) lead to increased LD abundance in primary tumors, decreased metastasis, and increased ACSL activity in two animal models of TNBC. Finally, TNBC lung metastases have lower LD abundance than their corresponding primary tumors, indicating that LD abundance in primary tumor might serve as a prognostic marker for metastatic potential. Our studies have important implications for the development of TNBC therapeutics to specifically block CDCP1-driven FAO and oxidative phosphorylation, which contribute to TNBC migration and metastasis.

Efficient Gating of Ion Transport in Three-Dimensional Metal-Organic Framework Sub-Nanochannels with Confined Light-Responsive Azobenzene Molecules.

1D nanochannels modified with responsive molecules are fabricated to replicate gating functionalities of biological ion channels, but gating effects are usually weak because small molecular gates cannot efficiently block the large channels in the closed states. Now, 3D metal-organic framework (MOF) sub-nanochannels (SNCs) confined with azobenzene (AZO) molecules achieve efficient light-gating functionalities. The 3D MOFSNCs consisting of a MOF UiO66 with ca. 9-12 Šcavities connected by ca. 6 Štriangular windows work as angstrom-scale ion channels, while confined AZO within the MOF cavities function as light-driven molecular gates to efficiently regulate the ion flux. The AZO-MOFSNCs show good cyclic gating performance and high on-off ratios up to 17.8, an order of magnitude higher than ratios observed in conventional 1D AZO-modified nanochannels (1.3-1.5). This work provides a strategy to develop highly efficient switchable ion channels based on 3D porous MOFs and small responsive molecules.

Antiviral activity of mitoxantrone dihydrochloride against human herpes simplex virus mediated by suppression of the viral immediate early genes.

BACKGROUND: HSV-1 is a common pathogen that infects 50-90% of the human population worldwide. HSV-1 causes numerous infection-related diseases, some of which are severely life-threatening. There are antiviral medications with activity against HSV-1. However, with the emergence of drug-resistant mutant strains of HSV-1, there is an urgent need to develop new effective anti-HSV-1 agents. METHODS: Therefore, we screened a chemical library of approximately 1500 compounds to identify inhibitors of HSV-1-induced toxicity for further drug development. Moreover, we performed several experiments, including western blot analysis, Q-PCR analysis and luciferase activity assay, to explore the antiviral mechanism of the candidates. RESULTS: Here, we identified a small molecule, mitoxantrone dihydrochloride, with potency against HSV-1-induced toxicity. Furthermore, the viral titers and expression levels of HSV-1 viral proteins were potently reduced by the presence of MD in many cell lines. Using Q-PCR analysis, we found that MD efficiently reduced the transcription of viral genes that are essential for DNA synthesis, namely, UL5, UL9, UL29, UL30, UL42 and UL52. Notably, MD also significantly inhibited the transcription of the immediate early genes ICP0, ICP22, ICP27 and ICP47, all of which are required for the expression of early and late viral gene products. Using immunofluorescence and western blot analysis, we found that the antiviral effect of MD was independent of the activation of the NF-κB and MAPK pathways. Furthermore, we found that the reduction in the transcription of viral immediate early genes was not related to the promoter activities of ICP0. CONCLUSIONS: Therefore, the identification of compound MD as an inhibitor of toxicity induced by HSV-1 highlights its potential use in the development of novel anti-HSV-1 drugs.