ABBV-319: a CD19-targeting glucocorticoid receptor modulator antibody-drug conjugate therapy for B-cell malignancies.

ABSTRACT: Glucocorticoids are key components of the standard-of-care treatment regimens for B-cell malignancy. However, systemic glucocorticoid treatment is associated with several adverse events. ABBV-319 is a CD19-targeting antibody-drug conjugate engineered to reduce glucocorticoid-associated toxicities while possessing 3 distinct mechanisms of action (MOA) to increase therapeutic efficacy: (1) antibody-mediated delivery of a glucocorticoid receptor modulator (GRM) payload to activate apoptosis, (2) inhibition of CD19 signaling, and (3) enhanced fragment crystallizable (Fc)-mediated effector function via afucosylation of the antibody backbone. ABBV-319 elicited potent GRM-driven antitumor activity against multiple malignant B-cell lines in vitro, as well as in cell line-derived xenografts and patient-derived xenografts (PDXs) in vivo. Remarkably, a single dose of ABBV-319 induced sustained tumor regression and enhanced antitumor activity compared with repeated dosing of systemic prednisolone at the maximum tolerated dose in mice. The unconjugated CD19 monoclonal antibody (mAb) also displayed antiproliferative activity in a subset of B-cell lymphoma cell lines through the inhibition of phosphoinositide 3-kinase signaling. Moreover, afucosylation of CD19 mAb enhanced Fc-mediated antibody-dependent cellular cytotoxicity. Notably, ABBV-319 displayed superior efficacy compared with afucosylated CD19 mAb in human CD34+ peripheral blood mononuclear cell-engrafted NSG-Tg(Hu-IL15) transgenic mice, demonstrating enhanced antitumor activity when multiple MOAs are enabled. ABBV-319 also showed durable antitumor activity across multiple B-cell lymphoma PDX models, including nongerminal center B-cell diffuse large B-cell lymphoma and relapsed lymphoma after R-CHOP treatment. Collectively, these data support the ongoing evaluation of ABBV-319 in a phase 1 clinical trial.

Risk of kidney and liver diseases after COVID-19 infection: A systematic review and meta-analysis.

COVID-19 is not only associated with substantial acute liver and kidney injuries, but also with an elevated risk of post-acute sequelae involving the kidney and liver system. We aimed to investigate whether COVID-19 exposure increases the long-term risk of kidney and liver disease, and what are the magnitudes of these associations. We searched PubMed, Embase, Web of Science, ClinicalTrials.gov, and the Living Overview of the Evidence COVID-19 Repository for cohort studies estimating the association between COVID-19 and kidney and liver outcomes. Random-effects meta-analyses were performed to combine the results of the included studies. We assessed the certainty of the evidence using the Grading of Recommendations Assessment, Development and Evaluation approach. Fifteen cohort studies with more than 32 million participants were included in the systematic review COVID-19 was associated with a 35% greater risk of kidney diseases (10 more per 1000 persons; low certainty evidence) and 54% greater risk of liver disease (3 more per 1000 persons; low certainty evidence). The absolute increases due to COVID-19 for acute kidney injury, chronic kidney disease, and liver test abnormality were 3, 8, and 3 per 1000 persons, respectively. Subgroup analyses found no differences between different type of kidney and liver diseases. The findings provide further evidence for the association between COVID-19 and incident kidney and liver conditions. The absolute magnitude of the effect of COVID-19 on kidney and liver outcomes was, however, relatively small.

Engineering Oncolytic Coxsackievirus A21 with Small Transgenes and Enabling Cell-Mediated Virus Delivery by Integrating Viral cDNA into the Genome.

Coxsackievirus A21 (CVA21) is a naturally occurring RNA virus that, in preclinical studies and clinical trials, has demonstrated promising potential in treating a range of malignancies. Other oncolytic viruses, such as adenovirus, vesicular stomatitis virus, herpesvirus, and vaccinia virus, all can be engineered to carry one or more transgenes for various purposes, including immune modulation, virus attenuation, and induction of apoptosis of tumor cells. However, it remained unknown whether CVA21 can express therapeutic or immunomodulatory payloads due to its small size and high mutation rate. Using reverse genetics techniques, we demonstrated that a transgene encoding a truncated green fluorescent protein (GFP) of up to 141 amino acids (aa) can be successfully carried in the 5' end of the coding region. Furthermore, a chimeric virus carrying an eel fluorescent protein, UnaG (139 aa), was also made and shown to be stable, and it maintained efficient tumor cell-killing activity. Similar to other oncolytic viruses, the likelihood of delivering CVA21 by the intravenous route is low due to issues like blood absorption, neutralizing antibodies, and liver clearance. To address this problem, we designed the CVA21 cDNA under the control of a weak RNA polymerase II promoter, and subsequently, a stable cell pool in 293T cells was made by integrating the resulting CVA21 cDNA into the cell genome. We showed that the cells are viable and able to persistently generate rCVA21 de novo. The carrier cell approach described here may pave the way to designing new cell therapy strategies by arming with oncolytic viruses. IMPORTANCE As a naturally occurring virus, coxsackievirus A21 is a promising oncolytic virotherapy modality. In this study, we first used reverse genetics to determine whether A21 can stably carry transgenes and found that it could express up to 141 amino acids of foreign GFP. The chimeric virus carrying another fluorescent eel protein UnaG (139 amino acids) gene also appeared to be stable over at least 7 passages. Our results provided guidance on how to select and engineer therapeutic payloads for future A21 anticancer research. Second, the challenges of delivering oncolytic viruses by the intravenous route hamper the broader use of oncolytic viruses in the clinic. Here, we used A21 to show that cells could be engineered to stably carry and persistently release the virus by harboring the viral cDNA in the genome. The approach we presented here may pave a new way for oncolytic virus administration using cells as carriers.

Preparation and chromatographic evaluation of hydrophilic polymer brushes grafted-silica with post modification of silicon/carbon dots as a green liquid chromatography stationary phase.

Polar stationary phases were prepared by grafting hydrophilic acrylamide (Am) polymer brushes with post modification of carbon dots (CDs) and silicon dots (SiDs) onto SiO2 particles. The prepared stationary phases, SiO2-PAm-CDs, SiO2-PAm-CDs/SiDs, and SiO2-PAm-SiDs, were packed as chromatographic columns, respectively. Using nucleic bases, organic acids, and ß-agonists as target substances to investigate the influence of chromatographic conditions on retention and separation, the packed columns showed the partitioning and adsorption of mixed retention behavior in hydrophilic interaction liquid chromatography mode and successfully separated the polar compounds. Most importantly, under per aqueous liquid chromatography mode (using 100% water as mobile phase), those columns still had good separation ability toward nucleic bases, ß-agonist, and organic acids. Because AM is a temperature-sensitive monomer, the resulting van't Hoff curves exhibited a nonlinear relationship, having temperature-responsive chromatographic characteristic under pure water separation. Hence, building on temperature-sensitive characteristics and pure water of separation conditions, the separation selectivity toward hydrophilic compounds greatly improved. Compared with the commercial hydrophilic columns, the efficiency of our developed column had the superior ability in separation and detection of betaine in Goji berry with the enhanced resolution achieved in the proposed green separation method (just using pure water as mobile phase).

Changes in Heart Rate after Pulmonary Vein Isolation in Patients with Paroxysmal Atrial Fibrillation and Sinus Bradycardia.

This study aimed to evaluate the effect of circumferential pulmonary vein isolation (CPVI) on autonomic nervous function and prognosis in patients with paroxysmal atrial fibrillation (AF) with or without sinus bradycardia.A total of 66 patients with paroxysmal AF accompanied by sinus bradycardia and who underwent CPVI were recruited as the sinus bradycardia group. A total of 91 patients with paroxysmal AF but without sinus bradycardia and who underwent catheter ablation were selected as the control group. After surgical contraindications were eliminated, CPVI was performed by three-dimensional mapping system. 24-hour dynamic electrocardiogram was used to observe the changes of heart rate before and 2 days after surgery.A total of 45 (68%) and 51 (56%) patients in the sinus bradycardia and control groups, respectively, maintained sinus rhythm. There was an increase in heart rate after CPVI in both groups. The standard deviation of normal-to-normal (NN) intervals (SDNN), standard deviation of the average NN intervals (SDANN), low frequency (LF), and LF/high frequency (HF) in the sinus bradycardia and control groups decreased after CPVI (P < 0.01). Moreover, SDANN was higher in patients with sinus bradycardia treated by successful ablation than in those with recurrence (P < 0.01), while SDNN, a standard statistical measure of heart rate variability (rMSSD), LF, and HF were significantly lower in patients with sinus bradycardia (P < 0.05).CPVI was able to produce a significant reduction effect on vagal nerve and sympathetic activity regardless of whether patients with paroxysmal AF had sinus bradycardia. Moreover, CPVI exerted a certain influence on the success rate of AF catheter ablation.

Structure-Activity Relationships of Hydroxyapatite-Binding Peptides.

Elucidating the structure-activity relationships between biomolecules and hydroxyapatite (HAP) is essential to understand bone mineralization mechanisms, develop HAP-based implants, and design drug delivery vectors. Here, four peptides identified by phage display were selected as model HAP-binding peptides (HBPs) to examine the effects of primary amino acid sequence, phosphorylation of serine, presence of charged amino acid residues, and net charge of the peptide on (1) HAP-binding affinity, (2) secondary conformation, and (3) HAP nucleation and crystal growth. Binding affinities were determined by obtaining adsorption isotherms by mass depletion, and the conformations of the peptides in solution and bound states were observed by circular dichroism. Results showed that the magnitude of the net charge primarily controlled binding affinity, with little dependence on the other HBP features. The binding affinity and conformation results were in good agreement with our previous molecular dynamics simulation results, thus providing an excellent benchmark for the simulations. Transmission electron microscopy was used to explore the effect of these HBPs on calcium phosphate (Ca-PO4) nucleation and growth. Results indicated that HBPs may inhibit nucleation of Ca-PO4 nanoparticles and their phase transition to crystalline HAP, as well as control crystal growth rates in specific crystallographic directions, thus changing the classical needle-like morphology of inorganically grown HAP crystals to a biomimetic plate-like morphology.

Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.

A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

Method Evaluations for Adsorption Free Energy Calculations at the Solid/Water Interface through Metadynamics, Umbrella Sampling, and Jarzynski's Equality.

Considerable interest in characterizing protein/peptide-surface interactions has prompted extensive computational studies on calculations of adsorption free energy. However, in many cases, each individual study has focused on the application of free energy calculations to a specific system; therefore, it is difficult to combine the results into a general picture for choosing an appropriate strategy for the system of interest. Herein, three well-established computational algorithms are systemically compared and evaluated to compute the adsorption free energy of small molecules on two representative surfaces. The results clearly demonstrate that the characteristics of studied interfacial systems have crucial effects on the accuracy and efficiency of the adsorption free energy calculations. For the hydrophobic surface, steered molecular dynamics exhibits the highest efficiency, which appears to be a favorable method of choice for enhanced sampling simulations. However, for the charged surface, only the umbrella sampling method has the ability to accurately explore the adsorption free energy surface. The affinity of the water layer to the surface significantly affects the performance of free energy calculation methods, especially at the region close to the surface. Therefore, a general principle of how to discriminate between methodological and sampling issues based on the interfacial characteristics of the system under investigation is proposed.

Quantitatively Identifying the Roles of Interfacial Water and Solid Surface in Governing Peptide Adsorption.

Understanding the molecular mechanism of protein adsorption on solids is critical to their applications in materials synthesis and tissue engineering. Although the water phase at the surface/water interface has been recognized as three types: bulk water, intermediate water phase and surface-bound water layers, the roles of the water and surface in determining the protein adsorption are not clearly identified, particularly at the quantitative level. Herein, we provide a methodology involving the combination of microsecond strengthen sampling simulation and force integration to quantitatively characterize the water-induced contribution and the peptide-surface interactions into the adsorption free energy. Using hydroxyapatite and graphene surfaces as examples, we demonstrate how the distinct interfacial features dominate the delicate force balance between these two thermodynamics parameters, leading to surface preference/resistance to peptide adsorption. Specifically, the water layer provides sustained repelling force against peptide adsorption, as indicated by a monotonic increase in the water-induced free energy profile, whereas the contribution from the surface-peptide interactions is thermodynamically favorable to peptide adsorptions. More importantly, the revealed adsorption mechanism is critically dictated by the distribution of water phase, which plays a crucial role in establishing the force balance between the interactions of the peptide with the water layer and the surface. For the HAP surface, the charged peptide exhibits strong binding affinity to the surface, due to the controlling contribution of peptide-surface interaction in the intermediate water phase. The surface-bound water layers are observed as the origin of bioresistance of solid surfaces toward the adsorption of charge-neutral peptides. The preferred peptide adsorption on the graphene, however, is dominated by the surface-induced component at the water layers adjacent to the surface. Our results further elucidate that the intermediate water phase significantly shortens the effective range of the surface dispersion force, in contrast to the observation on the hydrophilic surface.

Osteocalcin facilitates calcium phosphate ion complex growth as revealed by free energy calculation.

The nanoscopic structural and thermodynamic basis of biomolecule-regulated assembly and crystallization of inorganic solids have a tremendous impact on the rational design of novel functional nanomaterials, but are concealed by many difficulties in molecular-level characterization. Here we demonstrate that the free energy calculation approach, enabled by combining advanced molecular simulation techniques, can unravel the structural and energetic mechanisms of protein-mediated inorganic solid nucleation. It is observed that osteocalcin (OCN), an important non-collagenous protein involved in regulating bone formation, promotes the growth of nanosized calcium phosphate (CaP) ion clusters from a supersaturated solution. Free energy calculation by umbrella sampling indicates that this effect by OCN is prominent at the scale of 1 to 3 nm ion-association complexes (IACs). The binding interactions between gamma-carboxyl glutamate and the C-terminal and, interestingly, the arginine side chains of OCN and IACs stabilize under-coordinated IACs, thus promoting their growth. The promoter effect of OCN on the enlargement and further aggregation of IACs into cluster assemblies of tens of nm are confirmed by conventional molecular dynamics simulation and dynamic light scattering experiments. To the best of our knowledge, this is the first time that the free energy landscape of the early stages of CaP nucleation is shown. The free energy change as a function of IAC size shares the feature of decreasing monotonically as shown previously for the calcium carbonate system. Therefore, the nucleation of both these major biominerals apparently involves an initial phase of liquid-like ionic aggregates. The structural and thermodynamic information regarding OCN-CaP interactions amplifies the current understanding of biomineralization mechanisms at the nanoscale, with general relevance to biomolecule-tuned fabrication of inorganic materials.

Structure analysis of collagen fibril at atomic-level resolution and its implications for intra-fibrillar transport in bone biomineralization.

Bone is a hierarchical biocomposite material in which a collagen fibril matrix self-assembled in a three-dimensional (3-D) pseudohexagonal array controls many important processes in mineralization such as providing the pathways by which calcium and phosphate species are delivered and a template for the earliest nucleation sites, determining the spatial distribution of the mineral and the topology for binding of associated non-collagenous proteins. However, the structural characteristics of collagen molecules in the fibril remain unclear at the atomic level. Here we performed the first large-scale molecular dynamics simulations to provide a comprehensive all-atom structural analysis of the entire fibril of Type I collagen including intra-fibrillar water distribution. We found that the ideal fibril structure is preserved in specific sites where the earliest nucleation occurs, but is severely distorted in areas that mineralize later. In detail, the ideal pseudohexagonal structure is well-preserved in the overlap zone (c1, c2 and b bands), in the a bands of the hole zone but is severely distorted at the hole/overlap transition (d and c3 bands). As a result, the expected uniform "channel," formed by connecting holes in adjacent unit cells along the b-axis, and having dimensions of 1.5 nm height along the a-axis and width of 40 nm along the c-axis is not formed. The expected uniform channel of 1.5 nm height is preserved only in the a bands in a narrow sub-channel region only 5.8 nm wide. At the hole/overlap transition, an irregular, tortuous sub-channel of widely varying dimensions (∼1.8-4.0 nm height × âˆ¼3.0 nm width) is formed. The well-defined sub-channel in the a bands along with their preferred orientation of charged amino acid residues could facilitate faster molecular diffusion than the tortuous sub-channels and ionic interactions, thus providing the first nucleation sites. Intra-fibrillar water occupies nano-spaces and shows low density (∼0.7 g cm-3), which should promote dehydration of ion species. These results provide the first atomic-level understanding of the structure of the collagen fibril and the properties of the aqueous compartments within the fibril, which offer a physical, chemical and steric explanation for calcium phosphate infiltration paths and for the initiation of mineralization at the a band collagen fibril. The mechanism revealed here for the observed specificity of collagen biomineralization in bone formation ultimately contributes to the biochemical and biomechanical functions of the skeleton.

Recent Development of pH-Responsive Polymers for Cancer Nanomedicine.

Cancer remains a leading cause of death worldwide with more than 10 million new cases every year. Tumor-targeted nanomedicines have shown substantial improvements of the therapeutic index of anticancer agents, addressing the deficiencies of conventional chemotherapy, and have had a tremendous growth over past several decades. Due to the pathophysiological characteristics that almost all tumor tissues have lower pH in comparison to normal healthy tissues, among various tumor-targeted nanomaterials, pH-responsive polymeric materials have been one of the most prevalent approaches for cancer diagnosis and treatment. In this review, we summarized the types of pH-responsive polymers, describing their chemical structures and pH-response mechanisms; we illustrated the structure-property relationships of pH-responsive polymers and introduced the approaches to regulating their pH-responsive behaviors; we also highlighted the most representative applications of pH-responsive polymers in cancer imaging and therapy. This review article aims to provide general guidelines for the rational design of more effective pH-responsive nanomaterials for cancer diagnosis and treatment.

Predicting the Structure-Activity Relationship of Hydroxyapatite-Binding Peptides by Enhanced-Sampling Molecular Simulation.

Understanding the molecular structural and energetic basis of the interactions between peptides and inorganic surfaces is critical to their applications in tissue engineering and biomimetic material synthesis. Despite recent experimental progresses in the identification and functionalization of hydroxyapatite (HAP)-binding peptides, the molecular mechanisms of their interactions with HAP surfaces are yet to be explored. In particular, the traditional method of molecular dynamics (MD) simulation suffers from insufficient sampling at the peptide-inorganic interface that renders the molecular-level observation dubious. Here we demonstrate that an integrated approach combining bioinformatics, MD, and metadynamics provides a powerful tool for investigating the structure-activity relationship of HAP-binding peptides. Four low charge density peptides, previously identified by phage display, have been considered. As revealed by bioinformatics and MD, the binding conformation of the peptides is controlled by both the sequence and the amino acid composition. It was found that formation of hydrogen bonds between lysine residue and phosphate ions on the surface dictates the binding of positively charged peptide to HAP. The binding affinities of the peptides to the surface are estimated by free energy calculation using parallel-tempering metadynamics, and the results compare favorably to measurements reported in previous experimental studies. The calculation suggests that the charge density of the peptide primarily controls the binding affinity to the surface, while the backbone secondary structure that may restrain side chain orientation toward the surface plays a minor role. We also report that the application of enhanced-sampling metadynamics effects a major advantage over the steered MD method by significantly improving the reliability of binding free energy calculation. In general, our novel integration of diverse sampling techniques should contribute to the rational design of surface-recognition peptides in biomedical applications.

Surface energetics of the hydroxyapatite nanocrystal-water interface: a molecular dynamics study.

Face-specific interfacial energies and structures of water at ionic crystal surfaces play a dominant role in a wide range of biological, environmental, technological, and industrial processes. Nanosized, plate-shaped crystals of calcium phosphate (CaP) with nonideal stoichiometry of hydroxyapatite (HAP, ideal stoichiometry Ca10(PO4)6(OH)2) comprise the inorganic component of bone and dentin. The crystal shape and size contribute significantly to these tissues' biomechanical properties. Plate-shaped HAP can be grown in the presence of biomolecules, whereas inorganically grown HAP crystals have a needlelike shape. Crystal morphology reflects the relative surface areas of the faces and, for an ideal inorganically grown crystal, should be governed by the surface energies of the faces with water. Interfacial energies and dynamics also affect biomolecule adsorption. Obtaining face-specific surface energies remains experimentally challenging because of the difficulty in growing large HAP single crystals. Here we employed molecular dynamics (MD) simulations to determine nanocrystalline HAP-water interfacial energies. The (100) face was found to be the most favorable energetically, and (110) and (004) were less hydrophilic. The trend in increasing interfacial energy was accompanied by a decrease in the average coordination number of water oxygen to surface calcium ions. The atomic-level geometry of the faces influenced interfacial energy by limiting lateral diffusion of water and by interrupting the hydrogen bond network. Such unfavorable interactions were limited on (100) compared to the other faces. These results provide a thermodynamic basis for the empirically observed trends in relative surface areas of HAP faces. The penetration of charged biomolecules through the interfacial water to form direct interactions with HAP faces, thus potentially influencing morphology, can also be rationalized.

Zwitterionic polymers grafting of metal organic framework encapsulated boronic acid carbon dots as antibiofouling fluorescent probe for baicalin monitoring.

The sensitivity and accuracy of fluorescence probes for biological samples are affected by not only interfering molecule compounds but also the nonspecific adsorption of proteins and other macromolecules. Herein, fluorescence probe based on zwitterionic sulfobetaine methacrylate polymer (PSBMA) as an antibiofouling layer and amino boric acid carbon dots encapsulated in the metal-organic framework UiO-66-NH2 (UiO-66-NH2/BN-CDs) as a target recognition site was designed for the detection of baicalin (BAI). Owing to the introduction of BN-CDs into UiO-66-NH2 with high specific surface area, the prepared UiO-66-NH2/BN-CDs@PSBMA probe exhibited a high adsorption capacity of 78.9 mg g-1, while presented fluorescence enhancing and superior fluorescence selectivity to BAI at excitation and emission wavelengths of 400 and 425 nm, respectively. Connecting PSBMA with good hydrophilicity to UiO-66-NH2, resulted in an anti-protein capacity of over 96.3 %, effectively inhibiting protein interference with the fluorescence signal. By virtue of its good antibiofouling and recognizing capacities, the fluorescence probe exhibited a satisfactory detection range of 10-80 nmol L-1, with a fairly low detection limit of 0.0064 µmol L-1. Using the method to detect BAI in Goji berry, Sophora and Yinhuang oral solution, demonstrating its potential for the accurate and quantitative detection of BAI in complex biological samples.

Overcoming Debye screening effect in field-effect transistors for enhanced biomarker detection sensitivity.

Field-effect transistor (FET)-based biosensors not only enable label-free detection by measuring the intrinsic charges of biomolecules, but also offer advantages such as high sensitivity, rapid response, and ease of integration. This enables them to play a significant role in disease diagnosis, point-of-care detection, and drug screening, among other applications. However, when FET sensors detect biomolecules in physiological solutions (such as whole blood, serum, etc.), the charged molecules will be surrounded by oppositely charged ions in the solution. This causes the effective charge carried by the biomolecules to be shielded, thereby significantly weakening their ability to induce charge rearrangement at the sensing interface. Such shielding hinders the change of carriers inside the sensing material, reduces the variation of current between the source and drain electrodes of the FET, and seriously limits the sensitivity and reliability of the device. In this article, we summarize the research progress in overcoming the Debye screening effect in FET-based biosensors over the past decade. Here, we first elucidate the working principles of FET sensors for detecting biomarkers and the mechanism of the Debye screening. Subsequently, we emphasize optimization strategies to overcome the Debye screening effect. Finally, we summarize and provide an outlook on the research on FET biosensors in overcoming the Debye screening effect, hoping to help the development of FET electronic devices with high sensitivity, specificity, and stability. This work is expected to provide new ideas for next-generation biosensing technology.

Internal multiple interactions-adsorption and external zwitterionic polymer-exclusion of restricted access materials as adsorbent for offline and online extraction of neonicotinoid pesticides in Goji samples.

A method based on novel restricted access materials (RAMs) for the determination of neonicotinoid pesticides in Goji samples using offline and online solid phase extraction (SPE) coupled with high-performance liquid chromatography (LC). RAMs were synthesized using poly(chloromethylstyrene-co-divinylbenzene) (PVBC/DVB) microspheres as substrate, styrene (St) and n-vinylpyrrolidone (NVP) were first copolymerized on the interior to construct adsorption sites, and sulfobetaine methacrylate (SBMA) was then polymerized on the exterior to form exclusion sites via two-step surface initiated-atom transfer polymerization. The prepared PVBC/DVB@poly(St-co-NVP)@poly(SBMA) RAMs could efficiently extract neonicotinoid pesticides and automatically exclude proteins. Under the optimized conditions, the developed methods of offline (magnetic SPE and SPE column) and online extraction coupled with LC both using PVBC/DVB@poly(St-co-NVP)@poly(SBMA) RAMs as the extractant, exhibit a wide linearity, low limits of detection and limit of quantification and good inter-day and intra-day precision with satisfactory recoveries. Among these methods, online extraction coupled with LC based on novel RAMs exhibits clear advantages for the determination of neonicotinoid pesticides in Goji samples has clear advantages, such as simple operation by direct injection, short extraction times, and high accuracy with less human error.

Sensitivity-Enhancing Strategies of Graphene Field-Effect Transistor Biosensors for Biomarker Detection.

The ultrasensitive recognition of biomarkers plays a crucial role in the precise diagnosis of diseases. Graphene-based field-effect transistors (GFET) are considered the most promising devices among the next generation of biosensors. GFET biosensors possess distinct advantages, including label-free, ease of integration and operation, and the ability to directly detect biomarkers in liquid environments. This review summarized recent advances in GFET biosensors for biomarker detection, with a focus on interface functionalization. Various sensitivity-enhancing strategies have been overviewed for GFET biosensors, from the perspective of optimizing graphene synthesis and transfer methods, refinement of surface functionalization strategies for the channel layer and gate electrode, design of biorecognition elements and reduction of nonspecific adsorption. Further, this review extensively explores GFET biosensors functionalized with antibodies, aptamers, and enzymes. It delves into sensitivity-enhancing strategies employed in the detection of biomarkers for various diseases (such as cancer, cardiovascular diseases, neurodegenerative disorders, infectious viruses, etc.) along with their application in integrated microfluidic systems. Finally, the issues and challenges in strategies for the modulation of biosensing interfaces are faced by GFET biosensors in detecting biomarkers.

Assessing the Risk of Bias in Randomized Clinical Trials With Large Language Models.

Importance: Large language models (LLMs) may facilitate the labor-intensive process of systematic reviews. However, the exact methods and reliability remain uncertain. Objective: To explore the feasibility and reliability of using LLMs to assess risk of bias (ROB) in randomized clinical trials (RCTs). Design, Setting, and Participants: A survey study was conducted between August 10, 2023, and October 30, 2023. Thirty RCTs were selected from published systematic reviews. Main Outcomes and Measures: A structured prompt was developed to guide ChatGPT (LLM 1) and Claude (LLM 2) in assessing the ROB in these RCTs using a modified version of the Cochrane ROB tool developed by the CLARITY group at McMaster University. Each RCT was assessed twice by both models, and the results were documented. The results were compared with an assessment by 3 experts, which was considered a criterion standard. Correct assessment rates, sensitivity, specificity, and F1 scores were calculated to reflect accuracy, both overall and for each domain of the Cochrane ROB tool; consistent assessment rates and Cohen κ were calculated to gauge consistency; and assessment time was calculated to measure efficiency. Performance between the 2 models was compared using risk differences. Results: Both models demonstrated high correct assessment rates. LLM 1 reached a mean correct assessment rate of 84.5% (95% CI, 81.5%-87.3%), and LLM 2 reached a significantly higher rate of 89.5% (95% CI, 87.0%-91.8%). The risk difference between the 2 models was 0.05 (95% CI, 0.01-0.09). In most domains, domain-specific correct rates were around 80% to 90%; however, sensitivity below 0.80 was observed in domains 1 (random sequence generation), 2 (allocation concealment), and 6 (other concerns). Domains 4 (missing outcome data), 5 (selective outcome reporting), and 6 had F1 scores below 0.50. The consistent rates between the 2 assessments were 84.0% for LLM 1 and 87.3% for LLM 2. LLM 1's κ exceeded 0.80 in 7 and LLM 2's in 8 domains. The mean (SD) time needed for assessment was 77 (16) seconds for LLM 1 and 53 (12) seconds for LLM 2. Conclusions: In this survey study of applying LLMs for ROB assessment, LLM 1 and LLM 2 demonstrated substantial accuracy and consistency in evaluating RCTs, suggesting their potential as supportive tools in systematic review processes.

Preparation and chromatographic evaluation of a mixed polymer brush-silica stationary phase with temperature-sensitive property.

In this study, a developed chromatographic stationary phase combines the high selectivity of mixed-mode retention with a temperature-responsive property to boost separation efficiency. Copolymer brushes were grafted onto silica gels through surface initiated-atom transfer radical polymerization by polymerizing two types of monomer, temperature-responsive vinylcaprolactam (VCl) and quinine (Qun) containing benzopyridine, a tertiary ammonium positive center, and hydroxyl groups. The obtained silica@poly(Qun-co-VCl) stationary phases were packed as a chromatographic column, and the retention behavior of hydrophobic polycyclic aromatics, highly polar nucleosides, charged organic acids and ß-agonists was studied for this column under different separation modes. The ability to separate different types of analyte shows that the silica@poly(Qun-co-VCl) column provides multiple hydrophobic, hydrophilic and electrostatic interactions toward analytes, achieving the separation of various compounds in one column. In addition, temperature-dependent resolution of polycyclic aromatics, nucleosides, organic acids and ß-agonists was investigated using modulation of the column temperature, and the column exhibited adjustable separation selectivity by simply changing the column temperature. These results demonstrate that the grafting of copolymer brushes on a silica surface, consisting of temperature-responsive poly-VCl and multifunctional groups of poly-Qun, is useful as a mixed-mode chromatographic stationary phase for thermally-modulated multiple interactions. Additionally, this column was also used for the quantitative detection of uridine and inosine from cordyceps.