3D printing of customized bioceramics for promoting bone tissue regeneration by regulating sympathetic nerve behavior.

Numerous studies have shown that there are multiple neural activities involved in the process of bone resorption and bone regeneration, and promoting osteogenesis by promoting neural network reconstruction is an effective strategy for repairing critical size bone defects. However, traumatic bone defects often cause activation of the sympathetic nervous system (SNS) in the damaged area, releasing excess catecholamines (CAs), resulting in a decrease in the rate of bone formation. Herein, a 3D-printed scaffold loaded with propranolol (PRN) is proposed to reduce CA concentrations in bone defect areas and promote bone regeneration through drug release. For this purpose, PRN-loaded methacrylated gelatin (GelMA) microspheres were mixed with low-concentration GelMA and perfused into a 3D-printed porous hydroxyapatite (HAp) scaffold. By releasing PRN, which can block ß-adrenergic receptors, it hinders the activation of sympathetic nerves and inhibits the release of excess CA by the SNS. At the same time, the composite scaffold recruits bone marrow mesenchymal stem cells (BMSCs) and promotes the differentiation of BMSCs in the direction of osteoblasts, which effectively promotes bone regeneration in the rabbit femoral condyle defect model. The results of the study showed that the release of PRN from the composite scaffold could effectively hinder the activation of sympathetic nerves and promote bone regeneration, providing a new strategy for the treatment of bone defects.

Nitrogen removal and carbon reduction of mature landfill leachate under extremely low dissolved oxygen conditions by simultaneous partial nitrification anammox and denitrification.

In this study, a SNAD-SBBR process was implemented to achieve ammonia removal and carbon reduction of mature landfill leachate under extremely low dissolved oxygen conditions (0.051 mg/L) for a continuous operation of 266 days. The process demonstrated excellent removal performance, with ammonia nitrogen removal efficiency reaching 100 %, total nitrogen removal efficiency reaching 87.56 %, and an average removal rate of 0.180 kg/(m3·d). The recalcitrant organic compound removal efficiency reached 34.96 %. Nitrogen mass balance analysis revealed that the Anammox process contributed to approximately 98.1 % of the nitrogen removal. Candidatus Kuenenia achieved a relative abundance of 1.49 % in the inner layer of the carrier. In the SNAD-SBBR system, the extremely low DO environment created by the highly efficient partial nitrification stage enabled the coexistence of AnAOB, denitrifying bacteria, and Nitrosomonas, synergistically achieving ammonia removal and carbon reduction. Overall, the SNAD-SBBR process exhibits low-cost and high-efficiency characteristics, holding tremendous potential for landfill leachate treatment.

Point-of-care testing of methamphetamine and cocaine utilizing wearable sensors.

The imperative for the point-of-care testing of methamphetamine and cocaine in drug abuse prevention necessitates innovative solutions. To address this need, we have introduced a multi-channel wearable sensor harnessing CRISPR/Cas12a system. A CRISPR/Cas12a based system, integrated with aptamers specific to methamphetamine and cocaine, has been engineered. These aptamers function as signal-mediated intermediaries, converting methamphetamine and cocaine into nucleic acid signals, subsequently generating single-stranded DNA to activate the Cas12 protein. Additionally, we have integrated a microfluidic system and magnetic separation technology into the CRISPR system, enabling rapid and precise detection of cocaine and methamphetamine. The proposed sensing platform demonstrated exceptional sensitivity, achieving a detection limit as low as 0.1 ng/mL. This sensor is expected to be used for on-site drug detection in the future.

Characteristics of saltiness-enhancing peptides derived from yeast proteins and elucidation of their mechanism of action by molecular docking.

This study aimed to identify saltiness-enhancing peptides from yeast protein and elucidate their mechanisms by molecular docking. Yeast protein hydrolysates with optimal saltiness-enhancing effects were prepared under conditions determined using an orthogonal test. Ten saltiness-enhancing peptide candidates were screened using an integrated virtual screening strategy. Sensory evaluation demonstrated that these peptides exhibited diverse taste characteristics (detection thresholds: 0.13-0.50 mmol/L). Peptides NKF, LGLR, WDL, NMKF, FDSL and FDGK synergistically or additively enhanced the saltiness of a 0.30% NaCl solution. Molecular docking revealed that these peptides predominantly interacted with TMC4 by hydrogen bonding, with hydrophilic amino acids from both peptides and TMC4 playing a pivotal role in their binding. Furthermore, Leu217, Gln377, Glu378, Pro474 and Cys475 were postulated as the key binding sites of TMC4. These findings establish a robust theoretical foundation for salt reduction strategies in food and provide novel insights into the potential applications of yeast proteins.

Construction of CDKN2A-related competitive endogenous RNA network and identification of GAS5 as a prognostic indicator for hepatocellular carcinoma.

BACKGROUND: Competitive endogenous RNA (ceRNA) is an innovative way of gene expression modulation, which plays a crucial part in neoplasia. However, the intricacy and behavioral characteristics of the ceRNA network in hepatocellular carcinoma (HCC) remain dismal. AIM: To establish a cyclin dependent kinase inhibitor 2A (CDKN2A)-related ceRNA network and recognize potential prognostic indicators for HCC. METHODS: The mutation landscape of CDKN2A in HCC was first explored using the cBioPortal database. Differential expression analysis was implemented between CDKN2Ahigh and CDKN2Alow expression HCC samples. The targeted microRNAs were predicted by lncBasev3.0, and the targeted mRNAs were predicted by miRDB, and Targetscan database. The univariate and multivariate analysis were utilized to identify independent prognostic indicators. RESULTS: CDKN2A was frequently mutated and deleted in HCC. The single-cell RNA-sequencing analysis revealed that CDKN2A participated in cell cycle pathways. The CDKN2A-related ceRNA network-growth arrest specific 5 (GAS5)/miR-25-3p/SRY-box transcription factor 11 (SOX11) was successfully established. GAS5 was recognized as an independent prognostic biomarker, whose overexpression was correlated with a poor prognosis in HCC patients. The association between GAS5 expression and methylation, immune infiltration was explored. Besides, traditional Chinese medicine effective components targeting GAS5 were obtained. CONCLUSION: This CDKN2A-related ceRNA network provides innovative insights into the molecular mechanism of HCC formation and progression. Moreover, GAS5 might be a significant prognostic biomarker and therapeutic target in HCC.

Reduction of monoclonal antibody viscosity using interpretable machine learning.

Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.

BACKGROUND: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. METHODS: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. RESULTS: Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms. CONCLUSION: SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Achieving advanced nitrogen removal from mature landfill leachate in continuous flow system involving partial nitrification-anammox and denitrification.

Considering the challenges associated with nitrogen removal from mature landfill leachate, a novel combined continuous-flow process integrating denitrification and partial nitrification-Anammox (PN/A) was developed using an internal circulation (IC) system and a biological aerated filter (BAF) biofilm reactor (IBBR). In this study, IBBR successfully operated for 343 days, and when influent NH4+-N concentration of mature landfill leachate reached 1258.1 mg/L, an impressive total nitrogen removal efficiency (TNRE) of 93.3 % was achieved, along with a nitrogen removal rate (NRR) of 1.13 kg N/(m3·d). The analysis of the microbial community revealed that Candidatus Kuenenia, the dominant genus responsible for anammox, accounted for 1.7 % (day 265). Additionally, Nitrosomonas, Thauera and Truepera were identified as key contributors to the efficient removal of nitrogen from mature landfill. As a novel nitrogen removal strategy, the practical application of the IBBR system offers novel perspectives on addressing mature landfill leachate.

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And in fields such as healthcare, there are higher requirements for the accuracy of the generated imputation values. To train a high-performance classifier and pursue high accuracy, we attempted to resolve any potential negative impact by using a novel algorithmic approach based on the combination of multivariate imputation by chained equations and the ensemble learning method (MICEEN), which can solve the two problems simultaneously. We used multivariate imputation by chained equations to generate more accurate imputation values for the training set passed to ensemble learning to build a predictor. On the other hand, missing values were introduced into minority classes and used them to generate new samples belonging to the minority classes in order to balance the distribution of classes. On real-world datasets, we perform extensive experiments to assess our method and compare it to other state-of-the-art approaches. The advantages of the proposed method are demonstrated by experimental results for the benchmark datasets and self-collected datasets of physical fitness assessment of tumor patients with varying missing rates.

Highly efficient removal of Sr2+ from aqueous solutions using a polyacrylic acid/crown-ether/graphene oxide hydrogel composite.

With the development of nuclear power, efficiently treating nuclear wastes generated during operation has attracted extensive attention. Hydrogels are common adsorbent materials in the treatment of wastewater due to their high swelling rate and easy post-treatment. In this work, a novel polyacrylic acid/crown-ether/graphene oxide (PAA/DB18C6/GO) hydrogel composite was synthesized by a radical cross-linking copolymerization method and characterized using various analytical tools such as SEM, FT-IR, TGA and XPS. The effects of time, pH, initial Sr2+ concentration, and temperature on Sr2+ adsorption onto the PAA/DB18C6/GO were studied. The PAA/DB18C6/GO shows a high adsorption capacity of 379.35 mg g-1 at an initial Sr2+ concentration of 772 mg L-1 due to the unique structure of dibenzo-18-crown-ether-6 and high swelling. The composite has a high selectivity for Sr2+ with a removal rate of 82.4% when concentrations of Na+ and K+ were 10 times higher than that of Sr2+. The pH and temperature have no apparent impact on adsorption performance of the PAA/DB18C6/GO under the experimental conditions. The composite shows excellent reusability with more than 92% removal rate for Sr2+ after five continuous cycles. In addition, the mechanism of Sr2+ adsorption by PAA/DB18C6/GO was analyzed by fitting the adsorption data to the theoretical models and XPS data.

Identification and Blood Transfusion of a Rare A1.02/BA.02 Genotype.

BACKGROUND: The aim was to analyze the serological and molecular genetic characteristics of a rare B(A) subtype pedigree, explore its pathogenesis, and discuss transfusion strategies. METHODS: ABO blood typing serological tests were conducted on a female subject and her family member using standard serological methods. Sequencing analysis of the ABO gene exons 6 and 7 was performed using PCR technique for the female subject and her family member to examine the blood types of the participants. RESULTS: The serological test results showed a discrepancy between the forward and reverse typings of the female subject. The forward typing was similar to that of AB subtype serological forward typing, while the reverse typing indicated AB blood type. Based on the sequencing results, it is inferred that the female subject and her son have 8 mutations on one BA.02 chain: 297A>G, 526C>G, 657C>T, 700C>G, 703G>A, 796C>A, 803G>C, and 930G>A. Comparing these eight mutation sites with the Blood Group Antigen Gene Mutation Database (BGMUT), it was found that the female subject had a heterozygous mutation at c.700C>G in the 7th exon of the B.01 gene, consistent with the characteristics of the BA.02 allele. The genotype of the female subject was determined as A1.02/ BA.02, while the genotype of her son was determined as O.01.01/BA.02. CONCLUSIONS: The serological presentation of the B(A) subtype for the female subject reported in this study was unique. It differed from previously reported cases, indicating that the determination of B(A) subtypes cannot solely rely on serological testing. It requires a comprehensive analysis combining the results of genetic testing and pedigree investigation.

Biodegradable bio-film based on Cordyceps militaris and metal-organic frameworks for fruit preservation.

In this study, Cordyceps militaris matrix was employed for the first time to fabricate a biodegradable food packaging. Carmine and Ag@CuBTC were introduced to cross-link with mycelium and were uniformly dispersed within the matrix to enhance the water resistance, antimicrobial, and antioxidant properties of the bio-films. The bio-film displayed high biodegradability, with nearly 100 % degradation achieved after three weeks. The bio-film exhibited exceptional resistance to oxidation (49.30 % DPPH and 93.94 % ABTS•+), as well as effective inhibitory capabilities against E. coli and S. aureus, respectively. The composite film maintained a high CO2/O2 selective permeability, which was advantageous for mitigating fruit metabolism and extending shelf life. Simultaneously, food preservation experiments confirmed that these bio-films can decelerate the spoilage of fruits and effectively prolong the shelf-life of food. The experimental findings indicated that the prepared Bio-R-Ag@Cu film held promise as an environmentally friendly biodegradable material for food packaging.

DLP Fabrication of Multiple Hierarchical Biomimetic GelMA/SilMA/HAp Scaffolds for Enhancing Bone Regeneration.

Severe bone defects resulting from trauma and diseases remain a persistent clinical challenge. In this study, a hierarchical biomimetic microporous hydrogel composite scaffold was constructed by mimicking the hierarchical structure of bone. Initially, gelatin methacrylamide (GelMA) and methacrylic anhydride silk fibroin (SilMA) were synthesized, and GelMA/SilMA inks with suitable rheological and mechanical properties were prepared. Biomimetic micropores were then generated by using an aqueous two-phase emulsification method. Subsequently, biomimetic microporous GelMA/SilMA was mixed with hydroxyapatite (HAp) to prepare biomimetic microporous GelMA/SilMA/HAp ink. Hierarchical biomimetic microporous GelMA/SilMA/HAp (M-GSH) scaffolds were then fabricated through digital light processing (DLP) 3D printing. Finally, in vitro experiments were conducted to investigate cell adhesion, proliferation, and inward migration as well as osteogenic differentiation and vascular regeneration effects. In vivo experiments indicated that the biomimetic microporous scaffold significantly promoted tissue integration and bone regeneration after 12 weeks of implantation, achieving 42.39% bone volume fraction regeneration. In summary, this hierarchical biomimetic microporous scaffold provides a promising strategy for the repair and treatment of bone defects.

A city-level dataset of heavy metal emissions into the atmosphere across China from 2015-2020.

The absence of nationwide distribution data regarding heavy metal emissions into the atmosphere poses a significant constraint in environmental research and public health assessment. In response to the critical data deficiency, we have established a dataset covering Cr, Cd, As, and Pb emissions into the atmosphere (HMEAs, unit: ton) across 367 municipalities in China. Initially, we collected HMEAs data and covariates such as industrial emissions, vehicle emissions, meteorological variables, among other ten indicators. Following this, nine machine learning models, including Linear Regression (LR), Ridge, Bayesian Ridge (Bayesian), K-Neighbors Regressor (KNN), MLP Regressor (MLP), Random Forest Regressor (RF), LGBM Regressor (LGBM), Lasso, and ElasticNet, were assessed using coefficient of determination (R2), root-mean-square error (RMSE) and Mean Absolute Error (MAE) on the testing dataset. RF and LGBM models were chosen, due to their favorable predictive performance (R2: 0.58-0.84, lower RMSE/MAE), confirming their robustness in modelling. This dataset serves as a valuable resource for informing environmental policies, monitoring air quality, conducting environmental assessments, and facilitating academic research.

miR-199a/b-3p inhibits HCC cell proliferation and invasion through a novel compensatory signaling pathway DJ-1\Ras\PI3K/AKT.

Several studies have reported the effects of DJ-1 gene and miR-199a/b-3p on HCC development. However, whether miR-199a/b-3p regulates HCC progression through a novel compensatory signaling pathway involving DJ-1, Ras, and PI3K/AKT remains unknown. We used (TCGA, HPA, miRWalk and Target scan) databases, cancer and para-tissue HCC patients, dual-luciferase reporter gene analysis, proteomic imprinting, qPCR, cell proliferation, scratch, transport, and flow cytometry to detect the molecular mechanism of DJ-1 and miR-199a/b-3p co-expression in HCC cell lines. Bioinformatics analysis showed that DJ-1 was highly expressed in HCC ((P < 0.001) were closely associated with tumor stage (T), portal vein vascular invasion, OS, DSS, and PFI (P < 0.05); miR-199a/b-3p was lowly expressed in HCC (P < 0.001), which was the upstream regulator of DJ-1. Spearman coefficient r = -0.113, P = 0.031; Dual luciferase gene report verified the negative targeting relationship between them P< 0.001; Western blotting demonstrated that miR-199a/b-3p could inhibit the protein expression of DJ-1, Ras and AKT(P < 0.05); The results of CCK8, cell scratch, Transwell migration and flow cytometry showed that OE + DJ-1 increased the proliferation, migration and invasion ability of HepG2 cells, and decreased the apoptosis process, and the differences were statistically significant (P < 0.05), while miR-199a/b-3p had the opposite effect (P < 0.05).

NCAPG2 promotes prostate cancer malignancy and stemness via STAT3/c-MYC signaling.

BACKGROUND: Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men worldwide, and its incidence has risen substantially in recent years. Therefore, there is an urgent need to identify novel biomarkers and precise therapeutic targets for managing PCa progression and recurrence. METHODS: We investigated the clinical significance of NCAPG2 in PCa by exploring public datasets and our tissue microarray. Receiver operating characteristic (ROC) curve and survival analyses were performed to evaluate the correlation between NCAPG2 and PCa progression. Cell proliferation, wound healing, transwell, flow cytometry, cell cycle, tumor sphere formation, immunofluorescence (IF), co-immunoprecipitation (co-IP), and chromatin immunoprecipitation (ChIP) assays were conducted to further elucidate the molecular mechanism of NCAPG2 in PCa. Subcutaneous and orthotopic xenograft models were applied to investigate the effects of NCAPG2 on PCa proliferation in vivo. Tandem mass tag (TMT) quantitative proteomics was utilized to detect proteomic changes under NCAPG2 overexpression. RESULTS: NCAPG2 was significantly upregulated in PCa, and its overexpression was associated with PCa progression and unfavorable prognosis. Knockdown of NCAPG2 inhibited the malignant behavior of PCa cells, whereas its overexpression promoted PCa aggressiveness. NCAPG2 depletion attenuated the development and growth of PCa in vivo. TMT quantitative proteomics analyses indicated that c-MYC activity was strongly correlated with NCAPG2 expression. The malignancy-promoting effect of NCAPG2 in PCa was mediated via c-MYC. NCAPG2 could directly bind to STAT3 and induce STAT3 occupancy on the MYC promoter, thus to transcriptionally activate c-MYC expression. Finally, we identified that NCAPG2 was positively correlated with cancer stem cell (CSC) markers and enhanced self-renewal capacity of PCa cells. CONCLUSIONS: NCAPG2 is highly expressed in PCa, and its level is significantly associated with PCa prognosis. NCAPG2 promotes PCa malignancy and drives cancer stemness via the STAT3/c-MYC signaling axis, highlighting its potential as a therapeutic target for PCa.

Advanced strategies in high-throughput droplet screening for enzyme engineering.

Enzymes, as biocatalysts, play a cumulatively important role in environmental purification and industrial production of chemicals and pharmaceuticals. However, natural enzymes are limited by their physiological properties in practice, which need to be modified driven by requirements. Screening and isolating certain enzyme variants or ideal industrial strains with high yielding of target product enzymes is one of the main directions of enzyme engineering research. Droplet-based high-throughput screening (DHTS) technology employs massive monodisperse emulsion droplets as microreactors to achieve single strain encapsulation, as well as continuous monitoring for the inside mutant library. It can effectively sort out strains or enzymes with desired characteristics, offering a throughput of 108 events per hour. Much of the early literature focused on screening various engineered strains or designing signalling sorting strategies based on DHTS technology. However, the field of enzyme engineering lacks a comprehensive overview of advanced methods for microfluidic droplets and their cutting-edge developments in generation and manipulation. This review emphasizes the advanced strategies and frontiers of microfluidic droplet generation and manipulation facilitating enzyme engineering development. We also introduce design for various screening signals that cooperate with DHTS and devote to enzyme engineering.

Sleeve Gastrectomy Surgery makes Obstructive Sleep Apnea Worse or Better?: a Multi-Center Observational Study in Patients with Obesity.

BACKGROUND AND OBJECTIVE: Obstructive sleep apnea (OSA) is highly prevalent in the bariatric surgical population, with rates ranging from 50 to 70%. The impact of laparoscopic sleeve gastrectomy (LSG) on OSA and its associated risk factors remain relatively understudied. The aim of this study is to assess the effect of LSG on OSA and investigate predictors of new or worsening OSA postoperatively. Additionally, the study aims to provide evidence for the individualized selection of LSG procedures based on patient characteristics. METHODS: This multi-center observational study enrolled 119 patients with obesity who underwent LSG and were subdivided into two groups based on their preoperative AHI: AHI < 15 and AHI ≥ 15. The patients were followed up and evaluated before and 30 days after LSG. The study utilized univariate and multivariate analyses to assess risk factors for postoperative AHI development. RESULTS: Following LSG, there was a significant decrease in the mean AHI, leading to the resolution of OSA symptoms in 67.6% of patients with AHI ≥ 15. Neck circumference and the number of METS were also identified as independent risk factors for postoperative OSA. Furthermore, preoperative hypertension was found to be a significant predictor of new or worsened OSA after LSG. CONCLUSION: LSG demonstrated effectiveness in improving OSA among patients with obesity. The study highlights the importance of preoperative hypertension evaluation and postoperative management in patients undergoing LSG. Further long-term, multicenter, and large-scale studies are recommended to validate and generalize these findings to diverse patient populations.

Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning.

Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.

CRISPR-powered microfluidic biosensor for preamplification-free detection of ochratoxin A.

The CRISPR technology, which does not require complex instruments, expensive reagents or professional operators, has attracted a lot of attention. When utilizing the CRISPR-Cas system for detection, the pre-amplification step is often necessary to enhance sensitivity. However, this approach tends to introduce complexity and prolong the time required. To address this issue, we employed Pd@PCN-222 nanozyme to label single-stranded DNA, referred to as Pd@PCN-222 CRISPR nanozyme, which serves as the reporter of the CRISPR system. Pd@PCN-222 nanozyme possess exceptional catalytic activity for the reduction of H2O2. Compared with traditional electrochemical probe ferrocene and methylene blue without catalytic activity, there is a significant amplification of the electrochemical signal. So the need for pre-amplification was eliminated. In this study, we constructed a CRISPR-Cas system for ochratoxin A, utilizing the Pd@PCN-222 CRISPR nanozyme to amplified signal avoiding pre-amplification with outstanding detection of 1.21 pg/mL. Furthermore, we developed a microfluidic electrochemical chip for the on-site detection of ochratoxin A. This achievement holds significant promise in establishing a practical on-site detection platform for identifying food safety hazards.