OliTag-seq enhances in cellulo detection of CRISPR-Cas9 off-targets.

The potential for off-target mutations is a critical concern for the therapeutic application of CRISPR-Cas9 gene editing. Current detection methodologies, such as GUIDE-seq, exhibit limitations in oligonucleotide integration efficiency and sensitivity, which could hinder their utility in clinical settings. To address these issues, we introduce OliTag-seq, an in-cellulo assay specifically engineered to enhance the detection of off-target events. OliTag-seq employs a stable oligonucleotide for precise break tagging and an innovative triple-priming amplification strategy, significantly improving the scope and accuracy of off-target site identification. This method surpasses traditional assays by providing comprehensive coverage across various sgRNAs and genomic targets. Our research particularly highlights the superior sensitivity of induced pluripotent stem cells (iPSCs) in detecting off-target mutations, advocating for using patient-derived iPSCs for refined off-target analysis in therapeutic gene editing. Furthermore, we provide evidence that prolonged Cas9 expression and transient HDAC inhibitor treatments enhance the assay's ability to uncover off-target events. OliTag-seq merges the high sensitivity typical of in vitro assays with the practical application of cellular contexts. This approach significantly improves the safety and efficacy profiles of CRISPR-Cas9 interventions in research and clinical environments, positioning it as an essential tool for the precise assessment and refinement of genome editing applications.

Fertility preservation in male adolescents with cancer (2011-2020): A retrospective study in China.

BACKGROUND: According to the studies, more than 80% of pediatric patients with cancer can achieve a survival rate greater than 5 years; however, long-term chemotherapy and/or radiation therapy may seriously affect their reproductive ability. Fertility preservation in adolescents with cancer in China was initiated late, and related research is lacking. Analyze data to understand the current situation and implement measures to improve current practices. METHODS: From 2011 to 2020, data on 275 male adolescents with cancer whose age ranged from 0 to 19 years old were collected from 16 human sperm banks for this retrospective study. Methods include comparing the basic situation of male adolescents with cancer, the distribution of cancer types, and semen quality to analyze the status of fertility preservation. RESULTS: The mean age was 17.39 ± 1.46 years, with 13 cases (4.7%) aged 13-14 years and 262 cases (95.3%) aged 15-19 years. Basic diagnoses included leukemia (55 patients), lymphomas (76), germ cell and gonadal tumors (65), epithelial tumors (37), soft tissue sarcomas (14), osteosarcoma (7), brain tumors (5), and other cancers (16). There are differences in tumor types in different age stages and regions. The tumor type often affects semen quality, while age affects semen volume. Significant differences were found in sperm concentration and progressive motility before and after treatment (p < 0.001). Moreover, 90.5% of patients had sperm in their semen and sperm were frozen successfully in 244 patients (88.7%). CONCLUSIONS: The aim of this study is to raise awareness of fertility preservation in male adolescents with cancer, to advocate for fertility preservation prior to gonadotoxic therapy or other procedures that may impair future fertility, and to improve the fertility status of future patients.

Comparison of different drying pretreatment combined with ultrasonic-assisted enzymolysis extraction of anthocyanins from Lycium ruthenicum Murr.

Extraction of anthocyanins from Lycium ruthenicum Murr. (L. ruthenicum) is a notable challenge in food production, requiring methods that balance efficiency and safety. In this study, we conducted a comparative analysis the extraction of anthocyanins by natural air drying (NAD), vacuum freeze drying (VFD), hot air drying (HAD), and vacuum microwave drying (MVD) combined with ultrasonic-assisted enzymolysis extraction (UAEE). The results demonstrated that the extraction yield and antioxidant activity of anthocyanins were significantly higher in VFD. This phenomenon can be attributed to the modification of raw material's microstructure, leading to an increased extraction yield of specific anthocyanins such as Cyanidin-3-galactoside, Delphinidin chloride, Cyanidin, and Petunidin. According to the pretreatment results, the extraction process of anthocyanins was further optimized. The highest yield (3.16 g/100 g) was obtained in following conditions: 0.24 % pectinase, 48 °C, solid:liquid = 1:21, and 21 min ultrasonic time. This study improves the commercial value and potential application of L. ruthenicum in food industry.

Frontier in gellan gum-based microcapsules obtained by emulsification: Core-shell structure, interaction mechanism, intervention strategies.

As a translucent functional gel with biodegradability, non-toxicity and acid resistance, gellan gum has been widely used in probiotic packaging, drug delivery, wound dressing, metal ion adsorption and other fields in recent years. Because of its remarkable gelation characteristics, gellan gum is suitable as the shell material of microcapsules to encapsulate functional substances, by which the functional components can improve stability and achieve delayed release. In recent years, many academically or commercially reliable products have rapidly emerged, but there is still a lack of relevant reports on in-depth research and systematic summaries regarding the process of microcapsule formation and its corresponding mechanisms. To address this challenge, this review focuses on the formation process and applications of gellan gum-based microcapsules, and details the commonly used preparation methods in microcapsule production. Additionally, it explores the impact of factors such as ion types, ion strength, temperature, pH, and others present in the solution on the performance of the microcapsules. On this basis, it summarizes and analyzes the prospects of gellan gum-based microcapsule products. The comprehensive insights from this review are expected to provide inspiration and design ideas for researchers.

Daily occupational exposure in swine farm alters human skin microbiota and antibiotic resistome.

Antimicrobial resistance (AMR) is a major threat to global public health, and antibiotic resistance genes (ARGs) are widely distributed across humans, animals, and environment. Farming environments are emerging as a key research area for ARGs and antibiotic resistant bacteria (ARB). While the skin is an important reservoir of ARGs and ARB, transmission mechanisms between farming environments and human skin remain unclear. Previous studies confirmed that swine farm environmental exposures alter skin microbiome, but the timeline of these changes is ill defined. To improve understanding of these changes and to determine the specific time, we designed a cohort study of swine farm workers and students through collected skin and environmental samples to explore the impact of daily occupational exposure in swine farm on human skin microbiome. Results indicated that exposure to livestock-associated environments where microorganisms are richer than school environment can reshape the human skin microbiome and antibiotic resistome. Exposure of 5 h was sufficient to modify the microbiome and ARG structure in workers' skin by enriching microorganisms and ARGs. These changes were preserved once formed. Further analysis indicated that ARGs carried by host microorganisms may transfer between the environment with workers' skin and have the potential to expand to the general population using farm workers as an ARG vector. These results raised concerns about potential transmission of ARGs to the broader community. Therefore, it is necessary to take corresponding intervention measures in the production process to reduce the possibility of ARGs and ARB transmission.

Nonadiabatic Coupling-Induced Quantum Coherence in Two-Dimensional Materials.

Two-dimensional materials provide a rich platform demonstrating quantum effects, and the process of electron-hole recombination occurring in them has significant applications in the fields of the photocatalytic and optoelectronic community. Here, we present nonadiabatic coupling-induced quantum coherence and quantum beats in Al-doped blue phosphorene. The work improves our understanding and utilization of nonadiabatic coupling in low-dimensional materials from a new perspective. In addition, our investigations provide meaningful guidance for manipulating quantum coherence in low-dimensional materials and promoting their novel optoelectronic properties.

Large-Area Near-Infrared Emission Enhancement on Single Upconversion Nanoparticles by Metal Nanohole Array.

Single lanthanide (Ln) ion doped upconversion nanoparticles (UCNPs) exhibit great potential for biomolecule sensing and counting. Plasmonic structures can improve the emission efficiency of single UCNPs by modulating the energy transferring process. Yet, achieving robust and large-area single UCNP emission modulation remains a challenge, which obstructs investigation and application of single UCNPs. Here, we present a strategy using metal nanohole arrays (NHAs) to achieve energy-transfer modulation on single UCNPs simultaneously within large-area plasmonic structures. By coupling surface plasmon polaritons (SPPs) with higher-intermediate state (1D2 → 3F3, 1D2 → 3H4) transitions, we achieved a remarkable up to 10-fold enhancement in 800 nm emission, surpassing the conventional approach of coupling SPPs with an intermediate ground state (3H4 → 3H6). We numerically simulate the electrical field distribution and reveal that luminescent enhancement is robust and insensitive to the exact location of particles. It is anticipated that the strategy provides a platform for widely exploring applications in single-particle quantitative biosensing.

Computation-Driven Rational Design of Self-Assembled Short Peptides for Catalytic Hydrogen Production.

Self-assembling peptides represent a captivating area of study in nanotechnology and biomaterials. This interest is largely driven by their unique properties and the vast application potential across various fields such as catalytic functions. However, design complexities, including high-dimensional sequence space and structural diversity, pose significant challenges in the study of such systems. In this work, we explored the possibility of self-assembled peptides to catalyze the hydrolysis of hydrosilane for hydrogen production using ab initio calculations and carried out wet-lab experiments to confirm the feasibility of these catalytic reactions under ambient conditions. Further, we delved into the nuanced interplay between sequence, structural conformation, and catalytic activity by combining modeling with experimental techniques such as transmission electron microscopy and nuclear magnetic resonance and proposed a dual mode of the microstructure of the catalytic center. Our results reveal that although research in this area is still at an early stage, the development of self-assembled peptide catalysts for hydrogen production has the potential to provide a more sustainable and efficient alternative to conventional hydrogen production methods. In addition, this work also demonstrates that a computation-driven rational design supplemented by experimental validation is an effective protocol for conducting research on functional self-assembled peptides.

A frontier exploration of ancient craftsmanship: Effects of various tea products in traditional Chinese cuisine "tea flavored beef".

Modern science often overlooks to reveal the scientific essence of traditional crafts to promote their inheritance and development. In this work, five different types of tea products were prepared using the same variety of tea leaves referring to traditional methods. The analysis of their components and activities indicated that the processing reduced total catechin contents (from 172.8 mg/g to 48.2 mg/g) and promoted the synthesis of theaflavins (from 17.9 mg/g to 43.4 mg/g), reducing antioxidant and antimicrobial abilities of the resulting tea products. On this basis, the tea products were applied to "tea flavored beef" to reveal long-term effects. Within 15 days of storage, tea treatment showed remarkable antimicrobial and antioxidant activities on the beef. Also, the declines of sensory scores and texture of the treated beef were significantly suppressed. Meanwhile, protein degradation in the beef was inhibited, limiting the contents of various biogenic amines within relatively low levels.

Simulating chemical reactions promoted by self-assembled peptides with catalytic properties.

Peptides that self-assemble exhibit distinct three-dimensional structures and attributes, positioning them as promising candidates for biocatalysts. Exploring their catalytic processes enhances our comprehension of the catalytic actions inherent to self-assembling peptides, laying a theoretical foundation for creating novel biocatalysts. The investigation into the intricate reaction mechanisms of these entities is rendered challenging due to the vast variability in peptide sequences, their aggregated formations, supportive elements, structures of active sites, types of catalytic reactions, and the interplay between these variables. This complexity hampers the elucidation of the linkage between sequence, structure, and catalytic efficiency in self-assembling peptide catalysts. This chapter delves into the latest progress in understanding the mechanisms behind peptide self-assembly, serving as a catalyst in hydrolysis and oxidation reactions, and employing computational analyses. It discusses the establishment of models, selection of computational strategies, and analysis of computational procedures, emphasizing the application of modeling techniques in probing the catalytic mechanisms of peptide self-assemblies. It also looks ahead to the potential future trajectories within this research domain. Despite facing numerous obstacles, a thorough investigation into the structural and catalytic mechanisms of peptide self-assemblies, combined with the ongoing advancement in computational simulations and experimental methodologies, is set to offer valuable theoretical insights for the development of new biocatalysts, thereby significantly advancing the biocatalysis field.

Intermittent hyperglycaemia induces macrophage dysfunction by extracellular regulated protein kinase-dependent PKM2 translocation in periodontitis.

Early fluctuations in blood glucose levels increased susceptibility to macrophage dysfunction. However, the underlying pathological mechanisms linking glucose variations and macrophage dysregulation remains elusive. In current study, we established an animal model of transient intermittent hyperglycaemia (TIH) to simulate early fluctuations in blood glucose levels. Our findings revealed that both TIH and diabetic group exhibited more severe periodontal lesions and increased secretion of pro-inflammatory cytokines compared to healthy controls. In immortalized bone marrow-derived macrophages (iBMDMs), phagocytosis and chemotaxis were impaired with transient and lasting hyperglycaemia, accompanied by enhanced glycolysis. We also found that TIH activated pyruvate kinase M2 (PKM2) through the phosphorylation of extracellular regulated protein kinase (ERK) in vivo, particularly at dimeric levels. In macrophage cultured with TIH, PKM2 translocated into the nucleus and involved in the regulating inflammatory genes, including TNF-α, IL-6 and IL-1ß. PKM2 translocation and secretion of inflammatory cytokines were attenuated by PD98059, while PKM2 tetramer activator TEPP-46 prevented the formation of dimeric PKM2 in macrophages. Moreover, inhibition of glycolysis alleviated the TIH-induced pro-inflammatory cytokines. In conclusion, our manuscript provides a rationale for understanding how TIH modulates metabolic rewiring and dysfunction in macrophages via ERK-dependent PKM2 nuclear translocation.

Sign language recognition based on dual-path background erasure convolutional neural network.

Sign language is an important way to provide expression information to people with hearing and speaking disabilities. Therefore, sign language recognition has always been a very important research topic. However, many sign language recognition systems currently require complex deep models and rely on expensive sensors, which limits the application scenarios of sign language recognition. To address this issue, based on computer vision, this study proposed a lightweight, dual-path background erasing deep convolutional neural network (DPCNN) model for sign language recognition. The DPCNN consists of two paths. One path is used to learn the overall features, while the other path learns the background features. The background features are gradually subtracted from the overall features to obtain an effective representation of hand features. Then, these features are flatten into a one-dimensional layer, and pass through a fully connected layer with an output unit of 128. Finally, use a fully connected layer with an output unit of 24 as the output layer. Based on the ASL Finger Spelling dataset, the total accuracy and Macro-F1 scores of the proposed method is 99.52% and 0.997, respectively. More importantly, the proposed method can be applied to small terminals, thereby improving the application scenarios of sign language recognition. Through experimental comparison, the dual path background erasure network model proposed in this paper has better generalization ability.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study.

OBJECTIVE: To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance. METHODS: The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS: The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %. CONCLUSION: The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Trends in cancer-related suicide in the United States: a population-based epidemiology study spanning 40 years of data.

Large cohort studies examining trends in cancer-related suicide are lacking. We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database, encompassing a total of 4,870,410 patients diagnosed with cancer from 1975 to 2017 in the United States. Joinpoint regression was used to estimate the annual percent change (APC) and average annual percentage change (AAPC) of age-adjusted rates of suicide. In the past 40 years, we revealed a gradual increase in cancer-related suicide rates from 1975 to 1989, followed by a gradual decrease from 1989 to 2013, and a marked decrease from 2013 to 2017. These trends suggested the potential impact of advancements in psychosocial care for patients with cancer in contributing to the observed decrease in suicide rates.

Retrosynthesis Zero: Self-Improving Global Synthesis Planning Using Reinforcement Learning.

The field of computer-aided synthesis planning (CASP) has witnessed significant growth in recent years. Still, many CASP programs rely on large data sets to train neural networks, resulting in limitations due to the data quality and prior knowledge from chemists. In response, we propose Retrosynthesis Zero (ReSynZ), a reaction template-based method that combines Monte Carlo Tree Search with reinforcement learning inspired by AlphaGo Zero. Unlike other single-step reaction template-based CASP methods, ReSynZ takes complete synthesis paths for complex molecules, determined by reaction rules, as input for training the neural network. ReSynZ enables neural networks trained with relatively small reaction data sets (tens of thousands of data) to generate multiple synthesis pathways for a target molecule and suggest possible reaction conditions. On multiple data sets of molecular retrosynthesis, ReSynZ demonstrates excellent predictive performance compared to existing algorithms. The advantages, such as self-improving model features, flexible reward settings, the potential to surpass human limitations in chemical synthesis route planning, and others, make ReSynZ a valuable tool in chemical synthesis design.

Recent advances in potential therapeutic targets of ferroptosis­associated pathways for the treatment of stroke (Review).

Stroke is a severe neurological disease that is associated with high rates of morbidity and mortality, and the underlying pathological processes are complex. Ferroptosis fulfills a significant role in the progression and treatment of stroke. It is well established that ferroptosis is a type of programmed cell death that is distinct from other forms or types of cell death. The process of ferroptosis involves multiple signaling pathways and regulatory mechanisms that interact with mechanisms inherent to stroke development. Inducers and inhibitors of ferroptosis have been shown to exert a role in the onset of this cell death process. Furthermore, it has been shown that interfering with ferroptosis affects the occurrence of stroke, indicating that targeting ferroptosis may offer a promising therapeutic approach for treating patients of stroke. Hence, the present review aimed to summarize the latest progress that has been made in terms of using therapeutic interventions for ferroptosis as treatment targets in cases of stroke. It provides an overview of the relevant pathways and molecular mechanisms that have been investigated in recent years, highlighting the roles of inducers and inhibitors of ferroptosis in stroke. Additionally, the intervention potential of various types of Traditional Chinese Medicine is also summarized. In conclusion, the present review provides a comprehensive overview of the potential therapeutic targets afforded by ferroptosis­associated pathways in stroke, offering new insights into how ferroptosis may be exploited in the treatment of stroke.

Unraveling the relationships between processing conditions and PhIP formation in chemical model system and roast pork patty via principal component analysis.

2-amino-1-methyl-6-phenylimidazole [4,5-b] pyridine (PhIP) is one of the higher levels of HAAs produced in protein foods during heating. The effects of heating temperature, time, and concentration of precursors on PhIP and related substances in the chemical model system and roast pork patty were studied using HPLC-Q-Orbitrap-HRMS and GC-MS. Results showed that the heating temperature, time, and concentration of four precursors significantly affected PhIP and its related substances (P < 0.05) in the chemical model system. Among them, PhIP production was greatest when heating at 200 min with 220 °C, and the concentrations of phenylalanine, creatinine, glucose, and creatine added were 10, 20, 20, and 20 mmol/L, respectively. Moreover, as the fat proportion of roast pork patties increased, PhIP and its intermediate-phenylacetaldehyde concentrations increased substantially (P < 0.05). PCA results showed that the samples of PhIP and related substances gradually dispersed as the temperature and time increased, and there were obvious effects among them.

Constructing Metal(II)-Sulfate Site Catalysts toward Low Overpotential Carbon Dioxide Electroreduction to Fuel Chemicals.

Precise regulation of the active site structure is an important means to enhance the activity and selectivity of catalysts in CO2 electroreduction. Here, we creatively introduce anionic groups, which can not only stabilize metal sites with strong coordination ability but also have rich interactions with protons at active sites to modify the electronic structure and proton transfer process of catalysts. This strategy helps to convert CO2 into fuel chemicals at low overpotentials. As a typical example, a composite catalyst, CuO/Cu-NSO4/CN, with highly dispersed Cu(II)-SO4 sites has been reported, in which CO2 electroreduction to formate occurs at a low overpotential with a high Faradaic efficiency (-0.5 V vs. RHE, FEformate=87.4 %). Pure HCOOH is produced with an energy conversion efficiency of 44.3 % at a cell voltage of 2.8 V. Theoretical modeling demonstrates that sulfate promotes CO2 transformation into a carboxyl intermediate followed by HCOOH generation, whose mechanism is significantly different from that of the traditional process via a formate intermediate for HCOOH production.

Enhancing the Lithium Storage Performance of the Nb2O5 Anode via Synergistic Engineering of Phase and Cu Doping.

Nb2O5 has been viewed as a promising anode material for lithium-ion batteries by virtue of its appropriate redox potential and high theoretical capacity. However, it suffers from poor electric conductivity and low ion diffusivity. Herein, we demonstrate the controllable fabrication of Cu-doped Nb2O5 with orthorhombic (T-Nb2O5) and monoclinic (H-Nb2O5) phases through annealing the solvothermally presynthesized Nb2O5 precursor under different temperatures in air, and the Cu doping amount can be readily controlled by the concentration of the precursor solution, whose effect on the lithium storage behaviors of the Cu-doped Nb2O5 is thoroughly investigated. H-Nb2O5 shows obvious redox peaks (Nb5+/Nb4+ and Nb4+/Nb3+) with much higher capacity and better cycling stability than those for the widely investigated T-Nb2O5. When introducing appropriate Cu doping, the optimized H-Cu0.1-Nb2O5 electrode shows greatly enhanced conductivity and lower diffusion barrier as revealed by the theoretical calculations and electrochemical characterizations, delivering a high reversible capacity of 203.6 mAh g-1 and a high capacity retention of 140.8 mAh g-1 after 5000 cycles at 1 A g-1, with a high initial Coulombic efficiency of 91% and a high rate capacity of 144.2 mAh g-1 at 4 A g-1. As a demonstration for full-cell application, the H-Cu0.1-Nb2O5||LiFePO4 cell displays good cycling performance, exhibiting a reversible capacity of 135 mAh g-1 after 200 cycles at 0.2 A g-1. More importantly, this work offers a new synthesis protocol of the monoclinic Nb2O5 phase with high capacity retention and improved reaction kinetics.