The multi-dimensional challenges of controlling respiratory virus transmission in indoor spaces: Insights from the linkage of a microscopic pedestrian simulation and SARS-CoV-2 transmission model.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.

Highly Conductive Imidazolate Covalent Organic Frameworks with Ether Chains as Solid Electrolytes for Lithium Metal Batteries.

Poly(ethylene oxide) (PEO)-based electrolytes are often used for Li+ conduction as they can dissociate the Li salts efficiently. However, high entanglement of the chains and lack of pathways for rapid ion diffusion limit their applications in advanced batteries. Recent developments in ionic covalent organic frameworks (iCOFs) showed that their highly ordered structures provide efficient pathways for Li+ transport, solving the limitations of traditional PEO-based electrolytes. Here, we present imidazolate COFs, PI-TMEFB-COFs, having methoxyethoxy chains, synthesized by Debus-Radziszewski multicomponent reactions and their ionized form, Li+@PI-TMEFB-COFs, showing a high Li+ conductivity of 8.81 mS cm-1 and a transference number of 0.974. The mechanism for such excellent electrochemical properties is that methoxyethoxy chains dissociate LiClO4, making free Li+, then those Li+ are transported through the imidazolate COFs' pores. The synthesized Li+@PI-TMEFB-COFs formed a stable interface with Li metal. Thus, employing Li+@PI-TMEFB-COFs as the solid electrolyte to assemble LiFePO4 batteries showed an initial discharge capacity of 119.2 mAh g-1 at 0.5 C, and 82.0 % capacity and 99.9 % Coulombic efficiency were maintained after 400 cycles. These results show that iCOFs with ether chains synthesized via multicomponent reactions can create a new chapter for making solid electrolytes for advanced rechargeable batteries.

Highly Conductive Covalent-Organic Framework Films.

Chemically inert organic networks exhibiting electrical conductivity comparable to metals can advance organic electronics, catalysis, and energy storage systems. Covalent-organic frameworks (COFs) have emerged as promising materials for those applications due to their high crystallinity, porosity, and tunable functionality. However, their low conductivity has limited their practical utilization. In this study, copper-coordinated-fluorinated-phthalocyanine and 2,3,6,7-tetrahydroxy-9,10-anthraquinone-based COF (CuPc-AQ-COF) films with ultrahigh conductivity are developed. The COF films exhibit an electrical conductivity of 1.53 × 103 S m-1 and a Hall mobility of 6.02 × 102 cm2 V-1 s-1 at 298 K, reaching the level of metals. The films are constructed by linking phthalocyanines and anthraquinones through vapor-assisted synthesis. The high conductivity properties of the films are attributed to the molecular design of the CuPc-AQ-COFs and the generation of high-quality crystals via the vapor-assisted method. Density functional theory analysis reveals that an efficient donor-acceptor system between the copper-coordinated phthalocyanines and anthraquinones significantly promotes charge transfer. Overall, the CuPc-AQ-COF films set new records of COF conductivity and mobility and represent a significant step forward in the development of COFs for electronic, catalytic, and electrochemical applications.

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

Synergistic mediating effect of edible fungal polysaccharides (Auricularia and Tremellan) and Crataegus flavonoids in hyperlipidemic rats.

Both edible fungal polysaccharides (Auricularia and Tremellan) and Crataegus flavonoids promote the balance of dyslipidemia, which have a positive biological regulating effect on intestinal flora. In this study, the extraction of water-soluble polysaccharides from Auricularia and Tremellan was investigated and optimized firstly. Polysaccharides and flavonoids were then combined to study the effects on the mediating role of abnormal blood lipid concentration and intestinal flora in vivo. The rats were divided into 10 groups, the NC (normal control), HM (model), PCI (Simvastatin control), PCII (Fenofibrate control), AAP (Auricularia auricular Polysaccharide), TFP (Tremella fuciformis Polysaccharide), HF (Crataegus Flavonoid), LDC (Low-dose combination), MDC (Medium dose combination), and HDC (High-dose combination), used to explore the impact of polysaccharides and flavonoids complex on state of blood lipid, liver, and intestinal flora of dyslipidemia rats. The results showed that the combination of polysaccharides and flavonoids could significantly decrease the levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL-C), and increase the level of high-density lipoprotein cholesterol (HDL-C). It also significantly decreased the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and improved liver morphology. What is more, the HDC favorably alters the intestinal microflora balance, promotes intestinal integrity and mobility, and inhibits the growth of harmful bacteria such as Escherichia coli/Shigella and Clostridium compared with HM group. In brief, the combination of polysaccharides and flavonoids had a synergistic effect on the remission of dyslipidemia, and promoted health by improving lipid metabolism, protecting liver tissue, and regulating the intestinal flora in hyperlipidemia rats.

Poly(Ethylene Piperidinium)s for Anion Exchange Membranes.

The lack of anion exchange membranes (AEMs) that possess both high hydroxide conductivity and stable mechanical and chemical properties poses a major challenge to the development of high-performance fuel cells. Improving one side of the balance between conductivity and stability usually means sacrificing the other. Herein, we used facile, high-yield chemical reactions to design and synthesize a piperidinium polymer with a polyethylene backbone for AEM fuel cell applications. To improve the performance, we introduced ionic crosslinking into high-cationic-ratio AEMs to suppress high water uptake and swelling while further improving the hydroxide conductivity. Remarkably, PEP80-20PS achieved a hydroxide conductivity of 354.3 mS cm-1 at 80 °C while remaining mechanically stable. Compared with the base polymer PEP80, the water uptake of PEP80-20PS decreased by 69 % from 813 % to 350 %, and the swelling decreased substantially by 85 % from 350.0 % to 50.2 % at 80 °C. PEP80-20PS also showed excellent alkaline stability, 84.7 % remained after 35 days of treatment with an aqueous KOH solution. The chemical design in this study represents a significant advancement toward the development of simultaneously highly stable and conductive AEMs for fuel cell applications.

Towards the fastest kinetics and highest uptake of post-functionalized UiO-66 for Hg2+ removal from water.

Recent advances in adsorbents have improved the removal of mercury ions from wastewater. Metal-organic frameworks (MOFs) have been increasingly used as adsorbents due to their high adsorption capacity and ability to adsorb various heavy metal ions. UiO-66 (Zr) MOFs are mainly used because they are highly stable in aqueous solutions. However, most functionalized UiO-66 materials are unable to achieve a high adsorption capacity because of the undesired reactions that occur during post-functionalization. Herein, we report a facile post-functionalization method to synthesize a MOF adsorbent with fully active amide- and thiol-functionalized chelating groups, termed UiO-66-A.T. UiO-66-A.T. was synthesized via a two-step reaction by crosslinking with a monomer containing a disulfide moiety, followed by disulfide cleavage to activate the thiol groups. UiO-66-A.T. removed Hg2+ from water with a maximum adsorption capacity of 691 mg g-1 and a rate constant of 0.28 g mg-1 min-1 at pH 1. In a mixed solution containing 10 different heavy metal ions, UiO-66-A.T. has a Hg2+ selectivity of 99.4%, which is the highest reported to date. These results demonstrate the effectiveness of our design strategy for synthesizing purely defined MOFs to achieve the best Hg2+ removal performance to date among post-functionalized UiO-66-type MOF adsorbents.

Multitrack and multianchor point screw technique combined with the Wiltse approach for lesion debridement for lumbar tuberculosis.

BACKGROUND: The incidence of lumbar tuberculosis is high worldwide, and effective treatment is a continuing problem. AIM: To study the safety and efficacy of the multitrack and multianchor point screw technique combined with the contralateral Wiltse approach for lesion debridement to treat lumbar tuberculosis. METHODS: The C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), visual analogue scale (VAS) score, oswestry disability index (ODI) and American Spinal Injury Association (ASIA) grade were recorded and analysed pre- and postoperatively. RESULTS: The CRP level and ESR returned to normal, and the VAS score and ODI were decreased at 3 mo postoperatively, with significant differences compared with the preoperative values (P < 0.01). Neurological dysfunction was relieved, and the ASIA grade increased, with no adverse events. CONCLUSION: The multitrack, multianchor point screw fixation technique combined with the contralateral Wiltse approach for debridement is an effective and safe method for the treatment of lumbar tuberculosis.

Placenta-derived exosomes as carriers of non-coding RNAs in maternal circulation of patients with preeclampsia.

This study was conducted to illustrate the origin of these PE-related ncRNAs in maternal circulation and their underlying transport methods into target cells. We selected 10 women with severe pre-eclampsia (PE group) and 10 healthy participants who served as controls (NC group). Exosomes were isolated from their sera and their origin was determined by a specific marker, placental alkaline phosphatase (PLAP). We compared the ncrna associated with PE in exosomes and whole serum to observe the exchange of serum exosomes with trophoblast cells. The results showed that PLAP was enriched in the isolated exosomes. Seven PE-associated ncRNAs, including lnc-SNHG5, miR-26a-5p, miR-221-3p, miR-30a-3p, miR-18a, miR-152 and miR-155, were analyzed in placenta-derived exosomes and whole serum from both groups. miR-26a-5p, miR-152 and miR-155 were upregulated in the PE group compared with the NC group. 152 and miR-155 were upregulated. 152 and miR-155 were upregulated, while miR-18a and miR-221-3p were downregulated (P<0.05). ncRNAs were altered in serum and placenta-derived serum exosomes in a consistent trend. Fluorescence microscopy results showed that the nuclei were counterstained in HTR-8 cells exposed to PKH26-labeled exosomes. PE-associated ncRNAs can enter the maternal circulation through secretion and encapsulation into placenta-derived exosomes and participate in the development and progression of PE by targeting trophoblast cells. differential expression of ncRNAs in exosomes has the potential to be used as predictors for targeted therapy, providing new ideas and perspectives for improving maternal and infant outcomes.

Macrocycle-Based Covalent Organic Frameworks.

Macrocycles with well-defined cavities and the ability to undergo supramolecular interactions are classical materials that have played an essential role in materials science. However, one of the most substantial barriers limiting the utilization of macrocycles is their aggregation, which blocks the active regions. Among many attempted strategies to prevent such aggregation, installing macrocycles into covalent organic frameworks (COFs), which are porous and stable reticular networks, has emerged as an ideal solution. The resulting macrocycle-based COFs (M-COFs) preserve the macrocycles' unique activities, enabling applications in various fields such as single-atom catalysis, adsorption/separation, optoelectronics, phototherapy, and structural design of forming single-layered or mechanically interlocked COFs. The resulting properties are unmatchable by any combination of macrocycles with other substrates, opening a new chapter in advanced materials. This review focuses on the latest progress in the concepts, synthesis, properties, and applications of M-COFs, and presents an in-depth outlook on the challenges and opportunities in this emerging field.

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer.

Background: Liver metastasis (LM) is an independent risk factor that affects the prognosis of patients with ovarian cancer; however, there is still a lack of prediction. This study developed a limit gradient enhancement (XGBoost) to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer, thereby improving prediction efficiency. Patients and Methods. Data of patients diagnosed with ovarian cancer in the Surveillance, Epidemiology, and Final Results (SEER) database from 2010 to 2015 were retrospectively collected. The XGBoost algorithm was used to establish a lung metastasis model for patients with ovarian cancer. The performance of the predictive model was tested by the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Results: The results of the XGBoost algorithm showed that the top five important factors were age, laterality, histological type, grade, and marital status. XGBoost showed good discriminative ability, with an AUC of 0.843. Accuracy, sensitivity, and specificity were 0.982, 1.000, and 0.686, respectively. Conclusion: This study is the first to develop a machine-learning-based prediction model for lung metastasis in patients with ovarian cancer. The prediction model based on the XGBoost algorithm has a higher accuracy rate than traditional logistic regression and can be used to predict the risk of lung metastasis in newly diagnosed patients with ovarian cancer.

Herpes Simplex Virus Infection Increases Beta-Amyloid Production and Induces the Development of Alzheimer's Disease.

Background: Alzheimer's disease, a neurodegenerative memory disease, primarily results from the formation of amyloid plaques (Aß) that gradually inhibit neuron communications. The entire mechanism of Aß production remains unclear to date, and it is of particular interest among scientists to find out the exact mechanism that leads to amyloid precursor protein (APP) cleavage through the amyloidogenic pathway so that effective treatments can be developed. Method: 2 sets of experiments with the use of human H4-N cell lines are proposed to fully investigate the validity of the hypothesis. All of the experiments would involve immunoblotting of Aß using an anti-Aß antibody, and the results would be analyzed with the assistance of an image analyzer. A significant amount of Aß would be expected to be present in the cytoplasm of cells with herpes simplex virus (HSV-1) applied, as APP endocytosis would be induced by HSV-1, which leads to higher Aß levels inside the cell. Results: In this paper, a new hypothesis is presented on how HSV-1 infection initiates APP endocytosis and causes an increase in APP cleavage and Aß production inside the cells. It is also hypothesized that increased Aß peptides exit the cell via exocytosis, therefore, leading to the development of Alzheimer's disease. The findings will support the hypothesis if intracellular Aß concentration is significantly higher after the introduction of dHSV-1 and subsequently if extracellular Aß concentration becomes higher without TeNT exocytosis inhibition. Conclusion: The results of this study would provide valuable insights into the mechanisms underlying Alzheimer's disease and open new scopes of research for its potential treatments. Further studies on virus infection and the development of memory diseases should be conducted to investigate possible correlations.

A new nomogram for predicting lung metastasis in newly diagnosed endometrial carcinoma patients: A study based on SEER.

Background: Lung metastasis (LM) is an independent risk factor for survival in patients with endometrial cancer (EC). Methods: We reviewed data on patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The independent predictors of LM in patients with EC were identified using univariate and multivariate logistic regression analyses. A nomogram for predicting LM in patients with EC was developed, and the predictive model was evaluated using calibration and receiver operating characteristic (ROC) curves. Results: Univariate and multivariate logistic regression analyses showed that high grade; specific histological type; high tumor and node stages; larger tumor size; and liver, brain, and bone metastases were positively associated with LM risk. A new nomogram was developed by combining these factors to predict LM in patients newly diagnosed with EC. Internal and external verification of the calibration charts showed that the nomogram was well calibrated. The areas under the ROC curves for the training and validation cohorts were 0.924 and 0.913, respectively. Conclusion: We performed a retrospective analysis of 42,073 patients with EC using the SEER database, established a new nomogram for predicting LM based on eight independent risk factors, and visualized the model using a nomogram for the first time.

The Effect of Time Window Length on EEG-Based Emotion Recognition.

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

Synthesis of Zr metal-organic frameworks (MOFs) to remove Fe3+ from water.

Although a trace amount of Fe3+ is essential for human physiological function, excessive amounts are lethal. Here, we present a protocol for removing Fe3+ from water through highly crystalline and stable thiol-functionalized Zr metal-organic frameworks (Zr-MOFs). We provide details of the MOFs synthesis and post-functionalization procedures, and include key performance data of the Zr-MOFs for removing Fe3+, which were collected from the inductively coupled plasma-optical emission spectrometer (ICP-OES) and inductively coupled plasma mass spectrometer (ICP-MS). For complete details on the use and execution of this protocol, please refer to Yuan et al. (2022).

An effective long-wavelength fluorescent sensor for Cu2+ based on dibenzylidenehydrazine-bridged biphenylacrylonitrile.

Although numerous fluorescence sensors for Cu2+ have been presented, a long-wavelength sensor in aqueous media has rarely been reported as expected due to practical application requirements. In this work, a novel AIE molecule (DHBB) containing two biphenylacrylonitrile units bridged by dibenzylidenehydrazine was prepared. It possessed the merits of long-wavelength emission, good emission in aqueous media, and multiple functional groups for binding Cu2+. It exhibited good sensing selectivity for Cu2+ among all kinds of tested metal ions. The detection limit was as low as 1.08 × 10-7 M. The sensing mechanism was clarified as 1:1 stoichiometric ratio based on the binding cooperation of O and N functional groups of DHBB. The selective sensing ability for Cu2+ remained stable at pH = 5-9 and was influenced little by other metal ions. The Cu2+ sensing ability of DHBB was applied in real samples with 96% recovery rate. The bio-imaging experiment of living cells suggested that DHBB possessed not only good bio-imaging performance but also sensing ability for Cu2+ in living environments. This work suggested the good application prospect of DHBB to sense Cu2+ in real samples and living environment.

The effect of the Ontario stay-at-home order on Covid-19 third wave infections including vaccination considerations: An interrupted time series analysis.

The Covid-19 global pandemic that began in March 2020 was not fully mitigated through governmental Non-Pharmaceutical Interventions (NPIs) and continued to infect people and take lives through 2021. Since many countries were affected by the second, third, and fourth waves of Covid-19, governments extended and strengthened NPIs, but these actions led to citizen protests and fatigue. In this study, we investigate the effect of a lockdown policy on Covid-19 third wave implemented by the province of Ontario, Canada, on April 3rd 2021, followed by a stay-at-home order on April 7th 2021 while free Covid-19 testing and vaccination were in progress. Herein, the effect of both NPIs and vaccination are considered simultaneously. We used the prevalence of Covid-19 cases, tests, and administered vaccines data reported publicly by the Government of Ontario on their website. Because mobility changes can reflect the behaviors and adherence of residents with a stay-at-home order, Covid-19 community mobility data for Ontario provided by Google was also considered. A statistical method called interrupted time series was used to analyze the data. The results indicated that, although vaccinations helped to control the Covid-19 infection rate during this time, the stay-at-home order caused a rate reduction by decreasing the trend of the Covid-19 prevalence by 13 (±0.8962) persons per million daily and the level by 33 (±7.6854) persons per million. Furthermore, the stay-at-home order resulted in approximately a 37% reduction in Covid-19 prevalence one week after the intervention's effective date. Therefore, Ontario's strict lockdown policy, including several NPIs, mitigated the Covid-19 surge during the third wave. The results show that even when vaccination is in progress, strict NPIs such as lockdown is required to control Covid-19 waves, and early re-openings should be avoided. These results may also be useful for other countries that have implemented delayed vaccination schedules.

Downregulation of the long non-coding RNA MALAT1 in tenofovir-treated pregnant women with hepatitis B virus infection promotes immune recovery of natural killer cells via the has-miR-155-5p/HIF-1α axis.

BACKGROUND: Currently, tenofovir disoproxil fumarate (TDF) treatment in pregnant women with hepatitis B virus (HBV) infection in the second trimester of pregnancy to reduce the HBV DNA load and block the mother-to-child transmission of HBV has become a consensus. An increasing number of studies have shown that lncRNAs play an important role in immune regulation; however, there are only a few studies on how lncRNAs affect the immune function of TDF-treated pregnant women with HBV infection. METHODS: The peripheral blood of pregnant women with HBV infection was collected before and after TDF treatment for whole-transcriptome sequencing; the differential expression of lncRNA MALAT1 in PBMCs and natural killer (NK) cells was verified by qRT-PCR. ELISA and flow cytometry were used to detect the effect of MALAT1 on NK-92 cells on IFN-γ secretion. Dual-luciferase reporter, qRT-PCR, and western blot analyses were used to verify the relationship between the expression levels of MALAT1, has-miR-155-5p and HIF-1α. RESULTS: After TDF treatment, the MALAT1 expression in the PBMCs of pregnant women with HBV infection, especially in NK cells, was significantly reduced. MALAT1 overexpression decreased IFN-γ secretion in NK-92 cells, while IFN-γ secretion increased after MALAT1 knockdown. Mechanistically, we identified MALAT1 as a competitive endogenous RNA that regulates HIF-1α expression by sponging has-miR-155-5p. Low HIF-1α expression resulted in increased IFNG expression in, and IFN-γ secretion by, NK cells. CONCLUSIONS: Thus, MALAT1 may play an important role in NK cell-mediated immunity in TDF-treated pregnant women with HBV infection by regulating the has-miR-155-5p/HIF-1α axis.

High-throughput Genetically Modified Animal Experiments Achieved by Next-generation Mammalian Genetics.

Animal models are essential tools for modern scientists to conduct biological experiments and investigate their hypotheses in vivo. However, for the past decade, raising the throughput of such animal experiments has been a great challenge. Conventionally, in vivo high-throughput assay was achieved through large-scale mutagen-driven forward genetic screening, which took years to find causal genes. In contrast, reverse genetics accelerated the causal gene identification process, but its throughput was also limited by 2 barriers, that is, the genome modification step and the time-consuming crossing step. Defined as genetics without crossing, next-generation genetics is able to produce gene-modified animals that can be analyzed at the founder generation (F0). This method is or can be accomplished through recent technological advances in gene editing and virus-based efficient gene modifications. Notably, next-generation genetics has accelerated the process of cross-species studies, and it will be a useful technique during animal experiments as it can provide genetic perturbation at an individual level without crossing. In this review, we begin by introducing the history of animal-based high-throughput analysis, with a specific focus on chronobiology. We then describe ways that gene modification efficiency during animal experiments was enhanced and why crossing remained a barrier to reaching higher efficiency. Moreover, we mention the Triple CRISPR as a critical technique for achieving next-generation genetics. Finally, we discuss the potential applications and limitations of next-generation mammalian genetics.

Alteration of the Immune Microenvironment in HBsAg and HBeAg Dual-Positive Pregnant Women Presenting a High HBV Viral Load.

PURPOSE: The aim of this study was to compare the differences in the immune microenvironment between HBV-infected pregnant women with a high HBV viral load and healthy pregnant women, with an emphasis on T cell subset alteration. PATIENTS AND METHODS: We compared the differences of cellular and molecular signatures between HBV-infected and healthy pregnant women by performing single-cell RNA and T cell receptor sequencing of peripheral blood mononuclear cells from 51,836 women in the mid-trimester pregnancy stage. Specific trajectories of the different T clusters throughout the course of T cell differentiation were investigated. Flow cytometry was used to validate the proportion of different T cell subtypes. RESULTS: We identified nine cellular subtypes and found an increased proportion of effector/memory CD8+ T cells in HBV-infected pregnant women. Both CD4+ and CD8+ effector/memory T cells in HBV-related samples expressed higher levels of metallothionein (MT)-related genes (MT2A, MTIE, MTIF, MTIX), metal ion pathways, and multiple inflammatory responses. Among CD8+ T cell clusters, we identified a particular subset of effector/memory CD8+ T cells (CD8-cluster 2) with MTs as the top-ranking genes, which may be enriched in HBV-related samples. These cells showed an increased clonal expansion in HBV infection. Moreover, we found more active immune responses, according to cellular interaction patterns, between immune cell subsets in HBV-infected samples. CONCLUSION: This study shows significant differences between HBV-infected and healthy samples, including cell clusters, dominant gene sets, T cell function, clonal expansion, and V/J gene usage of T cell clonotypes, and identifies a distinct CD8+ T cell cluster with immune-active and antiviral properties. These findings pave the way for a deeper understanding of the impact of HBV infection on the immune microenvironment during pregnancy.