Inhalation of 2, 4-di-tert-butylphenol-Loaded micelles suppresses respiratory syncytial virus infection in mice.

Human respiratory syncytial virus (RSV) is a common cause of respiratory infections in infants, young children, and elderly people. However, there are no effective treatments or vaccines available in most countries. In this study, we explored the anti-RSV potential of 2, 4-Di-tert-butylphenol (2, 4-DTBP), a compound derived from Houttuynia cordata Thunb. To overcome the poor solubility of 2, 4-DTBP, we encapsulated it in polymeric micelles and delivered it by inhalation. We found that 2, 4-DTBP-loaded micelles inhibited RSV infection in vitro and improved survival, lung pathology, and viral clearance in RSV-infected mice. Our results suggested that 2, 4-DTBP-loaded micelle is a promising novel therapeutic agent for RSV infection.

KIF5B plays important roles in dendritic spine plasticity and dendritic localization of PSD95 and FMRP in the mouse cortex in vivo.

Kinesin 1 (KIF5) is one major type of motor protein in neurons, but its members' function in the intact brain remains less studied. Using in vivo two-photon imaging, we find that conditional knockout of Kif5b (KIF5B cKO) in CaMKIIα-Cre-expressing neurons shows heightened turnover and lower stability of dendritic spines in layer 2/3 pyramidal neurons with reduced spine postsynaptic density protein 95 acquisition in the mouse cortex. Furthermore, the RNA-binding protein fragile X mental retardation protein (FMRP) is translocated to the proximity of newly formed spines several hours before the spine formation events in vivo in control mice, but this preceding transport of FMRP is abolished in KIF5B cKO mice. We further find that FMRP is localized closer to newly formed spines after fear extinction, but this learning-dependent localization is disrupted in KIF5B cKO mice. Our findings provide the crucial in vivo evidence that KIF5B is involved in the dendritic targeting of synaptic proteins that underlies dendritic spine plasticity.

Underground Mine Safety and Health: A Hybrid MEREC-CoCoSo System for the Selection of Best Sensor.

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of "Ease of Installation" as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model's performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.

Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete.

As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources to prepare the cementitious component of the product. The challenging issue with employing geopolymer concrete in the building business is the absence of a standard mix design. According to the chemical composition of its components, this work proposes a thorough system or framework for estimating the compressive strength of fly ash-based geopolymer concrete (FAGC). It could be possible to construct a system for predicting the compressive strength of FAGC using soft computing methods, thereby avoiding the requirement for time-consuming and expensive experimental tests. A complete database of 162 compressive strength datasets was gathered from the research papers that were published between the years 2000 and 2020 and prepared to develop proposed models. To address the relationships between inputs and output variables, long short-term memory networks were deployed. Notably, the proposed model was examined using several soft computing methods. The modeling process incorporated 17 variables that affect the CSFAG, such as percentage of SiO2 (SiO2), percentage of Na2O (Na2O), percentage of CaO (CaO), percentage of Al2O3 (Al2O3), percentage of Fe2O3 (Fe2O3), fly ash (FA), coarse aggregate (CAgg), fine aggregate (FAgg), Sodium Hydroxide solution (SH), Sodium Silicate solution (SS), extra water (EW), superplasticizer (SP), SH concentration, percentage of SiO2 in SS, percentage of Na2O in SS, curing time, curing temperature that the proposed model was examined to several soft computing methods such as multi-layer perception neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFNN), support vector regression (SVR), decision tree (DT), random forest (RF), and LSTM. Three main innovations of this study are using the LSTM model for predicting FAGC, optimizing the LSTM model by a new evolutionary algorithm called the marine predators algorithm (MPA), and considering the six new inputs in the modeling process, such as aggregate to total mass ratio, fine aggregate to total aggregate mass ratio, FASiO2:Al2O3 molar ratio, FA SiO2:Fe2O3 molar ratio, AA Na2O:SiO2 molar ratio, and the sum of SiO2, Al2O3, and Fe2O3 percent in FA. The performance capacity of LSTM-MPA was evaluated with other artificial intelligence models. The results indicate that the R2 and RMSE values for the proposed LSTM-MPA model were as follows: MLPNN (R2 = 0.896, RMSE = 3.745), BRNN (R2 = 0.931, RMSE = 2.785), GFFNN (R2 = 0.926, RMSE = 2.926), SVR-L (R2 = 0.921, RMSE = 3.017), SVR-P (R2 = 0.920, RMSE = 3.291), SVR-S (R2 = 0.934, RMSE = 2.823), SVR-RBF (R2 = 0.916, RMSE = 3.114), DT (R2 = 0.934, RMSE = 2.711), RF (R2 = 0.938, RMSE = 2.892), LSTM (R2 = 0.9725, RMSE = 1.7816), LSTM-MPA (R2 = 0.9940, RMSE = 0.8332), and LSTM-PSO (R2 = 0.9804, RMSE = 1.5221). Therefore, the proposed LSTM-MPA model can be employed as a reliable and accurate model for predicting CSFAG. Noteworthy, the results demonstrated the significance and influence of fly ash and sodium silicate solution chemical compositions on the compressive strength of FAGC. These variables could adequately present variations in the best mix designs discovered in earlier investigations. The suggested approach may also save time and money by accurately estimating the compressive strength of FAGC with low calcium content.

Quantitative characterization of run-and-tumble statistics in bulk bacterial suspensions.

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy.

Decision tree models for the estimation of geo-polymer concrete compressive strength.

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash-Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Characterization and Control of the Run-and-Tumble Dynamics of Escherichia Coli.

We characterize the full spatiotemporal gait of populations of swimming Escherichia coli using renewal processes to analyze the measurements of intermediate scattering functions. This allows us to demonstrate quantitatively how the persistence length of an engineered strain can be controlled by a chemical inducer and to report a controlled transition from perpetual tumbling to smooth swimming. For wild-type E. coli, we measure simultaneously the microscopic motility parameters and the large-scale effective diffusivity, hence quantitatively bridging for the first time small-scale directed swimming and macroscopic diffusion.

Facile Synthesis of Novel Ti2C Nano Bipyramids for Photothermal and Photodynamic Therapy of Breast Cancer.

Photo-responsive synergetic therapeutics achieved significant attraction in cancer theranostic due to the versatile characteristics of nanomaterials. There have been substantial efforts in developing the simplest nano-design with exceptional synergistic properties and multifunctionalities. In this work, biocompatible Ti2C MXene nano bipyramids (MNBPs) were synthesized by hydrothermal method with dual functionalities of photothermal and photodynamic therapies. The MNBPs shape was obtained from two-dimensional (2D) Ti2C nanosheets by controlling the temperature of the reaction mixture. The structure of these Ti2C MNBPs was characterized by a high-resolution transmission electron microscope, scanning electron microscope, atomic force microscope, X-ray photoelectron spectroscopy, and X-ray diffraction. The Ti2C NBPs have shown exceptional photothermal properties with increased temperature to 72.3 °C under 808 nm laser irradiation. The designed nano bipyramids demonstrated excellent cellular uptake and biocompatibility. The Ti2C NBP has established a remarkable photothermal therapy (PTT) effect against 4T1 breast cancer cells. Moreover, Ti2C NBPs showed a profound response to UV light (6 mW/cm2) and produced reactive oxygen species, making them useful for photodynamic therapy (PDT). These in-vitro studies pave a new path to tune the properties of photo-responsive MXene nanosheets, indicating a potential use in biomedical applications.

Detecting Respiratory Viruses Using a Portable NIR Spectrometer-A Preliminary Exploration with a Data Driven Approach.

Respiratory viruses' detection is vitally important in coping with pandemics such as COVID-19. Conventional methods typically require laboratory-based, high-cost equipment. An emerging alternative method is Near-Infrared (NIR) spectroscopy, especially a portable one of the type that has the benefits of low cost, portability, rapidity, ease of use, and mass deployability in both clinical and field settings. One obstacle to its effective application lies in its common limitations, which include relatively low specificity and general quality. Characteristically, the spectra curves show an interweaving feature for the virus-present and virus-absent samples. This then provokes the idea of using machine learning methods to overcome the difficulty. While a subsequent obstacle coincides with the fact that a direct deployment of the machine learning approaches leads to inadequate accuracy of the modelling results. This paper presents a data-driven study on the detection of two common respiratory viruses, the respiratory syncytial virus (RSV) and the Sendai virus (SEV), using a portable NIR spectrometer supported by a machine learning solution enhanced by an algorithm of variable selection via the Variable Importance in Projection (VIP) scores and its Quantile value, along with variable truncation processing, to overcome the obstacles to a certain extent. We conducted extensive experiments with the aid of the specifically developed algorithm of variable selection, using a total of four datasets, achieving classification accuracy of: (1) 0.88, 0.94, and 0.93 for RSV, SEV, and RSV + SEV, respectively, averaged over multiple runs, for the neural network modelling of taking in turn 3 sessions of data for training and the remaining one session of an 'unknown' dataset for testing. (2) the average accuracy of 0.94 (RSV), 0.97 (SEV), and 0.97 (RSV + SEV) for model validation and 0.90 (RSV), 0.93 (SEV), and 0.91 (RSV + SEV) for model testing, using two of the datasets for model training, one for model validation and the other for model testing. These results demonstrate the feasibility of using portable NIR spectroscopy coupled with machine learning to detect respiratory viruses with good accuracy, and the approach could be a viable solution for population screening.

Enhancing immune responses of ESC-based TAA cancer vaccines with a novel OMV delivery system.

Embryonic stem cell (ESC)-derived epitopes can act as therapeutic tumor vaccines against different types of tumors Jin (Adv Healthc Mater 2023). However, these epitopes have poor immunogenicity and stimulate insufficient CD8+ T cell responses, which motivated us to develop a new method to deliver and enhance their effectiveness. Bacterial outer membrane vesicles (OMVs) can serve as immunoadjuvants and act as a delivery vector for tumor antigens. In the current study, we engineered a new OMV platform for the co-delivery of ESC-derived tumor antigens and immune checkpoint inhibitors (PD-L1 antibody). An engineered Staphylococcal Protein A (SpA) was created to non-specifically bind to anti-PD-L1 antibody. SpyCatcher (SpC) and SpA were fused into the cell outer membrane protein OmpA to capture SpyTag-attached peptides and PD-L1 antibody, respectively. The modified OMV was able to efficiently conjugate with ESC-derived TAAs and PD-L1 antibody (SpC-OMVs + SpT-peptides + anti-PD-L1), increasing the residence time of TAAs in the body. The results showed that the combination therapy of ESC-based TAAs and PD-L1 antibody delivered by OMV had significant inhibitory effects in mouse tumor model. Specifically, it was effective in reducing tumor growth by enhancing IFN-γ-CD8+ T cell responses and increasing the number of CD8+ memory cells and antigen-specific T cells. Overall, the new OMV delivery system is a versatile platform that can enhance the immune responses of ESC-based TAA cancer vaccines.

Divergent trajectory of replication and intrinsic pathogenicity of SARS-CoV-2 Omicron post-BA.2/5 subvariants in the upper and lower respiratory tract.

BACKGROUND: Earlier Omicron subvariants including BA.1, BA.2, and BA.5 emerged in waves, with a subvariant replacing the previous one every few months. More recently, the post-BA.2/5 subvariants have acquired convergent substitutions in spike that facilitated their escape from humoral immunity and gained ACE2 binding capacity. However, the intrinsic pathogenicity and replication fitness of the evaluated post-BA.2/5 subvariants are not fully understood. METHODS: We systemically investigated the replication fitness and intrinsic pathogenicity of representative post-BA.2/5 subvariants (BL.1, BQ.1, BQ.1.1, XBB.1, CH.1.1, and XBB.1.5) in weanling (3-4 weeks), adult (8-10 weeks), and aged (10-12 months) mice. In addition, to better model Omicron replication in the human nasal epithelium, we further investigated the replication capacity of the post-BA.2/5 subvariants in human primary nasal epithelial cells. FINDINGS: We found that the evaluated post-BA.2/5 subvariants are consistently attenuated in mouse lungs but not in nasal turbinates when compared with their ancestral subvariants BA.2/5. Further investigations in primary human nasal epithelial cells revealed a gained replication fitness of XBB.1 and XBB.1.5 when compared to BA.2 and BA.5.2. INTERPRETATION: Our study revealed that the post-BA.2/5 subvariants are attenuated in lungs while increased in replication fitness in the nasal epithelium, indicating rapid adaptation of the circulating Omicron subvariants in the human populations. FUNDING: The full list of funding can be found at the Acknowledgements section.

Targeted IFNγ induction by a genetically engineered Salmonella typhimurium is the key to the liver metastasis inhibition in a mouse model of pancreatic neuroendocrine tumor.

Background: Liver metastasis is one of the primary causes of death for the patients with pancreatic neuroendocrine tumors (PNETs). However, no curative therapy has been developed so far. Methods: The anti-tumor efficacy of a genetically engineered tumor-targeting Salmonella typhimurium YB1 was evaluated on a non-functional INR1G9 liver metastasis model. Differential inflammatory factors were screened by Cytometric Bead Array. Antibody depletion assay and liver-targeted AAV2/8 expression vector were used for functional evaluation of the differential inflammatory factors. Results: We demonstrated that YB1 showed significant anti-tumor efficacy as a monotherapy. Since YB1 cannot infect INR1G9 cells, its anti-tumor effect was possibly due to the modulation of the tumor immune microenvironment. Two inflammatory factors IFNγ and CCL2 were elevated in the liver after YB1 administration, but only IFNγ was found to be responsible for the anti-tumor effect. Liver-targeted expression of IFNγ caused the activation of macrophages and NK cells, and reproduced the therapeutic effect of YB1 on liver metastasis. Conclusion: We demonstrated that YB1 may exhibit anti-tumor effect mainly based on IFNγ induction. Targeted IFNγ therapy can replace YB1 for treating liver metastasis of PNETs.

Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation.

The rapid diagnosis of respiratory virus infection through breath and blow remains challenging. Here we develop a wireless, battery-free, multifunctional pathogenic infection diagnosis system (PIDS) for diagnosing SARS-CoV-2 infection and symptom severity by blow and breath within 110 s and 350 s, respectively. The accuracies reach to 100% and 92% for evaluating the infection and symptom severity of 42 participants, respectively. PIDS realizes simultaneous gaseous sample collection, biomarker identification, abnormal physical signs recording and machine learning analysis. We transform PIDS into other miniaturized wearable or portable electronic platforms that may widen the diagnostic modes at home, outdoors and public places. Collectively, we demonstrate a general-purpose technology for rapidly diagnosing respiratory pathogenic infection by breath and blow, alleviating the technical bottleneck of saliva and nasopharyngeal secretions. PIDS may serve as a complementary diagnostic tool for other point-of-care techniques and guide the symptomatic treatment of viral infections.

Family-Specific Training Improves Linear B Cell Epitope Prediction for Emerging Viruses.

The rational design of vaccines and antibody-based therapeutics against newly emerging viruses relies on B cell epitopes mainly. To predict the B cell epitopes of a novel virus, several algorithms have been developed. While most existing algorithms are trained on a dataset in which B cell epitopes are classified as 'Positive' or 'Negative'. However, we found that training on such data contaminates the target pattern of specific viruses, leading to inaccurate predictions in some cases. In this paper, we introduce a novel framework for predicting linear B cell epitopes of novel viruses by exclusively using highly similar viruses for training data. We employed kernel regression based on seropositive rates, which are the percentages of seropositive samples among the population, to predict the potential epitopes. To assess our method, we conducted simulations and utilized two real-world datasets. Our method significantly outperformed other existing methods on the testing data of four viruses with seropositive rates. Also, our strategy showed a better prediction in a larger dataset from the IEDB. Thus, a novel framework providing better linear B cell prediction of newly emerging viruses is established, which will benefit the rational design of vaccines and antibody-based therapeutics in the future.

Rational design of a booster vaccine against COVID-19 based on antigenic distance.

Current COVID-19 vaccines are highly effective against symptomatic disease, but repeated booster doses using vaccines based on the ancestral strain offer limited additional protection against SARS-CoV-2 variants of concern (VOCs). To address this, we used antigenic distance to in silico select optimized booster vaccine seed strains effective against both current and future VOCs. Our model suggests that a SARS-CoV-1-based booster vaccine has the potential to cover a broader range of VOCs. Candidate vaccines including the spike protein from ancestral SARS-CoV-2, Delta, Omicron (BA.1), SARS-CoV-1, or MERS-CoV were experimentally evaluated in mice following two doses of the BNT162b2 vaccine. The SARS-CoV-1-based booster vaccine outperformed other candidates in terms of neutralizing antibody breadth and duration, as well as protective activity against Omicron (BA.2) challenge. This study suggests a unique strategy for selecting booster vaccines based on antigenic distance, which may be useful in designing future booster vaccines as new SARS-CoV-2 variants emerge.

Bacterial outer-membrane vesicles promote Vγ9Vδ2 T cell oncolytic activity.

Background: Increasing evidence suggests the immune activation elicited by bacterial outer-membrane vesicles (OMVs) can initiate a potent anti-tumor immunity, facilitating the recognition and destruction of malignant cells. At present the pathways underlying this response remain poorly understood, though a role for innate-like cells such as γδ T cells has been suggested. Methods: Peripheral blood mononuclear cells (PBMCs) from healthy donors were co-cultured with E. coli MG1655 Δpal ΔlpxM OMVs and corresponding immune activation studied by cell marker expression and cytokine production. OMV-activated γδ T cells were co-cultured with cancer cell lines to determine cytotoxicity. Results: The vesicles induced a broad inflammatory response with γδ T cells observed as the predominant cell type to proliferate post-OMV challenge. Notably, the majority of γδ T cells were of the Vγ9Vδ2 type, known to respond to both bacterial metabolites and stress markers present on tumor cells. We observed robust cytolytic activity of Vγ9Vδ2 T cells against both breast and leukaemia cell lines (SkBr3 and Nalm6 respectively) after OMV-mediated expansion. Conclusions: Our findings identify for the first time, that OMV-challenge stimulates the expansion of Vγ9Vδ2 T cells which subsequently present anti-tumor capabilities. We propose that OMV-mediated immune activation leverages the anti-microbial/anti-tumor capacity of Vγ9Vδ2 T cells, an axis amenable for improved future therapeutics.

Discovery of potent and selective HPK1 inhibitors based on the 2,4-disubstituted pyrimidine scaffold with immune modulatory properties for ameliorating T cell exhaustion.

Hematopoietic progenitor kinase 1 (HPK1), a member of the mitogen-activated protein kinase (MAP4K) family, is a serine/threonine (SER/THR) kinase and has been demonstrated as a negative regulator of T cell receptor signaling. Targeting HPK1 has been considered as an attractive therapeutic strategy for immune-oncology. Here, we describe the discovery and structure-activity relationship (SAR) of potent HPK1 inhibitors based on the 2,4-disubstituted pyrimidine scaffold. Systematically SAR exploration afforded the desired compound HMC-H8 (F1) with potent HPK1 inhibition (IC50 = 1.11 nM) and highly selectivity profile. Compound HMC-H8 also exhibited robust inhibition of p-SLP 76 (IC50 = 283.0 nM) and promotion IL-2 release (EC50 = 157.08 nM), and INF-γ production in a dose-dependent manner in vitro assays. Strikingly, HMC-H8 shown effective immune reversal response in immunesuppressive condition. Moreover, Compound HMC-H8 displayed acceptable metabolic stability (T1/2 = 56.87 min), along with low CYP450 inhibition in human liver microsomes and good oral bioavailability (F = 15.05%) in rat. Furthermore, HMC-H8 was found to modulate the expression of c-Myc in Western blotting experiments. Taken together, this study provides new potent HPK1 inhibitors for further anticancer drug discovery based on immuno-oncology.

The viral fitness and intrinsic pathogenicity of dominant SARS-CoV-2 Omicron sublineages BA.1, BA.2, and BA.5.

BACKGROUND: Among the Omicron sublineages that have emerged, BA.1, BA.2, BA.5, and their related sublineages have resulted in the largest number of infections. While recent studies demonstrated that all Omicron sublineages robustly escape neutralizing antibody response, it remains unclear on whether these Omicron sublineages share any pattern of evolutionary trajectory on their replication efficiency and intrinsic pathogenicity along the respiratory tract. METHODS: We compared the virological features, replication capacity of dominant Omicron sublineages BA.1, BA.2 and BA.5 in the human nasal epithelium, and characterized their pathogenicity in K18-hACE2, A129, young C57BL/6, and aged C57BL/6 mice. FINDINGS: We found that BA.5 replicated most robustly, followed by BA.2 and BA.1, in the differentiated human nasal epithelium. Consistently, BA.5 infection resulted in higher viral gene copies, infectious viral titres and more abundant viral antigen expression in the nasal turbinates of the infected K18-hACE2 transgenic mice. In contrast, the Omicron sublineages are continuously attenuated in lungs of infected K18-hACE2 and C57BL/6 mice, leading to decreased pathogenicity. Nevertheless, lung manifestations remain severe in Omicron sublineages-infected A129 and aged C57BL/6 mice. INTERPRETATION: Our results suggested that the Omicron sublineages might be gaining intrinsic replication fitness in the upper respiratory tract, therefore highlighting the importance of global surveillance of the emergence of hyper-transmissive Omicron sublineages. On the contrary, replication and intrinsic pathogenicity of Omicron is suggested to be further attenuated in the lower respiratory tract. Effective vaccination and other precautions should be in place to prevent severe infections in the immunocompromised populations at risk. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

A smart and visible way to switch the aromaticity of silicon(IV) phthalocyanines.

Unlike traditional methods of modifying phthalocyanines (Pcs), we herein report a smart and visible way to switch the aromaticity of silicon(IV) phthalocyanines via a reversible nucleophilic addition reaction of the Pc skeleton induced by alkalis and acids, leading to an interesting allochroism phenomenon and the switching of photosensitive activities.

A Bayesian approach to estimate MHC-peptide binding threshold.

Major histocompatibility complex (MHC)-peptide binding is a critical step in enabling a peptide to serve as an antigen for T-cell recognition. Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.