Single-chain fluorescent integrators for mapping G-protein-coupled receptor agonists.

G protein-coupled receptors (GPCRs) transduce the effects of many neuromodulators including dopamine, serotonin, epinephrine, acetylcholine, and opioids. The localization of synthetic or endogenous GPCR agonists impacts their action on specific neuronal pathways. In this paper, we show a series of single-protein chain integrator sensors that are highly modular and could potentially be used to determine GPCR agonist localization across the brain. We previously engineered integrator sensors for the mu- and kappa-opioid receptor agonists called M- and K-Single-chain Protein-based Opioid Transmission Indicator Tool (SPOTIT), respectively. Here, we engineered red versions of the SPOTIT sensors for multiplexed imaging of GPCR agonists. We also modified SPOTIT to create an integrator sensor design platform called SPOTIT for all GPCRs (SPOTall). We used the SPOTall platform to engineer sensors for the beta 2-adrenergic receptor (B2AR), the dopamine receptor D1, and the cholinergic receptor muscarinic 2 agonists. Finally, we demonstrated the application of M-SPOTIT and B2AR-SPOTall in detecting exogenously administered morphine, isoproterenol, and epinephrine in the mouse brain via locally injected viruses. The SPOTIT and SPOTall sensor design platform has the potential for unbiased agonist detection of many synthetic and endogenous neuromodulators across the brain.

CherryML: scalable maximum likelihood estimation of phylogenetic models.

Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryML's utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.

A general method for chemogenetic control of peptide function.

Natural or engineered peptides serve important biological functions. A general approach to achieve chemical-dependent activation of short peptides will be valuable for spatial and temporal control of cellular processes. Here we present a pair of chemically activated protein domains (CAPs) for controlling the accessibility of both the N- and C-terminal portion of a peptide. CAPs were developed through directed evolution of an FK506-binding protein. By fusing a peptide to one or both CAPs, the function of the peptide is blocked until a small molecule displaces them from the FK506-binding protein ligand-binding site. We demonstrate that CAPs are generally applicable to a range of short peptides, including a protease cleavage site, a dimerization-inducing heptapeptide, a nuclear localization signal peptide, and an opioid peptide, with a chemical dependence up to 156-fold. We show that the CAPs system can be utilized in cell cultures and multiple organs in living animals.

NLRP12 Senses the SARS-CoV-2 Membrane Protein and Promotes an Inflammatory Response.

COVID-19 is an acute respiratory disorder that is caused by SARS-CoV-2, in which excessive systemic inflammation is associated with adverse patient clinical outcomes. Here, we observed elevated expression levels of NLRP12 (nucleotide-binding leucine-rich repeat-containing receptor 12) in human peripheral monocytes and lung tissue during infection with SARS-CoV-2. Co-immunoprecipitation analysis revealed that NLRP12 directly interacted with the M protein through its leucine-rich repeat domain. Moreover, in vitro studies demonstrated that NLRP12 interacted with TRAF3 and promoted its ubiquitination and degradation, which counteracted the inhibitory effect of TRAF3 on the NF-κB/MAPK signaling pathway and promoted the production of inflammatory cytokines. Furthermore, an in vivo study revealed that NLRP12 knockout mice displayed attenuated tissue injury and ameliorated inflammatory responses in the lungs when infected with a SARS-CoV-2 M protein-reconstituted pseudovirus and mouse coronavirus. Taken together, these findings suggest that NLRP12 mediates the inflammatory responses during coronavirus infection.

HLA3D: an integrated structure-based computational toolkit for immunotherapy.

MOTIVATION: The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism. RESULT: To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

Multimodal representation learning for predicting molecule-disease relations.

MOTIVATION: Predicting molecule-disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule-molecule, molecule-disease and disease-disease semantic dependencies can potentially improve prediction performance. METHODS: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule-disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. RESULTS: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture.

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. This study developed a novel and efficient digital pathology classifier called DPSeq to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizontal and vertical bidirectional long short-term memory networks. Using hematoxylin and eosin-stained histopathologic images of colorectal cancer from two international data sets (The Cancer Genome Atlas and Molecular and Cellular Oncology), the predictive performance of DPSeq was evaluated in a series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in colorectal cancer (microsatellite instability status, hypermutation, CpG island methylator phenotype status, BRAF mutation, TP53 mutation, and chromosomal instability), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. In addition, under the same experimental conditions using the same set of training and testing data sets, DPSeq surpassed four CNNs (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and two transformer (Vision Transformer and Swin Transformer) models, achieving the highest area under the receiver operating characteristic curve and area under the precision-recall curve values in predicting microsatellite instability status, BRAF mutation, and CpG island methylator phenotype status. Furthermore, DPSeq required less time for both training and prediction because of its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

The impacts of biologic treatment on metabolic profiling in psoriasis.

Psoriasis is an immune-mediated inflammatory disease commonly accompanied by various metabolic disorders. It is widely known that biologics could affect the metabolic status and comorbidities in psoriasis patients, however, the effects of biologics on metabolism in psoriasis patients remain poorly understood. The aim of this study was to elucidate the characteristic changes of metabolic profiling in psoriasis vulgaris (PsV) patients before and after applying biologics. Plasma samples were collected from a retrospective cohort of 43 PsV patients. Non-targeted metabolomics analyses were performed using liquid chromatography-mass spectrometry (LC-MS) to compare the metabolic profiles before and after applying adalimumab (ADA) or ixekizumab (IXE) for 4 weeks. Additionally, correlation analyses were conducted to investigate the associations between metabolite expression levels and clinical characteristics. The biologics significantly affected the metabolic profiles of PsV patients especially in glycerophospholipids (GPs). First, phosphatidylcholine (PC), unsaturated lysophosphatidylcholine (LPC), unsaturated lysophosphatidic acid (LPA) and unsaturated lysophosphatidylethanolamine (LPE) were significantly up-regulated, whereas phosphatidylethanolamine (PE), saturated LPC, saturated LPA and saturated LPE were predominantly down-regulated after biologic treatment. What is more, the changes in PE and LPA were mainly observed after applying IXE instead of ADA. Second, we also found GPs including PC, unsaturated LPC, unsaturated LPA and unsaturated LPE were primarily negatively correlated with disease severity, whereas, PE, saturated LPC, saturated LPA and saturated LPE displayed inverse correlations. Biologics could affect GP metabolism and facilitate the transition of metabolic status from a pro-inflammatory to an anti-inflammatory phenotype in PsV patients.

TLT-1 Promotes Platelet-Monocyte Aggregate Formation to Induce IL-10-Producing B Cells in Tuberculosis.

The immunoregulation of platelets and platelet-monocyte aggregates (PMAs) is increasingly recognized, but it roles in tuberculosis (TB) remain to be elucidated. In this study, we found that CD14+CD41+ PMAs were increased in peripheral blood of patients with active TB. CD14+CD41+ PMAs highly expressed triggering receptors expressed on myeloid cells (TREMs)-like transcript-1 (TLT-1), P-selectin (CD62P), and CD40L. Our in vitro study found that platelets from patients with active TB aggregate with monocytes to induce IL-1ß and IL-6 production by monocytes. Importantly, we identified that TLT-1 was required for formation of PMAs. The potential TLT-1 ligand was expressed and increased on CD14+ monocytes of patients with TB determined by using TLT-1 fusion protein (TLT-1 Fc). Blocking of ligand-TLT-1 interaction with TLT-1 Fc reduced PMA formation and IL-1ß and IL-6 production by monocytes. Further results demonstrated that PMAs induced IL-10 production by B cells (B10) dependent on IL-1ß, IL-6, and CD40L signals in a coculture system. Moreover, TLT-1 Fc treatment suppressed B10 polarization via blocking PMA formation. Taking all of these data together, we elucidated that TLT-1 promoted PMA-mediated B10 polarization through enhancing IL-1ß, IL-6, and CD40L origin from PMAs, which may provide potential targeting strategies for TB disease treatment.

Circulating blood biomarkers correlated with the prognosis of advanced triple negative breast cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) can improve survivals of metastatic triple negative breast cancer (mTNBC); however, we still seek circulating blood biomarkers to predict the efficacy of ICIs. MATERIALS AND METHODS: In this study, we analyzed the data of ICIs treated mTNBC collected in Anhui Medical University affiliated hospitals from 2018 to 2023. The counts of lymphocytes, monocytes, platelets, and ratio indexes (NLR, MLR, PLR) in peripheral blood were investigated via the Kaplan-Meier curves and the Cox proportional-hazards model. RESULTS: The total of 50 mTNBC patients were treated with ICIs. High level of peripheral lymphocytes and low level of NLR and MLR at baseline and post the first cycle of ICIs play the predictable role of immunotherapies. Lymphocytes counts (HR = 0.280; 95% CI: 0.095-0.823; p = 0.021) and NLR (HR = 1.150; 95% CI: 1.052-1.257; p = 0.002) are significantly correlated with overall survival. High NLR also increases the risk of disease progression (HR = 2.189; 95% CI:1.085-4.414; p = 0.029). When NLR at baseline ≥ 2.75, the hazard of death (HR = 2.575; 95% CI:1.217-5.447; p = 0.013) and disease progression (HR = 2.189; 95% CI: 1.085-4.414; p = 0.029) significantly rise. HER-2 expression and anti-tumor therapy lines are statistically correlated with survivals. CONCLUSIONS: Before the initiation of ICIs, enriched peripheral lymphocytes and poor neutrophils and NLR contribute to the prediction of survivals.

Discovery of a Novel Chromone Enantiomer and the Precursors of Nonactic Acid from the Coral-Reef-Derived Streptomyces sp. SCSIO 66814.

Three pairs of enantiomers (1-3)-the new 12R-aloesol (1a) and two new fatty acids (2 and 3)-and one new natural product (4) together three known compounds (5-7) were isolated from a coral-reef-derived Streptomyces sp. SCSIO 66814. Their structures were determined through extensive spectroscopic analysis, chiral analysis, and single-crystal X-ray diffraction data. Compounds 2 and 3 were presumed to be intermediates for further generating homononactic acid (5) and nonactic acid, and the latter two molecules were able to act as precursors to form macrotetrolides with remarkable biological activity. The isolation of related precursors, compounds 2-5, provided more evidence to support the proposal of a plausible biosynthetic pathway for nonactic acid and its homologs. Additionally, (+)-1 exhibited a weak activity against DPPH radicals.

2,3-Dimethoxycinnamic Acid from a Marine Actinomycete, a Promising Quorum Sensing Inhibitor in Chromobacterium violaceum.

An ethyl acetate extract of a marine actinomycete strain, Nocardiopsis mentallicus SCSIO 53858, isolated from a deep-sea sediment sample in the South China Sea, exhibited anti-quorum-sensing (QS) activity against Chromobacterium violaceum CV026. Guided by the anti-QS activity, a novel active compound was isolated and purified from the extract and was identified as 2,3-dimethoxycinnamic acid (2,3-DCA) through spectral data analysis. At a concentration of 150 µg/mL, 2,3-DCA exhibited robust inhibitory effects on three QS-regulated traits of C. violaceum CV026: violacein production, swarming motility, and biofilm formation, with inhibition rates of 73.9%, 65.9%, and 37.8%, respectively. The quantitative reverse transcription polymerase chain reaction results indicated that 2,3-DCA can disrupt the QS system in C. violaceum CV026 by effectively suppressing the expression of QS-related genes, including cviR, vioA, vioB, and vioE. Molecular docking analysis revealed that 2,3-DCA hinders the QS system by competitively binding to the same binding pocket on the CviR receptor as the natural signal molecule N-hexanoyl-L-homoserine lactone. Collectively, these findings suggest that 2,3-DCA exhibits promising potential as an inhibitor of QS systems, providing a potential solution to the emerging problem of bacterial resistance.

Passion matters but not equally everywhere: Predicting achievement from interest, enjoyment, and efficacy in 59 societies.

How to identify the students and employees most likely to achieve is a challenge in every field. American academic and lay theories alike highlight the importance of passion for strong achievement. Based on a Western independent model of motivation, passionate individuals-those who have a strong interest, demonstrate deep enjoyment, and express confidence in what they are doing-are considered future achievers. Those with less passion are thought to have less potential and are often passed over for admission or employment. As academic institutions and corporations in the increasingly multicultural world seek to acquire talent from across the globe, can they assume that passion is an equally strong predictor of achievement across cultural contexts? We address this question with three representative samples totaling 1.2 million students in 59 societies and provide empirical evidence of a systematic, cross-cultural variation in the importance of passion in predicting achievement. In individualistic societies where independent models of motivation are prevalent, relative to collectivistic societies where interdependent models of motivation are more common, passion predicts a larger gain (0.32 vs. 0.21 SD) and explains more variance in achievement (37% vs. 16%). In contrast, in collectivistic societies, parental support predicts achievement over and above passion. These findings suggest that in addition to passion, achievement may be fueled by striving to realize connectedness and meet family expectations. Findings highlight the risk of overweighting passion in admission and employment decisions and the need to understand and develop measures for the multiple sources and forms of motivation that support achievement.

Pathogenic recurrent copy number variants in 7,078 pregnancies via chromosomal microarray analysis.

OBJECTIVES: To investigate the incidence of pathogenic recurrent CNVs in fetuses with different referral indications and review the intrauterine phenotypic features of each CNV. METHODS: A total of 7,078 amniotic fluid samples were collected for chromosome microarray analysis (CMA) and cases carrying pathogenic recurrent CNVs were further studied. RESULTS: The highest incidence of pathogenic recurrent CNVs was 2.25 % in fetal ultrasound anomalies (FUA) group. Moreover, regardless of other indications, pregnant women with advanced maternal age have a lower incidence compared with whom less than 35 years old (p<0.05). In total 1.17 % (83/7,078) samples carried pathogenic recurrent CNVs: 20 cases with 22q11.2 recurrent region (12 microdeletion and eight microduplication), 11 with 1q21.1 (five microdeletion and six microduplication) and 16p13.11 (four microdeletion and seven microduplication), 10 with 15q11.2 recurrent microdeletion, seven with Xp22.31 recurrent microdeletion and 16p11.2 (three microdeletion and four microduplication), four with 7q11.23 (two microdeletion and two microduplication), three with 17p11.2 (three microdeletion), 17p12 (two microdeletion and one microduplication) and 17q12 (two microdeletion and one microduplication). The rest ones were rare in this study. CONCLUSIONS: Pathogenic recurrent CNVs are more likely to be identified in FUA group. Pregnant women with advanced maternal age have a lower incidence of pathogenic recurrent CNVs. The profile of pathogenic recurrent CNVs between prenatal and postnatal is different, especially in 22q11.2, 1q21.1, 15q13.3 recurrent region and 15q11.2 deletion.

Comprehensive review of emerging contaminants: Detection technologies, environmental impact, and management strategies.

Emerging contaminants (ECs) are a diverse group of unregulated pollutants increasingly present in the environment. These contaminants, including pharmaceuticals, personal care products, endocrine disruptors, and industrial chemicals, can enter the environment through various pathways and persist, accumulating in the food chain and posing risks to ecosystems and human health. This comprehensive review examines the chemical characteristics, sources, and varieties of ECs. It critically evaluates the current understanding of their environmental and health impacts, highlighting recent advancements and challenges in detection and analysis. The review also assesses existing regulations and policies, identifying shortcomings and proposing potential enhancements. ECs pose significant risks to wildlife and ecosystems by disrupting animal hormones, causing genetic alterations that diminish diversity and resilience, and altering soil nutrient dynamics and the physical environment. Furthermore, ECs present increasing risks to human health, including hormonal disruptions, antibiotic resistance, endocrine disruption, neurological effects, carcinogenic effects, and other long-term impacts. To address these critical issues, the review offers recommendations for future research, emphasizing areas requiring further investigation to comprehend the full implications of these contaminants. It also suggests increased funding and support for research, development of advanced detection technologies, establishment of standardized methods, adoption of precautionary regulations, enhanced public awareness and education, cross-sectoral collaboration, and integration of scientific research into policy-making. By implementing these solutions, we can improve our ability to detect, monitor, and manage ECs, reducing environmental and public health risks.

Bridging Histopathology and Radiomics Toward Prognosis of Metastasis in Early Breast Cancer.

Tumor histomorphology is crucial for the prognostication of breast cancer outcomes because it contains histological, cellular, and molecular tumor heterogeneity related to metastatic potential. To enhance breast cancer prognosis, we aimed to apply radiomics analysis-traditionally used in 3D scans-to 2D histopathology slides. This study tested radiomics analysis in a cohort of 92 breast tumor specimens for outcome prognosis, addressing -omics dimensionality by comparing models with moderate and high feature counts, using least absolute shrinkage and selection operator for feature selection and machine learning for prognostic modeling. In the test folds, models with radiomics features [area under the curves (AUCs) range 0.799-0.823] significantly outperformed the benchmark model, which only included clinicopathological (CP) parameters (AUC = 0.584). The moderate-dimensionality model with 11 CP + 93 radiomics features matched the performance of the highly dimensional models with 1,208 radiomics or 11 CP + 1,208 radiomics features, showing average AUCs of 0.823, 0.799, and 0.807 and accuracies of 79.8, 79.3, and 76.6%, respectively. In conclusion, our application of deep texture radiomics analysis to 2D histopathology showed strong prognostic performance with a moderate-dimensionality model, surpassing a benchmark based on standard CP parameters, indicating that this deep texture histomics approach could potentially become a valuable prognostic tool.

Fengycin produced by Bacillus subtilis XF-1 plays a major role in the biocontrol of Chinese cabbage clubroot via direct effect and defense stimulation.

Bacillus subtilis XF-1 is a well-investigated biocontrol agent against the biotrophic Plasmodiophora brassicae Woron., the causal agent of clubroot disease of cruciferous crops. The present study demonstrates that XF-1 could efficiently control clubroot disease via leaf spraying and provides an understanding of the biocontrol mechanisms. High-performance thin-layer chromatography (HTPLC) analysis indicated the presence of fengycin-type cyclopeptides in the supernatant. A ppsB deletion mutant of XF-1 resulted in no fengycin production, significantly reduced the lysis rate of testing spores in vitro and the primary infection rate of root hair in vivo, and decreased the protection value against clubroot disease under the greenhouse conditions. Confocal laser scanning microscopy proved that fengycin was not required for leaf internalization and root colonization. Moreover, the expression level of the ppsB gene in XF-1 was regulated by its cell density in root during interaction with P. brassicae. In addition, the ΔppsB mutant of XF-1 could not efficiently control disease because it led to a lower activation level of the jasmonic acid and salicylic acid signaling pathways in roots, which are necessary for the plant defense reaction upon pathogen invasion. Altogether, the present study provides a new understanding of specific cues in the interaction between B. subtilis and P. brassicae as well as insights into the application of B. subtilis in agriculture.

Steroidal saponin profiles and their key genes for synthesis and regulation in Asparagus officinalis L. by joint analysis of metabolomics and transcriptomics.

BACKGROUND: Asparagus officinalis L. is a worldwide cultivated vegetable enrichened in both nutrient and steroidal saponins with multiple pharmacological activities. The upstream biosynthetic pathway of steroidal saponins (USSP) for cholesterol (CHOL) synthesis has been studied, while the downstream pathway of steroidal saponins (DSSP) starting from cholesterol and its regulation in asparagus remains unknown. RESULTS: Metabolomics, Illumina RNAseq, and PacBio IsoSeq strategies were applied to different organs of both cultivated green and purple asparagus to detect the steroidal metabolite profiles & contents and to screen their key genes for biosynthesis and regulation. The results showed that there is a total of 427 compounds, among which 18 steroids were detected with fluctuated concentrations in roots, spears and flowering twigs of two garden asparagus cultivars. The key genes of DSSP include; steroid-16-hydroxylase (S16H), steroid-22-hydroxylase (S22H) and steroid-22-oxidase-16-hydroxylase (S22O-16H), steroid-26-hydroxylase (S26H), steroid-3-ß-glycosyltransferase (S3ßGT) and furostanol glycoside 26-O-beta-glucosidases (F26GHs) which were correlated with the contents of major steroidal saponins were screened, and the transcriptional factors (TFs) co-expressing with the resulted from synthetic key genes, including zinc fingers (ZFs), MYBs and WRKYs family genes were also screened. CONCLUSIONS: Based on the detected steroidal chemical structures, profiles and contents which correlated to the expressions of screened synthetic and TFs genes, the full steroidal saponin synthetic pathway (SSP) of asparagus, including its key regulation networks was proposed for the first time.

Collaborative Passivation for Dual Charge Transporting Layers Based on 4-(chloromethyl)benzonitrile Additive toward Efficient and Stable Inverted Perovskite Solar Cells.

Poor carrier transport capacity and numerous surface defects of charge transporting layers (CTLs), coupled with misalignment of energy levels between perovskites and CTLs, impact photoelectric conversion efficiency (PCE) of inverted perovskite solar cells (PSCs) profoundly. Herein, a collaborative passivation strategy is proposed based on 4-(chloromethyl) benzonitrile (CBN) as a solution additive for fabrication of both [6,6]-phenyl-C61-butyric acid methylester (PCBM) and poly(triarylamine) (PTAA) CTLs. This additive can improve wettability of PTAA and reduce the agglomeration of PCBM particles, which enhance the PCE and device stability of the PSCs. As a result, a PCE exceeding 20% with a remarkable short circuit current of 23.9 mA cm-2 , and an improved fill factor of 81% is obtained for the CBN- modified inverted PSCs. Devices maintain 80% and 70% of the initial PCE after storage under 30% and 85% humidity ambient conditions for 1000 h without encapsulation, as well as negligible light state PCE loss. This strategy demonstrates feasibility of the additive engineering to improve interfacial contact between the CTLs and perovskites for fabrication of efficient and stable inverted PSCs.

CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome.

The global outbreak of the monkeypox virus (MPXV) highlights the need for rapid and cost-effective MPXV detection tools to effectively monitor and control the monkeypox disease. Herein, we demonstrated a portable CRISPR-Cas-based system for naked-eye detection of MPXV. The system harnesses the high selectivity of CRISPR-Cas12 and the isothermal nucleic acid amplification potential of recombinase polymerase amplification. It can detect both the current circulating MPXV clade and the original clades. We reached a limit of detection (LoD) of 22.4 aM (13.5 copies/µl) using a microtiter plate reader, while the visual LoD of the system is 75 aM (45 copies/µl) in a two-step assay, which is further reduced to 25 aM (15 copies/µl) in a one-pot system. We compared our results with quantitative polymerase chain reaction and obtained satisfactory consistency. For clinical application, we demonstrated a sensitive and precise visual detection method with attomolar sensitivity and a sample-to-answer time of 35 min.