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Conjugated polymers are generally featured with low structural order due to their aromatic and irregular structural units, which limits their light absorption and charge mobility in organic solar cells. In this work, we report a conjugated molecule INMB-F that can act as a molecular bridge via electrostatic force to enhance the intermolecular stacking of BDT-based polymer donors toward efficient and stable organic solar cells. Molecular dynamics simulations and synchrotron X-ray measurements reveal that the electronegative INMB-F adsorb on the electropositive main chain of polymer donors to increase the donor-donor interactions, leading to enhanced structural order with shortened π-π stacking distance and consequently enhanced charge transport ability. Casting the non-fullerene acceptor layer on top of the INMB-F modified donor layer to fabricate solar cells via layer-by-layer deposition evidences significant power conversion efficiency boosts in a range of photovoltaic systems. A power conversion efficiency of 19.4% (certified 18.96%) is realized in PM6/L8-BO binary devices, which is one of the highest reported efficiencies of this material system. The enhanced structural order of polymer donors by INMB-F also leads to a six-fold enhancement of the operational stability of PM6/L8-BO organic solar cells. The low structural order of conjugated polymers limits their photovoltaic properties in organic solar cells. Here, the authors report a conjugated molecule as molecular bridge via electrostatic force for enhancing intermolecular packing, achieving certified efficiency close to 19% in binary devices.

H 2 O dissociation plays a crucial role in solar-driven catalytic CO 2 methanation, demanding high temperature even for solar-to-chemical conversion efficiencies <1% with modest product selectivity. Herein, we report an oxygen-vacancy (V o ) rich CeO 2 catalyst with single-atom Ni anchored around its surface V o sites by replacing Ce atoms to promote H 2 O dissociation and achieve effective photothermal CO 2 reduction under concentrated light irradiation. The high photon flux reduces the apparent activation energy for CH 4 production and prevents V o from depletion. The defects coordinated with single-atom Ni, significantly promote the capture of charges and local phonons at the Ni d -impurity orbitals, thereby inducing more effective H 2 O activation. The catalyst presents a CH 4 yield of 192.75 µmol/cm 2 /h, with a solar-to-chemical efficiency of 1.14% and a selectivity ~100%. The mechanistic insights uncovered in this study should help further the development of H 2 O-activating catalysts for CO 2 reduction and thereby expedite the practical utilization of solar-to-chemical technologies. This work reports a single-atom Ni incorporated CeO 2 catalyst that boosts the efficiency of solar CO 2 reduction under concentrated light irradiation.

The photoactive layer of organic solar cells consists of p-type electron donors and n-type electron acceptors, which phase separate to form fine and continuous networks for charge transport. The impact of the donor–acceptor interaction on the microstructure and optoelectronics of the photoactive layer remains unclear. In this work, a tiny amount (1 wt%) of donor PM6 is added into the non-fullerene acceptor (NFA) C8- R or L8-BO (or vice versa) to form a donor (or acceptor) diluted heterojunction. The structural order is improved through dipole–dipole interaction between the donor and the acceptor owing to their opposite electronegativity. We fabricate a pseudo-bilayer heterojunction solar cell based on NFA-diluted donor (that is, donor + 1% NFA) and donor-diluted NFA (that is, NFA + 1% donor) layers: the device exhibits superior power conversion efficiencies compared with their bulk heterojunction and conventional pseudo-bilayer counterparts. We demonstrate an efficiency of 19.4% (certified 19.1%) and 17.6% for 100 and 300 nm-thick PM6 + 1% L8-BO/L8-BO + 1% PM6 solar cells, respectively. Wang et al. show that a small amount of donor in the acceptor layer or vice versa induces structural order owing to dipole–dipole interaction between the donor and the acceptor, enabling a certified efficiency of 19.1% in pseudo-bilayer organic solar cells.

Elsinochromes (ESCs) are virulence factors produced by Elsinoë arachidis which is the cause of peanut scab. However, the biosynthesis pathway of ESCs in E . arachidis has not been elucidated and the potential pathogenic mechanism of E . arachidis is poorly understood. In this study, we report a high-quality genome sequence of E . arachidis . The size of the E . arachidis genome is 33.18Mb, which is comparable to the Ascomycota genome (average 36.91 Mb), encoding 9174 predicted genes. The self-detoxification family including transporters and cytochrome P450 enzymes were analysis, candidate effectors and cell wall degrading enzymes were investigated as the pathogenicity genes by using PHI and CAZy databases. Additionally, the E . arachidis genome contains 24 secondary metabolism gene clusters, in which ESCB1 was identified as the core gene of ESC biosynthesis. Taken together, the genome sequence of E . arachidis provides a new route to explore its potential pathogenic mechanism and the biosynthesis pathway of ESCs.

The climate crisis necessitates the development of non-fossil energy sources. Harnessing solar energy for fuel production shows promise and offers the potential to utilize existing energy infrastructure. However, solar fuel production is in its early stages of development, constrained by low conversion efficiency and challenges in scaling up production. Concentrated solar energy (CSE) technology has matured alongside the rapid growth of solar thermal power plants. This review provides an overview of current CSE methods and solar fuel production, analyzes their integration compatibility, and delves into the theoretical mechanisms by which CSE impacts solar energy conversion efficiency and product selectivity in the context of photo-electrochemistry, thermochemistry, and photo-thermal co-catalysis for solar fuel production. The review also summarizes approaches to studying the photoelectric and photothermal effects of CSE. Lastly, it explores emerging novel CSE technology methods in the field of solar fuel production. [Display omitted] •The concept of concentrating solar energy and solar fuel production are introduced.•Solar fuel production integrated with concentrating solar energy is reviewed.•Photoelectric and photothermal effects of concentrating solar energy are reviewed.•Novel devices for solar fuel production by concentrating light are discussed.

This study aims to construct a real-time deep convolutional neural networks (DCNNs) system to diagnose early esophageal squamous cell carcinoma (ESCC) with white light imaging endoscopy. A total of 4,002 images from 1,078 patients were used to train and cross-validate the DCNN model for diagnosing early ESCC. The performance of the model was further tested with independent internal and external validation data sets containing 1,033 images from 243 patients. The performance of the model was then compared with endoscopists. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen kappa coefficient were measured to assess performance. The DCNN model had excellent performance in diagnosing early ESCC with a sensitivity of 0.979, a specificity of 0.886, a positive predictive value of 0.777, a negative predictive value of 0.991, and an area under curve of 0.954 in the internal validation data set. The model also depicted a tremendously generalized performance in 2 external data sets and exhibited superior performance compared with endoscopists. The performance of the endoscopists was markedly elevated after referring to the predictions of the DCNN model. An open-accessed website of the DCNN system was established to facilitate associated research. A real-time DCNN system, which was constructed to diagnose early ESCC, showed good performance in validation data sets. However, more prospective validation is needed to understand its true clinical significance in the real world.

Background Risk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment. Methods High-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated. Results High-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB , CEP55 , SRSF3 , TCF7L2 , and FOXP1 ) and the SE-derived LMFS-signature (comprising SRSF3 , TCF7L2 , FOXP1 , and APOLD1 ). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors. Conclusions Oncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

Glioblastoma is a lethal brain cancer marked by functional plasticity driven by tumor cell-intrinsic mutations and their interplay with developmental programs. To investigate how canonical glioblastoma mutations promote functional plasticity, we have developed an isogenic human neural stem cell (NSC) model of glioblastoma by sequential addition of TERT promoter, TP53 , and PDGFRA point mutations. TP53 loss-of-function increases TERT expression during serial mutagenesis, but only triple mutant NSCs reliably form lethal brain tumors in vivo that recapitulate glioblastoma. Tumor cell evolution triggers stress-related metabolic changes and transitions toward a neuronal progenitor network driven by transcription factor INSM1. INSM1 is highly expressed in human glioblastoma tumors and, during cortical development, in intermediate progenitor cells, which give rise to neurons. Remarkably, INSM1 knockdown in triple mutant NSCs and primary glioblastoma cells disrupts oncogenic gene expression and function and inhibits the in vivo tumorigenicity of triple mutant NSCs, highlighting the functional importance of an intermediate progenitor cell-like cell state in glioblastoma pathogenesis. Glioblastoma (GBM) is characterized by a high degree of heterogeneity and plasticity due to interplay with neural developmental programs. Here, the authors develop a model of GBM by introducing sequential oncogenic mutations in human neural stem cells and using this, identify INSM1 as a driver of a neural progenitor gene network promoting tumorigenesis.

Objectives Over the past three decades, orthopedic surgical robots have experienced rapid advancements. This study, a case series, aimed to investigate the effectiveness, limitations, and technical improvements associated with the application of robots in the surgical treatment of bone tumors. Methods From November 2021 to October 2023, 54 patients with bone tumors who provided consent for robot‐assisted surgery were included. Patients were divided into three groups based on specific objectives: robot‐assisted path planning, pedicle screw insertion, and intraoperative real‐time navigation‐assisted tumor resection. Perioperative conditions were meticulously recorded for all patients, including intraoperative blood loss, operation duration, postoperative complications, and tumor diameter. Results Nineteen patients underwent robot‐assisted tissue biopsies, and pathological examinations confirmed a positive rate of 84.21%. Among the 21 patients undergoing robot‐assisted pedicle screw placement, surgical planning was executed with high accuracy. Twenty patients undergoing robot‐assisted lesion excision achieved precise resection of the tumor‐affected bone segments as planned preoperatively, and no secondary osteotomies were required. No perioperative complications related to the use of robots were observed in the 54 patients. To address the limitation of orthopedic robots in differentiating soft tissues, we integrated ultrasound technology and the da Vinci robot. Additionally, patient‐specific cutting guides were utilized to compensate for the prolonged operation time associated with planar planning using orthopedic robots. Conclusions Robot‐assisted technology facilitates the precise planning of the surgical path and determination of the osteotomy plane. The integration of orthopedic robots with intraoperative ultrasound or Da Vinci robots can potentially further ensure the safety of bone tumor surgery while maintaining its accuracy, thereby minimizing the risk of complications associated with surgical procedures. Furthermore, this technology combined with patient‐specific cutting guides may be conducive to reducing operation time. Robot‐assisted surgery facilitates precise path planning and osteotomy plane identification. With the combination of an orthopedic robot and intraoperative ultrasound or the da Vinci robot, it can enhance the precision and safety of bone tumor surgery. Furthermore, it can be integrated with patient‐specific cutting guides to minimize surgical duration.

Background Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy. Methods In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib’s efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments. Results We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line. Conclusion The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.