Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data.

MOTIVATION: Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. RESULTS: We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. AVAILABILITY: An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. CONTACT: dengmh@pku.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Journal Name online.

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma.

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.

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening.

BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy. METHODS: This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent). RESULTS: The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other. CONCLUSION: Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future.

dz2 Band Links Frontier Orbitals and Charge Carrier Dynamics of Single-Atom Cocatalyst-Aided Photocatalytic H2 Production.

The Cu single-atom catalyst (SAC) supported on TiO2 exhibits outstanding efficacy in photocatalytic hydrogen evolution. The precise operational mechanism remains a subject of ongoing debate. The focus resides with the interplay linking heightened catalytic activity, dynamic valence state alterations of Cu atoms, and their hybridization with H2O orbitals, manifested in catalyst color changes. Taking anatase TiO2 (101) as a prototypical surface, we perform ab initio quantum dynamics simulation to reveal that the high activity of the Cu-SAC is due to the quasi-planar coordination structure of the Cu atom after H2O adsorption, allowing it to trap photoexcited hot electrons and inject them into the hybridized orbital between Cu and H2O. The observed alterations in the valence state and the coloration can be attributed to the H atom released during H2O dissociation and adsorbed onto the lattice O atom neighboring the Cu-SAC. Notably, this adsorption of H atoms puts the Cu-SAC into an inert state, as opposed to an activating effect reported previously. Our work clarifies the relationship between the high photocatalytic activity and the local dynamic atomic coordination structure, providing atomistic insights into the structural changes occurring during photocatalytic reactions on SACs.

Cu catalyzed [4 + 2] cycloaddition for the synthesis of highly substituted 3-fluoropyridines.

A copper catalyzed annulation-aromatization of benzyl trifluoromethyl ketimines with 3-acryloyloxazolidin-2-ones for the synthesis of 3-fluoropyridines through double C-F bond cleavages has been developed. In this approach, the annulation occurred between the in situ formed dienes from trifluoromethyl ketimines via the first C-F bond cleavage and 3-acryloyloxazolidin-2-ones. Then the aromatization afforded 3-fluoropyridines in moderate yields through the second C-F bond cleavage. The 3-fluoropyridine products could be further hydrolyzed to multi-substituted 3-pyridinecarboxylic acids.

A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy.

BACKGROUND: Diagnosis of early gastric cancer (EGC) under narrow band imaging endoscopy (NBI) is dependent on expertise and skills. We aimed to elucidate whether artificial intelligence (AI) could diagnose EGC under NBI and evaluate the diagnostic assistance of the AI system. METHODS: In this retrospective diagnostic study, 21,785 NBI images and 20 videos from five centers were divided into a training dataset (13,151 images, 810 patients), an internal validation dataset (7057 images, 283 patients), four external validation datasets (1577 images, 147 patients), and a video validation dataset (20 videos, 20 patients). All the images were labeled manually and used to train an AI system using You look only once v3 (YOLOv3). Next, the diagnostic performance of the AI system and endoscopists were compared and the diagnostic assistance of the AI system was assessed. The accuracy, sensitivity, specificity, and AUC were primary outcomes. RESULTS: The AI system diagnosed EGCs on validation datasets with AUCs of 0.888-0.951 and diagnosed all the EGCs (100.0%) in video dataset. The AI system achieved better diagnostic performance (accuracy, 93.2%, 95% CI, 90.0-94.9%) than senior (85.9%, 95% CI, 84.2-87.4%) and junior (79.5%, 95% CI, 77.8-81.0%) endoscopists. The AI system significantly enhanced the performance of endoscopists in senior (89.4%, 95% CI, 87.9-90.7%) and junior (84.9%, 95% CI, 83.4-86.3%) endoscopists. CONCLUSION: The NBI AI system outperformed the endoscopists and exerted potential assistant impact in EGC identification. Prospective validations are needed to evaluate the clinical reinforce of the system in real clinical practice.

A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy.

BACKGROUND : Accurate identification of the differentiation status and margins for early gastric cancer (EGC) is critical for determining the surgical strategy and achieving curative resection in EGC patients. The aim of this study was to develop a real-time system to accurately identify differentiation status and delineate the margins of EGC on magnifying narrow-band imaging (ME-NBI) endoscopy. METHODS : 2217 images from 145 EGC patients and 1870 images from 139 EGC patients were retrospectively collected to train and test the first convolutional neural network (CNN1) to identify EGC differentiation status. The performance of CNN1 was then compared with that of experts using 882 images from 58 EGC patients. Finally, 928 images from 132 EGC patients and 742 images from 87 EGC patients were used to train and test CNN2 to delineate the EGC margins. RESULTS : The system correctly predicted the differentiation status of EGCs with an accuracy of 83.3 % (95 % confidence interval [CI] 81.5 % - 84.9 %) in the testing dataset. In the man - machine contest, CNN1 performed significantly better than the five experts (86.2 %, 95 %CI 75.1 % - 92.8 % vs. 69.7 %, 95 %CI 64.1 % - 74.7 %). For delineating EGC margins, the system achieved an accuracy of 82.7 % (95 %CI 78.6 % - 86.1 %) in differentiated EGC and 88.1 % (95 %CI 84.2 % - 91.1 %) in undifferentiated EGC under an overlap ratio of 0.80. In unprocessed EGC videos, the system achieved real-time diagnosis of EGC differentiation status and EGC margin delineation in ME-NBI endoscopy. CONCLUSION : We developed a deep learning-based system to accurately identify differentiation status and delineate the margins of EGC in ME-NBI endoscopy. This system achieved superior performance when compared with experts and was successfully tested in real EGC videos.

Serum CEA and CA19-9 Levels are Associated with the Presence and Severity of Colorectal Neoplasia.

BACKGROUND: High concentrations of carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) were found in the serum of patients with colon cancer. We performed the present work to investigate the association between elevated levels of serum biomarkers (CEA and CA19-9) and shortened survival in patients with colon cancer. METHODS: We examined patients who underwent colonoscopies between 2001 and 2014 and measured and analyzed the serum CEA and CA19-9 levels of 362 participants. RESULTS: Elevated CEA concentrations were found to be associated with advanced invasion, lymph node metastasis, and short survival. Elevated CA19-9 concentrations also were associated with lymph node metastasis and short survival. CONCLUSIONS: Elevated serum CEA or CA19-9 levels were found to be associated with shortened survival.

Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference.

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.

Asymmetric synthesis of isoquinolinonaphthyridines catalyzed by a chiral Brønsted acid.

A catalytic asymmetric method for the synthesis of chiral isoquinolinonaphthyridines has been developed. A chiral disulfonimide catalyzes a redox cyclization reaction between 2-methyl-3-aldehydeazaarenes and 1,2,3,4-tetrahydroisoquinolines to deliver a range of isoquinolinonaphthyridines with good to high yields (up to 91%) and up to 92 : 8 er.

Alkoxythiophene-Directed Fibrillization of Polymer Donor for Efficient Organic Solar Cells.

Realizing fibrillar molecular framework is highly encouraged in organic solar cells (OSCs) due to the merit of efficient charge carrier transport. This is however mainly achieved via the chemical structural design of photovoltaic semiconductors. In this work, through the utilization of three alkoxythiophene additives, T-2OMe, T-OEH, and T-2OEH, the intermolecular interactions among a series of BDT-type polymer donors, i.e., PM6, D18, PBDB-T, and PTB7-Th, are tuned to self-assemble into nanofibrils during solution casting. X-ray technique and molecular dynamics simulation reveal that the alkoxythiophene with (2-ethylhexyl)oxy (─OEH) chains can attach on the 2-ethylhexyl (EH) chains of these polymer donors and promote their self-assembly into 1D nanofibrils, in their neat films as well as photovoltaic blends with L8-BO. By adapting these fibrillar polymer donors to construct pseudo-bulk heterojunction (P-BHJ) OSCs via layer-by-layer deposition, generally improved device performance is seen, with power conversion efficiencies enhanced from 18.2% to 19.2% (certified 18.96%) and from 17.9% to 18.7% for the PM6/L8-BO and D18/L8-BO devices, respectively. This work provides a physical approach to promote the fibrillar charge transport channels for efficient photovoltaics.

Integration of Co Single Atoms and Ni Clusters on Defect-Rich ZrO2 for Strong Photothermal Coupling Boosts Photocatalytic CO2 Reduction.

We report a solvothermal method for the synthesis of an oxygen vacancy-enriched ZrO2 photocatalyst with Co single atoms and Ni clusters immobilized on the surface. This catalyst presents superior performance for the reduction of CO2 in H2O vapor, with a CO yield reaching 663.84 µmol g-1 h-1 and a selectivity of 99.52%. The total solar-to-chemical energy conversion efficiency is up to 0.372‰, which is among the highest reported values. The success, on one hand, depends on the Co single atoms and Ni clusters for both extended spectrum absorption and serving as dual-active centers for CO2 reduction and H2O dissociation, respectively; on the other hand, this is attributed to the enhanced photoelectric and thermal effect induced by concentrated solar irradiation. We demonstrate that an intermediate impurity state is formed by the hybridization of the d-orbital of single-atom Co with the molecular orbital of H2O, enabling visible-light-driven excitation over the catalyst. In addition, Ni clusters play a crucial role in altering the adsorption configuration of CO2, with the localized surface plasmon resonance effect enhancing the activation and dissociation of CO2 induced by visible-near-infrared light. This study provides valuable insights into the synergistic effect of the dual cocatalyst toward both efficient photothermal coupling and surface redox reactions for solar CO2 reduction.

Brominated Quaternary Ammonium Salt-Assisted Hybrid Electron Transport Layer for High-Performance Conventional Organic Solar Cells.

Interlayer engineering is crucial for achieving efficient and stable organic solar cells (OSCs). Herein, by introducing a commercialized brominated quaternary ammonium salt, hexamethonium bromide (HB), into a perylene diimide (PDI)-structured electron transport layer (ETL), a PDINN:HB hybrid ETL with enhanced charge collection ability and environmental/operational stability is realized. Molecular dynamics simulations and Kelvin probe force microscopy indicate that strong polar bromine and amine groups can form extra interfacial dipoles in the hybrid interlayer, while X-ray photoelectron spectroscopy and electron paramagnetic resonance suggest the hybrid ETL can interact with the Ag cathode, thereby regulating the energy level arrangement at the interface. As for the results, the PDINN:HB hybrid ETL enables improved power conversion efficiency (PCE) from 17.8 to 18.4% and 18.8 to 19.4% in PM6:C5-16 bulk heterojunction- and PM6/L8-BO pseudobulk heterojunction-based OSCs, respectively. The versatility of this method is further verified by introducing a range of brominated quaternary ammonium salts into PDINN, in which a superior PCE and stability are all obtained compared to the reference device.

Veliparib exerts protective effects in intracerebral hemorrhage mice by inhibiting the inflammatory response and accelerating hematoma resolution.

Poly (ADP-ribose) polymerase (PARP) inhibitors have potent anti-inflammatory effects, including the suppression of brain microglial activation. Veliparib, a well-known PARP1/2 inhibitor, exhibits particularly high brain penetration, but its effects on stroke outcome is unknown. Here, the effects of veliparib on the short-term outcome of intracerebral hemorrhage (ICH), the most lethal type of stroke, were investigated. Collagenase-induced mice ICH model was applied, and the T2-weighted magnetic resonance imaging was performed to evaluate lesion volume. Motor function and hematoma volume were also measured. We further performed immunofluorescence, enzyme linked immunosorbent assay, flow cytometry, and blood-brain barrier assessment to explore the potential mechanisms. Our results demonstrated veliparib reduced the ICH lesion volume dose-dependently and at a dosage of 5 mg/kg, veliparib significantly improved mouse motor function and promoted hematoma resolution at days 3 and 7 post-ICH. Veliparib inhibited glial activation and downregulated the production of pro-inflammatory cytokines. Veliparib significantly decreased microglia counts and inhibited peripheral immune cell infiltration into the brain on day 3 after ICH. Veliparib improved blood-brain barrier integrity at day 3 after ICH. These findings demonstrate that veliparib improves ICH outcome by inhibiting inflammatory responses and may represent a promising novel therapy for ICH.

Tumor-informed deep sequencing of ctDNA detects minimal residual disease and predicts relapse in osteosarcoma.

Background: Current surveillance modalities of osteosarcoma relapse exhibit limited sensitivity and specificity. Although circulating tumor DNA (ctDNA) has been established as a biomarker of minimal residual disease (MRD) in many solid tumors, a sensitive ctDNA detection technique has not been thoroughly explored for longitudinal MRD detection in osteosarcoma. Methods: From August 2019 to June 2023, 59 patients diagnosed with osteosarcoma at the First Affiliated Hospital of Sun Yat-sen University were evaluated in this study. Tumor-informed MRD panels were developed through whole exome sequencing (WES) of tumor tissues. Longitudinal blood samples were collected during treatment and subjected to multiplex PCR-based next-generation sequencing (NGS). Kaplan-Meier curves and Log-rank tests were used to compare outcomes, and Cox regression analysis was performed to identify prognostic factors. Findings: WES analysis of 83 patients revealed substantial mutational heterogeneity, with non-recurrent mutated genes accounting for 58.1%. Tumor-informed MRD panels were successfully obtained for 85.5% of patients (71/83). Among 59 patients with successful MRD panel customization and available blood samples, 13 patients exhibited positive ctDNA detection after surgery. Patients with negative post-operative ctDNA had better event-free survival (EFS) compared to those with positive ctDNA, at 1-6 months after surgery, after adjuvant chemotherapy, and more than 6 months after surgery (p < 0.05). In both univariate and multivariate Cox regression analysis, ctDNA results emerged as a significant predictor of EFS (p < 0.05). ctDNA detection preceded positive imaging in 5 patients, with an average lead time of 92.6 days. Thirty-nine patients remained disease-free, with ctDNA results consistently negative or turning negative during follow-up. Interpretation: Our study underscores the applicability of tumor-informed deep sequencing of ctDNA in osteosarcoma MRD surveillance and, to our knowledge, represents the largest cohort to date. ctDNA detection is a significant prognostic factor, enabling the early identification of tumor relapse and progression compared to standard imaging, thus offering valuable insights in guiding osteosarcoma patient management. Funding: The Grants of National Natural Science Foundation of China (No. 82072964, 82072965, 82203798, 82203026), the Natural Science Foundation of Guangdong (No. 2023A1515012659, 2023A1515010302), and the Regional Combination Project of Basic and Applied Basic Research Foundation of Guangdong (No. 2020A1515110010).

Concentrated solar CO2 reduction in H2O vapour with >1% energy conversion efficiency.

H2O dissociation plays a crucial role in solar-driven catalytic CO2 methanation, demanding high temperature even for solar-to-chemical conversion efficiencies <1% with modest product selectivity. Herein, we report an oxygen-vacancy (Vo) rich CeO2 catalyst with single-atom Ni anchored around its surface Vo sites by replacing Ce atoms to promote H2O dissociation and achieve effective photothermal CO2 reduction under concentrated light irradiation. The high photon flux reduces the apparent activation energy for CH4 production and prevents Vo 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 H2O activation. The catalyst presents a CH4 yield of 192.75 µmol/cm2/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 H2O-activating catalysts for CO2 reduction and thereby expedite the practical utilization of solar-to-chemical technologies.

Intestinal Barrier Function and Neurodegenerative Disease.

Neurodegenerative diseases are caused by the loss of neurons and/or their myelin sheaths, which deteriorate over time and become dysfunctional. Alzheimer's disease, Parkinson's disease, and multiple sclerosis are among the most prominent neurodegenerative diseases that affect millions of older adults worldwide. Despite extensive research over several decades, controversies still surround the etiology of neurodegenerative diseases, and many of them remain incurable. Meanwhile, an increasing number of new mechanistic studies related to the microbiota-gut-brain axis have emerged, among which the relationship between the function of the intestinal barrier and neurodegenerative diseases has received widespread attention. As one of the first lines of defense between the body and the external environment, the impaired function of the intestinal barrier is closely related to the development of neurodegenerative pathologies. Among them, the microbiota-gut-brain axis disorder characterized by intestinal barrier disruption mainly includes impaired function of the intestinal microbial barrier, chemical barrier, mechanical barrier, and immune barrier. This review focuses on the structure and molecular mechanisms of the various layers of the intestinal barrier as well as their relationship with neurodegenerative lesions. In recent years, intestinal barrier repair therapies have provided new ideas for the studied disease treatment modalities. We believe that a better understanding of the role of the intestinal barrier in neurodegenerative diseases would provide new insights for the development of viable therapeutic strategies for patients.

Effects of Intestinal M Cells on Intestinal Barrier and Neuropathological Properties in an AD Mouse Model.

Intestinal microfold cells (M cells) play a critical role in the immune response of the intestinal mucosa by actively taking up antigens, facilitating antigen presentation to immune cells, and promoting the production of secretory immunoglobulin A by B cells. Despite their known important functions in the gut, the effect of M cells on the central nervous system remains unclear. We investigated the expression of M cell-related factor genes and protein levels in Peyer's patches (PPs) of 3-month-old and 9-month-old APP/PS1 mice, as well as the expression of intestinal barrier proteins in the ileum and colon of these mice. Furthermore, we employed intestinal M cell conditional ablation mice (i.e., RankΔIEC mice) to assess the influence of M cells on the intestinal barrier and Alzheimer's disease (AD)-like behavioral and pathological features. Our findings revealed that compared to wild-type mice, APP/PS1 mice showed altered M cell-related genes and disrupted intestinal barriers. In addition, there is a significant decrease in glycoprotein 2 (GP2) mRNA levels in the PPs of 3-month-old APP/PS1 mice, with the relative expression of GP2 mRNA tending to zero. Parameters related to the intestinal barrier (IgA, MUC2, Claudin-5, ZO-1) were significantly downregulated in both 3-month-old and 9-month-old APP/PS1 mice compared to wild-type controls, and the differences were more pronounced in the 9-month-old mice. Moreover, M cell ablation in APP/PS1 mice (i.e., APP/PS1ΔMC mice) resulted in more severe intestinal barrier destruction. Notably, we observed through water maze experiments that APP/PS1ΔMC mice at 6 months of age exhibited significantly poorer spatial learning memory compared to APP/PS1 mice. And the neuropathological alterations were also observed in APP/PS1ΔMC mice at 6 months of age that when intestinal M cells are damaged in APP/PS1 mice, brain microglia are activated, Tau phosphorylation is exacerbated, and the number of neurons is reduced. Our results suggest for the first time that the absence of intestinal M cells might further aggravate intestinal leakage, lead to neuropathological damage, and subsequently cause the impairment of learning memory ability in AD mice. Our research highlights the impact of intestinal M cells on the intestinal barrier and AD neuropathogenesis in AD mouse model.

tRNA-Uridine Aminocarboxypropyltransferase DTW Domain Containing 2 Suppresses Colon Adenocarcinoma Progression.

Background: DTW Domain Containing 2 (DTWD2) is a newly identified transfer RNA-uridine aminocarboxypropyltransferase. Dysregulated expression of DTWD1 has been reported in several malignancies, nevertheless, the role of DTWD2 in cancers remains completely unknown. Here, we aimed to initially investigate the expression and role of DTWD2 in colon adenocarcinoma. Methods: We first evaluated the transcription and mRNA levels of DTWD2 using data from The Cancer Genome Atlas. Besides, we tested its mRNA and protein expression in our enrolled retrospective cohort. Univariate and multivariate analyses were conducted to assess its prognostic value. Cellular experiments and xenografts were also performed to validate the role of DTWD2 in colon cancer progression. Results: DTWD2 was downregulated in colon adenocarcinoma and associated with poor prognosis. Lymph node metastasis, distant metastasis, and advanced tumor stage are all characterized by lower DTWD2 levels. Furthermore, Cox regression analysis demonstrated that DTWD2 is a novel independent prognostic factor for colon cancer patients. Finally, cellular and xenograft data demonstrated that silencing DTWD2 significantly enhanced colon cancer growth. Conclusion: Low expression of DTWD2 may be a potential molecular marker for poor prognosis in colon cancer.

Surgical Strategy and Application of Robotic-Assisted Benign Sacral Neurogenic Tumor Resection.

BACKGROUND: Robotic surgery may be advantageous in neurogenic sacral tumor resection but only a few studies reported robotic-assisted neurogenic sacral tumor resection. OBJECTIVE: To propose a new surgical strategy for robotic-assisted benign sacral neurogenic tumor resection and introduce the ultrasonic osteotomy surgical system in robotic surgery. METHODS: Twelve patients who had robotic-assisted primary benign sacral neurogenic tumor resection between May 2015 and March 2021 were included. Our surgical strategy divides tumors into 4 types. Type I: Presacral tumors with diameter <10 cm. Type II: Narrow-base tumors involving the sacrum with diameter <10 cm. Type III: Broad-base tumors involving the sacrum with diameter <10 cm. Type IV: Tumors involving sacral nerve roots ≥2 levels and/or with diameter ≥10 cm. RESULTS: Five type I, 5 type II, and 1 type III patients underwent tumor resection via an anterior approach, and 1 type IV patient via a combined approach. The median operation time, blood loss, and postoperative hospital stay of type I and II were much less than those of type IV. The ultrasonic osteotomy surgical system facilitated osteotomy in 2 type II and 1 type III patients. Eleven patients had total resections, and 1 type III patient had a partial resection. During the follow-up period of 7.9 to 70.9 months (median: 28.5 months), no local recurrences or deaths were noted. CONCLUSION: With the largest single-center series to our knowledge, this surgical strategy helped to guide robotic-assisted benign sacral neurogenic tumor resection. The ultrasonic osteotomy surgical system was effective for type II and III.