Structure-based discovery of novel piperidine-biphenyl-DAPY derivatives as non-nucleoside reverse transcriptase inhibitors featuring improved potency, safety, and selectivity: From piperazine-biphenyl-DAPYs to piperidine-biphenyl-DAPYs.

Starting from our previously reported nonnucleoside reverse transcriptase inhibitor (NNRTI, 3), continuous efforts were made to enhance its potency and safety through a structure-based drug design strategy. This led to the discovery of a series of novel piperidine-biphenyl-diarylpyrimidines (DAPYs). Compound 10p, the most active compound in this series, exhibited an EC50 value of 6 nM against wide-type HIV-1 strain, which was approximately 560-fold more potent than the initial compound 3 (EC50 = 3.36 µM). Furthermore, significant improvements were observed in cytotoxicity and selectivity (CC50 > 202.17 µM, SI > 33144) compared to compound 3 (CC50 = 14.84 µM, SI = 4). Additionally, compound 10p demonstrated increased inhibitory activity against clinically mutant virus strains (EC50 = 7-63 nM). Further toxicity evaluation revealed that compound 10p exhibited minimal CYP enzyme and hERG inhibition. Importantly, single-dose acute toxicity testing did not result in any fatalities or noticeable pathological damage in mice. Therefore, compound 10p can be regarded as a lead candidate for guiding further development of biphenyl-diarylpyrimidine NNRTIs with favorable druggability for HIV therapy.

AdNAC20 Regulates Lignin and Coumarin Biosynthesis in the Roots of Angelica dahurica var. Formosana.

Angelica dahurica var. formosana (ADF), which belongs to the Umbelliferae family, is one of the original plants of herbal raw material Angelicae Dahuricae Radix. ADF roots represent an enormous biomass resource convertible for disease treatment and bioproducts. But, early bolting of ADF resulted in lignification and a decrease in the coumarin content in the root, and roots lignification restricts its coumarin for commercial utility. Although there have been attempts to regulate the synthesis ratio of lignin and coumarin through biotechnology to increase the coumarin content in ADF and further enhance its commercial value, optimizing the biosynthesis of lignin and coumarin remains challenging. Based on gene expression analysis and phylogenetic tree profiling, AdNAC20 as the target for genetic engineering of lignin and coumarin biosynthesis in ADF was selected in this study. Early-bolting ADF had significantly greater degrees of root lignification and lower coumarin contents than that of the normal plants. In this study, overexpression of AdNAC20 gene plants were created using transgenic technology, while independent homozygous transgenic lines with precise site mutation of AdNAC20 were created using CRISPR/Cas9 technology. The overexpressing transgenic ADF plants showed a 9.28% decrease in total coumarin content and a significant 12.28% increase in lignin content, while knockout mutant plants showed a 16.3% increase in total coumarin content and a 33.48% decrease in lignin content. Furthermore, 29,671 differentially expressed genes (DEGs) were obtained by comparative transcriptomics of OE-NAC20, KO-NAC20, and WT of ADF. A schematic diagram of the gene network interacting with AdNAC20 during the early-bolting process of ADF was constructed by DEG analysis. AdNAC20 was predicted to directly regulate the transcription of several genes with SNBE-like motifs in their promoter, such as MYB46, C3H, and CCoAOMT. In this study, AdNAC20 was shown to play a dual pathway function that positively enhanced lignin formation but negatively controlled coumarin formation. And the heterologous expression of the AdNAC20 gene at Arabidopsis thaliana proved that the AdNAC20 gene also plays an important role in the process of bolting and flowering.

Enhanced Ocular Delivery of Beva via Ultra-Small Polymeric Micelles for Noninvasive Anti-VEGF Therapy.

Pathological ocular neovascularization resulting from retinal ischemia constitutes a major cause of vision loss. Current anti-VEGF therapies rely on burdensome intravitreal injections of Bevacizumab (Beva). Herein ultrasmall polymeric micelles encapsulating Beva (P@Beva) are developed for noninvasive topical delivery to posterior eye tissues. Beva is efficiently loaded into 11 nm micelles fabricated via self-assembly of hyperbranched amphiphilic copolymers. The neutral, brush-like micelles demonstrate excellent drug encapsulation and colloidal stability. In vitro, P@Beva enhances intracellular delivery of Beva in ocular cells versus free drug. Ex vivo corneal and conjunctival-sclera-choroidal tissues transport after eye drops are improved 23-fold and 7.9-fold, respectively. Anti-angiogenic bioactivity is retained with P@Beva eliciting greater inhibition of endothelial tube formation and choroid sprouting over Beva alone. Remarkably, in an oxygen-induced retinopathy (OIR) model, topical P@Beva matching efficacy of intravitreal Beva injection, is the clinical standard. Comprehensive biocompatibility verifies safety. Overall, this pioneering protein delivery platform holds promise to shift paradigms from invasive intravitreal injections toward simplified, noninvasive administration of biotherapeutics targeting posterior eye diseases.

Engineering Matrix-Free Drug Protein Nanoparticles with Promising Penetration through Biobarriers for Treating Corneal Neovascularization.

Protein drugs have been widely used in treating various clinical diseases because of their high specificity, fewer side effects, and favorable therapeutic effect, but they greatly suffer from their weak permeability through tissue barriers, high sensitivity to microenvironments, degradation by proteases, and rapid clearance by the immune system. Herein, we disrupted the standard protocol where protein drugs must be delivered as the cargo via a delivery system and innovatively developed a free entrapping matrix strategy by simply mixing bevacizumab (Beva) with zinc ions to generate Beva-NPs (Beva-Zn2+), where Beva is coordinatively cross-linked by zinc ions with a loading efficiency as high as 99.2% ± 0.41%. This strategy was universal to generating various protein NPs, with different metal ions (Cu2+, Fe3+, Mg2+, Sr2+). The synthetic conditions of Beva-NPs were optimized, and the generated mechanism was investigated in detail. The entrapment, releasing profile, and the bioactivities of released Beva were thoroughly studied. By using in situ doping of the fourth-generation polyamindoamine dendrimer (G4), the Beva-G4-NPs exhibited extended ocular retention and penetration through biobarriers in the anterior segment through transcellular and paracellular pathways, effectively inhibiting corneal neovascularization (CNV) from 91.6 ± 2.03% to 13.5 ± 1.87% in a rat model of CNV. This study contributes to engineering of protein NPs by using a facile strategy for overcoming the weaknesses of protein drugs and protein NPs, such as weak tissue barrier permeability, low encapsulation efficiency, poor loading capacity, and susceptibility to inactivation.

Preoperative glucose-to-lymphocyte ratio predicts survival in cancer.

Background: Systemic inflammation and glucose metabolism have been closely related to the survival of cancer patients. Therefore, we aimed to evaluate whether preoperative glucose-to-lymphocyte ratio (GLR) can be used to predict the survival of cancer patients. Methods: We retrospectively examined 2172 cancer patients who underwent surgery from January 1, 2014, to December 31, 2016. There were 240 patients with non-small cell lung cancer (NSCLC), 378 patients with colorectal cancer (CRC), 221 patients with breast cancer (BC), 335 patients with gastric cancer (GC), 270 patients with liver cancer, 233 patients with esophageal cancer (EC), 295 patients with renal cancer, and 200 patients with melanoma. The formula for preoperative GLR calculation was as follows: GLR=glucose/lymphocyte count. The overall survival (OS) was estimated using the Kaplan-Meier method. The predictive factors for OS were determined using multivariate analysis. Results: The Kaplan-Meier analysis showed that the median survival time in the high-GLR group was much shorter than that of those in the low-GLR group for different cancers. Cox multivariate regression analysis reveals that preoperative GLR was an independent factor for predicting overall survival in different tumor types. Conclusion: Elevated preoperative GLR was remarkably associated with a poorer prognosis in patients with NSCLC, CRC, breast cancer, gastric cancer, kidney cancer, liver cancer, esophageal cancer, and melanoma. Preoperative GLR promises to be an essential predictor of survival for cancer patients.

Remodeling the Electronic Structure of Metallic Nickel and Ruthenium via Alloying in a Molecular Template for Sustainable Hydrogen Evolution.

The reasonably constructed high-performance electrocatalyst is crucial to achieve sustainable electrocatalytic water splitting. Alloying is a prospective approach to effectively boost the activity of metal electrocatalysts. However, it is a difficult subject for the controllable synthesis of small alloying nanostructures with high dispersion and robustness, preventing further application of alloy catalysts. Herein, we propose a well-defined molecular template to fabricate a highly dispersed NiRu alloy with ultrasmall size. The catalyst presents superior alkaline hydrogen evolution reaction (HER) performance featuring an overpotential as low as 20.6 ± 0.9 mV at 10 mA·cm-2. Particularly, it can work steadily for long periods of time at industrial-grade current densities of 0.5 and 1.0 A·cm-2 merely demanding low overpotentials of 65.7 ± 2.1 and 127.3 ± 4.3 mV, respectively. Spectral experiments and theoretical calculations revealed that alloying can change the d-band center of both Ni and Ru by remodeling the electron distribution and then optimizing the adsorption of intermediates to decrease the water dissociation energy barrier. Our research not only demonstrates the tremendous potential of molecular templates in architecting highly active ultrafine nanoalloy but also deepens the understanding of water electrolysis mechanism on alloy catalysts.

Long-term warming increased carbon sequestration capacity in a humid subtropical forest.

Tropical and subtropical forests play a crucial role in global carbon (C) pools, and their responses to warming can significantly impact C-climate feedback and predictions of future global warming. Despite earth system models projecting reductions in land C storage with warming, the magnitude of this response varies greatly between models, particularly in tropical and subtropical regions. Here, we conducted a field ecosystem-level warming experiment in a subtropical forest in southern China, by translocating mesocosms (ecosystem composed of soils and plants) across 600 m elevation gradients with temperature gradients of 2.1°C (moderate warming), to explore the response of ecosystem C dynamics of the subtropical forest to continuous 6-year warming. Compared with the control, the ecosystem C stock decreased by 3.8% under the first year of 2.1°C warming; but increased by 13.4% by the sixth year of 2.1°C warming. The increased ecosystem C stock by the sixth year of warming was mainly attributed to a combination of sustained increased plant C stock due to the maintenance of a high plant growth rate and unchanged soil C stock. The unchanged soil C stock was driven by compensating and offsetting thermal adaptation of soil microorganisms (unresponsive soil respiration and enzyme activity, and more stable microbial community), increased plant C input, and inhibitory C loss (decreased C leaching and inhibited temperature sensitivity of soil respiration) from soil drying. These results suggest that the humid subtropical forest C pool would not necessarily diminish consistently under future long-term warming. We highlight that differential and asynchronous responses of plant and soil C processes over relatively long-term periods should be considered when predicting the effects of climate warming on ecosystem C dynamics of subtropical forests.

Stem Cell-Based Hair Cell Regeneration and Therapy in the Inner Ear.

Hearing loss has become increasingly prevalent and causes considerable disability, thus gravely burdening the global economy. Irreversible loss of hair cells is a main cause of sensorineural hearing loss, and currently, the only relatively effective clinical treatments are limited to digital hearing equipment like cochlear implants and hearing aids, but these are of limited benefit in patients. It is therefore urgent to understand the mechanisms of damage repair in order to develop new neuroprotective strategies. At present, how to promote the regeneration of functional hair cells is a key scientific question in the field of hearing research. Multiple signaling pathways and transcriptional factors trigger the activation of hair cell progenitors and ensure the maturation of newborn hair cells, and in this article, we first review the principal mechanisms underlying hair cell reproduction. We then further discuss therapeutic strategies involving the co-regulation of multiple signaling pathways in order to induce effective functional hair cell regeneration after degeneration, and we summarize current achievements in hair cell regeneration. Lastly, we discuss potential future approaches, such as small molecule drugs and gene therapy, which might be applied for regenerating functional hair cells in the clinic.

Subcutaneous fat predicts bone metastasis in breast cancer: A novel multimodality-based deep learning model.

OBJECTIVES: This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. METHODS: CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. RESULTS: The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). CONCLUSION: Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

Albumin-bilirubin score predicts trastuzumab resistance in HER2-positive breast cancer.

BACKGROUND: The albumin-bilirubin (ALBI) score is a novel indicator of liver function. Some studies showed that the ALBI score was a predictive marker for the prognosis and efficacy of drug therapy in malignancies. We aimed to assess the predicted role of ALBI score in the sensitivity to therapy with trastuzumab in patients with human epidermal growth factor receptor 2 (HER2) positive breast cancer (BC). The clinical data of 226 HER2-positive BC patients at the Harbin Medical University Cancer Hospital from January 2017 and December 2018 were retrospectively collected. The ALBI score was calculated with serum albumin and bilirubin before diagnosis. The associations between ALBI score and trastuzumab resistance were analyzed by logistic regression analyses. The patients with trastuzumab resistance had higher ALBI scores compared with the patients without trastuzumab resistance. Moreover, there were weak correlations between the ALBI score and lymph node status (P= 0.093). In addition, multivariate analysis revealed that the ALBI score was an independent prognostic factor for trastuzumab resistance in HER2-positive BC. High ALBI score is associated with trastuzumab resistance in HER2-positive BC. Future studies are needed.

Autoimmune GFAP astrocytopathy after viral encephalitis: a case report of bimodal overlapping encephalitis.

Autoimmune glial fibrillary acidic protein (GFAP) astrocytopathy is a treatable autoimmune disorder affecting the central nervous system. Despite extensive research, the exact etiology and pathogenesis of this condition remain unclear. In recent years, autoimmune encephalitis (AE) after viral encephalitis (VE) has gathered significant attention. Here, we present a case report of autoimmune GFAP astrocytopathy after VE in a 43-year-old Asian male with a history of oral and labial herpes. The patient presented with high-grade fever, headache, urinary retention, unresponsiveness, and apathy. Elevated levels of protein and GFAP-IgG were observed in the cerebrospinal fluid (CSF), and enhanced brain magnetic resonance imaging (MRI) revealed linear enhancement oriented radially to the ventricles. Treatment with intravenous immunoglobulin (IVIG) resulted in symptom relief, reduced lesion enhancement, and decreased protein levels. This case report highlights bimodal encephalitis with no discernible interval between VE and autoimmune GFAP astrocytopathy, which poses diagnostic challenges. Notably, autoimmune GFAP astrocytopathy is a novel form of autoimmune encephalitis, and its treatment lacks sufficient clinical experience. Intriguingly, our patient demonstrated sensitivity to IVIG, a treatment that differed from past reports. Therefore, further exploration of treatment strategies for this condition is warranted.

Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community.

PURPOSE: ​Develop and validate a nomogram prediction model for hypertension-diabetes comorbidities based on chronic disease management in the community. PATIENTS AND METHODS: The nomogram prediction model was developed in a cohort of 7200 hypertensive patients at a community health service center in Hongshan District, Wuhan City. The data were collected from January 2022 to December 2022 and randomly divided into modeling and validation groups at a 7:3 ratio. The Lasso regression model was used for data dimensionality reduction, feature selection, and clinical test feature construction. Multivariate logistic regression analysis was used to build the prediction model. RESULTS: The application of the nomogram in the verification group showed good discrimination, with an AUC of 0.9205 (95% CI: 0.8471-0.9527) and a good calibration effect. Decision curve analysis demonstrated that the predictive model was clinically useful. CONCLUSION: This study presents a nomogram prediction model that incorporates age, waist-height ratio and elevated density lipoprotein cholesterol (HDL-CHOLESTEROL), which can be used to predict the risk of codeveloping diabetes in hypertensive patients.

Lower subcutaneous fat index predicts bone metastasis in breast cancer.

BACKGROUND: Bone metastases affect 50% to 70% of breast cancer (BC) patients and have a high mortality rate. Adipose tissue loss plays a pivotal role in the progression of cancer. OBJECTIVE: This study aims to evaluate the prognostic value of adipose tissue for bone metastasis in BC patients. METHODS: 517 BC patients were studied retrospectively. Patients' characteristics before the surgery were collected. Quantitative measurements of the subcutaneous fat index (SFI) were performed at the level of the eleventh thoracic vertebra. In order to adjust for the heterogeneity between the low SFI and high SFI groups, propensity score matching (PSM) was used. The Kaplan-Meier method was used to estimate the 5-year bone metastatic incidence. The prognostic analysis was performed with the Cox regression models. RESULTS: Compared with the patients without bone metastasis, the patients with bone metastasis had reduced SFI levels. In addition, Kaplan-Meier analysis revealed that patients with low SFI were more likely to develop bone metastases. The independent predictive value of SFI for bone metastases was confirmed by Cox regression analysis. The survival analysis was repeated after PSM with a 1:1 ratio, yielding similar results (P< 0.05). CONCLUSIONS: SFI is an independent predictor of bone metastasis in BC patients.

Causal associations between prodromal infection and neuromyelitis optica spectrum disorder: A Mendelian randomization study.

BACKGROUND AND PURPOSE: Prodromal infections are associated with neuromyelitis optica spectrum disorder (NMOSD), but it remains unclear which type of infection has a causal association with NMOSD. We aimed to explore the causal associations between four herpesvirus infections (chickenpox, cold sores, mononucleosis and shingles) and NMOSD, as well as between other types of infections and NMOSD. METHODS: For data on infections, we used the genome-wide association study (GWAS) summary statistics from the 23andMe cohort. For outcomes, we used the GWAS data of participants of European ancestry, including 215 NMOSD patients (132 anti-aquaporin-4 antibody [AQP4-ab]-positive patients and 83 AQP4-ab-negative patients) and 1244 normal controls. Single-nucleotide polymorphism (SNP) identification and two-sample Mendelian randomization (MR) analyses were then performed. RESULTS: In the 23andMe cohort, we identified one SNP for chickenpox (rs9266089 in HLA-B gene), one SNP for cold scores (rs885950 in the POU5F1 gene), one SNP for mononucleosis (rs2596465 in the HCP5 gene), and three SNPs for shingles (rs2523591 in the HLA-B gene; rs7047299 in the IFNA21 gene; rs9260809 in the MICD gene). The association between cold sores and AQP4-ab-positive NMOSD reached statistical significance (odds ratio [OR] 745.318; 95% confidence interval [CI] 22.176, 25,049.53 [p < 0.001, Q < 0.001]). The association between shingles and AQP4-ab-positive NMOSD was also statistically significant (OR 21.073; 95% CI 4.271, 103.974 [p < 0.001, Q < 0.001]). No significant association was observed between other infections and AQP4-ab-positive or AQP4-ab-negative NMOSD. CONCLUSION: These findings suggest there are positive associations between cold sores and shingles and AQP4-ab-positive NMOSD, indicating there may be causal links between herpes simplex virus and varicella-zoster virus infection and AQP4-ab-positive NMOSD.

Acidity of Soil and Water Decreases in Acid-Sensitive Forests of Tropical China.

Acid deposition in China has been declining since the 2000s. While this may help mitigate acidification in forest soils and water, little is known about the recovery of soils and water from previous severe acidification in tropical China. Here, we assessed the chemistry of mineral soils, water, and acid gases (SO2 and NOx) from three successional forest types in tropical China from 2000 to 2022. Our results showed that soil pH increased synchronously from 3.9 (2000-2015) to 4.2 (2016-2022) across all three forest types, with exchangeable acid initially decreasing and thereafter stabilizing. Surface and ground water pH also gradually increased throughout the monitoring period. Soil pH recovery was stronger in the primary than in the planted forest. However, soil pH recovery lagged behind the increase in rainfall pH by approximately a decade. The recovery of soil pH was likely related to the positive effects of the dissolution of Al/Fe-hydroxysulfate mineral and subsequent sulfur desorption on soil acid-neutralizing capacity, increased soil organic matter, and climate warming, but was likely moderated by increased exchangeable aluminum and potentially proton-producing hydroxysulfate mineral dissolution that caused the lagged soil pH recovery. Surface and ground water pH recovery was attributed to increased water acid-neutralizing capacity. Our study reports the potential for the recovery of acidified soil and water following decreased acid deposition and provides new insights into the functional recovery of acid-sensitive forests.

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data.

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged-peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

BACKGROUND: Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Currently, there are few quantitative assessments carried out between muscle biomarkers and distant metastasis using existing methods. PURPOSE: To assess the predictive value of the pectoralis muscle for BC distant metastasis, we developed a deep learning radiomics nomogram model (DLR-N) in this study. MATERIAL AND METHODS: A total of 493 patients with pathologically confirmed BC were registered. Image features were extracted from computed tomography (CT) images for each patient. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for distant metastasis. The DLR-N was built based on independent prognostic factors and CT images to predict distant metastases. The model was assessed in terms of overall performance, discrimination, calibration, and clinical value. Finally, the predictive performance of the model was validated using the testing cohort. RESULTS: The developed DLR-N combined multiple radiomic features and clinicopathological factors and demonstrated excellent predictive performance. The C-index of the training cohort was 0.983 (95% confidence interval [CI] = 0.969-0.998) and the C-index of the testing cohort was 0.948 (95% CI = 0.917-0.979). Decision curve analysis (DCA) showed that patients could benefit more from incorporating multimodal radiomic features into clinicopathological models. CONCLUSIONS: DLR-N verified that there were biomarkers at the level of the fourth thoracic vertebra (T4) that affected distant metastasis. Multimodal prediction models based on deep learning could be a potential method to aid in the prediction of distant metastases in patients with BC.

Clinical feature and disease outcome in patients with myelin oligodendrocyte glycoprotein antibody-associated disorder: a Chinese study.

BACKGROUND: To identify factors associated with relapse risk and disability in myelin oligodendrocyte glycoprotein antibody-associated disorder (MOGAD). METHOD: Between 2016 and 2021, 186 patients with MOGAD were included in the study. Factors associated with a relapsing course, annualised relapse rate (ARR), recurrent relapses under different maintenance treatments and unfavourable disability outcome were analysed. RESULTS: MOGAD affects women (53.8%) slightly more often than men. After a median disease duration of 51.0 months, 60.2% (112/186) relapsed, with an overall ARR of 0.5. The ARR (0.6 vs 0.4, p=0.049), median Expanded Disability Status Scale (EDSS) score (1 (range 0-9.5) vs 1 (range 0-3.5), p=0.005) and Visual Functional System Score (VFSS) (0 (range 0-6) vs 0 (range 0-3), p=0.023) at last visit were higher in adults than in children, and time to first relapse was shorter in adults than in children (4.1 (range 1.0-111.0) vs 12.2 (range 1.3-266.8) months, p=0.001). Myelin oligodendrocyte glycoprotein antibody (MOG-ab) persistence over 1 year was associated with a relapsing course (OR 7.41, 95% CI 2.46 to 22.33, p=0.000), while timely maintenance therapy was associated with a lower ARR (p=0.008). More than four attacks (OR 4.86, 95% CI 1.65 to 14.28, p=0.004) and poor recovery from the first attack (OR 75.28, 95% CI 14.45 to 392.05, p=0.000) were associated with an unfavourable outcome (EDSS score ≥2 including VFSS ≥2). CONCLUSIONS: The results underscored the importance of timely maintenance treatment to prevent further relapses, especially in adult patients with persistently positive MOG-ab and unsatisfactory recovery from the onset attack.

scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

Contrasting geochemical and fungal controls on decomposition of lignin and soil carbon at continental scale.

Lignin is an abundant and complex plant polymer that may limit litter decomposition, yet lignin is sometimes a minor constituent of soil organic carbon (SOC). Accounting for diversity in soil characteristics might reconcile this apparent contradiction. Tracking decomposition of a lignin/litter mixture and SOC across different North American mineral soils using lab and field incubations, here we show that cumulative lignin decomposition varies 18-fold among soils and is strongly correlated with bulk litter decomposition, but not SOC decomposition. Climate legacy predicts decomposition in the lab, and impacts of nitrogen availability are minor compared with geochemical and microbial properties. Lignin decomposition increases with some metals and fungal taxa, whereas SOC decomposition decreases with metals and is weakly related with fungi. Decoupling of lignin and SOC decomposition and their contrasting biogeochemical drivers indicate that lignin is not necessarily a bottleneck for SOC decomposition and can explain variable contributions of lignin to SOC among ecosystems.