Minimal residual disease profiling predicts pathological complete response in esophageal squamous cell carcinoma.

Accurate presurgical prediction of pathological complete response (pCR) can guide treatment decisions, potentially avoiding unnecessary surgeries and improving the quality of life for cancer patients. We developed a minimal residual disease (MRD) profiling approach with enhanced sensitivity and specificity for detecting minimal tumor DNA from cell-free DNA (cfDNA). The approach was validated in two independent esophageal squamous cell carcinoma (ESCC) cohorts. In a cohort undergoing neoadjuvant, surgical, and adjuvant therapy (NAT cohort), presurgical MRD status precisely predicted pCR. All MRD-negative cases (10/10) were confirmed as pCR by pathological evaluation on the resected tissues. In contrast, MRD-positive cases included all the 27 non-pCR cases and only one pCR case (10/10 vs 1/28, P < 0.0001, Fisher's exact test). In a definitive radiotherapy cohort (dRT cohort), post-dRT MRD status was closely correlated with patient prognosis. All MRD-negative patients (25/25) remained progression-free during the follow-up period, while 23 of the 26 MRD-positive patients experienced disease progression (25/25 vs 3/26, P < 0.0001, Fisher's exact test; progression-free survival, P < 0.0001, log-rank test). The MRD profiling approach effectively predicted the ESCC patients who would achieve pCR with surgery and those likely to remain progression-free without surgery. This suggests that the cancer cells in these MRD-negative patients have been effectively eliminated and they could be suitable candidates for a watch-and-wait strategy, potentially avoiding unnecessary surgery.

Analysis of distribution method of designed air quantity in coal mine ventilation-a case study.

In a coal mine, air leakage exists in some roadways through doors and other ventilation structures inevitably. Based on this opinion, there are different views on whether these roadways must be assigned airflow in coal mine ventilation design. This paper analyses some relevant regulations and criteria on the designed air quantity of coal mines. Then, based on the ventilation design of the Guizhou Yizhong Coal Mine, through the study of the calculation of needed air quantity of every working place and its distribution method in coal mine ventilation design, this paper puts forward that explosion-proof door, safety exit, and other short distance roadways with ventilation structures need not assign airflow in coal mine ventilation design, while some long-distance roadways need. Additionally, it presents the main reason to support this opinion, gives the distribution method of inner air leakage quantity, which comes with the calculation of the designed mine total air quantity, puts forward the remedy method for the air leakage through ventilation structures in a coal mine ventilation system, then offers the mine operator with the basic opinions for the day-to-day planning and effective operation of a coal mine ventilation system.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY: Datasets and source codes are available at https://github.com/Biohang/DiSMVC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automatic three-dimensional facial symmetry reference plane construction based on facial planar reflective symmetry net.

OBJECTIVES: Three-dimensional (3D) facial symmetry analysis is based on the 3D symmetry reference plane (SRP). Artificial intelligence is widely used in the dental and oral sciences. This study developed a novel deep learning model called the facial planar reflective symmetry net (FPRS-Net) to automatically construct an SRP and established a method for defining a 3D point-cloud region of interest (ROI) and high-dimensional feature computations suitable for this network model. METHODS: Overall, 240 patients were enrolled. The deep learning model was trained and predicted using 200 samples, and its clinical suitability was evaluated with 40 samples. Four FPRS-Net models were prepared, each using supervised and unsupervised learning approaches based on full facial and ROI data (FPRS-NetS, FPRS-NetSR, FPRS-NetU, and FPRS-NetUR). These models were trained on 160 3D facial datasets, validated on 20 cases, and tested on another 20 cases. The model predictions were evaluated using an additional 40 clinical 3D facial datasets by comparing the mean square error of the SRP between the parameters predicted by the four FPRS-Net models and the truth plane. The clinical suitability of FPRS-Net models was evaluated by measuring the angle error between the predicted and ground-truth planes; experts evaluated the predicted SRP of the four FPRS-Net models using the visual analogue scales (VAS) method. RESULTS: The FPRS-NetSR and FPRS-NetU models achieved an average angle error of 0.84° and 0.99° in predicting 3D facial SRP, respectively, with a VAS value of >8. Using the four FPRS-Net models to create an SRP in 40 cases of 3D facial data required <4 s. CONCLUSIONS: Our study demonstrated a new solution for automatically constructing oral clinical 3D facial SRPs. CLINICAL SIGNIFICANCE: This study proposes an innovative deep learning algorithm (FPRS-Net) to construct a symmetry reference plane that can reduce workload, shorten the time required for digital design, reduce dependence on expert experience, and improve therapeutic efficiency and effectiveness in dental clinics.

Engineering Heterostructured Fe-Co-P Arrays for Robust Sodium Storage.

Transition metal phosphides attract extensive concerns thanks to their high theoretical capacity in sodium ion batteries (SIBs). Nevertheless, the substantial volume fluctuation of metal phosphides during cycling leads to severe capacity decay, which largely hinders their large-scale deployment. In this regard, heterostructured Fe-Co-P (FeP/Co2P) arrays are firstly constructed in this work for SIBs. The novel self-supported construction without insulated binders favors fast charge migration and Na+ ion diffusion. In addition, the special heterostructure with abundant heterointerfaces could considerably mitigate the volume change during (de)sodiation and provide increased active sites for Na+ ions. Density functional theoretical (DFT) calculations confirm the built-in electric field in the heterointerfaces, which greatly hastens charge transfer and Na+ ion transportation, thereafter bringing about enhanced electrochemical performance. Most importantly, the FeP/Co2P heterostructure discloses higher electrical conductivity than that of bare FeP and Co2P based on the theoretical calculations. As anticipated, the heterostructured Fe-Co-P arrays demonstrate superior performance to that of Fe-P or Co-P anode, delivering high reversible capacities of 634 mAh g-1 at 0.2 A g-1 and 239 mAh g-1 at 1 A g-1 after 300 cycles.

Plant diversity increases spatial stability of aboveground productivity in alpine grasslands.

Plant diversity can significantly affect the grassland productivity and its stability. However, it remains unclear how plant diversity affects the spatial stability of natural grassland productivity, especially in alpine regions that are sensitive to climate change. We analyzed the interaction between plant (species richness and productivity, etc.) and climatic factors (precipitation, temperature, and moisture index, etc.) of alpine natural grassland on the Qinghai-Tibetan Plateau. In addition, we tested the relationship between plant diversity and spatial stability of grassland productivity. Results showed that an increase in plant diversity significantly enhanced community productivity and its standard deviation, while reducing the coefficient of variation in productivity. The influence of plant diversity on productivity and the reciprocal of productivity variability coefficient was not affected by vegetation types. The absolute values of the regression slopes between climate factors and productivity in alpine meadow communities with higher plant diversity were smaller than those in alpine meadow communities with lower plant diversity. In other words, alpine meadow communities with higher plant diversity exhibited a weaker response to climatic factors in terms of productivity, whereas those with lower plant diversity showed a stronger response. Our results indicate that high plant diversity buffers the impact of ambient pressure (e.g., precipitation, temperature) on alpine meadow productivity, and significantly enhanced the spatial stability of grassland productivity. This finding provides a theoretical basis for maintaining the stability of grassland ecosystems and scientifically managing alpine grasslands under the continuous climate change.

A brief version of the Attitudes to Ageing Questionnaire for older Chinese adults: development and psychometric evaluation.

BACKGROUND: Positive attitudes toward aging are considered essential for achieving psychological well-being in later life. However, there is currently a lack of a concise and comprehensive measurement tool specifically designed to assess attitudes toward aging among the elderly population in China. To address this gap, the present study aimed to develop a brief version of the Attitudes to Ageing Questionnaire tailored to older Chinese individuals and evaluate its psychometric properties. METHODS: Initially, a sample of community-dwelling older adults (Sample 1: n = 442, aged 60-88) was utilized to establish a new scale format. Subsequently, two convenience samples (Sample 2: n = 311, aged 60-90; Sample 3: n = 164, aged 60-89) were employed to evaluate the psychometric properties of this scale, including factor structure, internal consistency, test-retest reliability, convergent validity, and discriminant validity. RESULTS: We selected 12 items from the original questionnaire to create the brief scale. The brief scale maintained the three-factor structure of the full-format version, encompassing psychosocial loss, physical change, and psychological growth, and demonstrated adequate psychometric properties. CONCLUSIONS: This development process shortens the administration time of the questionnaire while avoiding excessive loss of information. The newly developed scale serves as a reliable and valid assessment tool for measuring attitudes toward aging among older Chinese individuals and is well-suited for implementation in large-scale surveys that utilize an extensive array of questionnaires. This tool can be applied to assessing the effectiveness of interventions aimed at eliminating ageism.

Role of Zhiqiao Chuanlian decoction in the treatment of food accumulation fever: Network pharmacology and animal experiments.

Objective: Food accumulation fever (FAF), a common clinical disease in children, is generally induced by the excessive intake of high-calorie or high-fat foods. Zhiqiao Chuanlian decoction (ZQCLD) is a classical traditional Chinese medicine (TCM) that may have therapeutic effects on FAF. Methods: Network pharmacological analyses of ZQCLD and FAF were conducted. Animal experiments lasted for 14 days. Rats in the model, positive control, and low-, medium-, and high-dose groups were fed a high-calorie diet. On days 11-14, the positive group was given a domperidone solution. The low-, medium-, and high-dose groups were administered different concentrations of ZQCLD. The body temperature, gastric emptying rate, and intestinal propulsion rate were measured. Relevant indicators were determined by ELISA. Results: The main target proteins included IL-1ß, C-C motif chemokine 2 (CCL2), prostaglandin G/H synthase 2 (PTGS2), transcription factor AP-1 (JUN), haem oxygenase 1 (HMOX1), interferon-gamma (IFN-γ), peroxisome proliferator-activated receptor-gamma (PPAR-γ), and inducible nitric oxide synthase (NOS2/iNOS). Compared with those in the control group, body weight, gastric emptying rate, intestinal propulsion rate, and neuronal nitric oxide synthase (NOS1/nNOS) levels were significantly lower in the model group, whereas body temperature and endotoxin, interleukin-1ß (IL-1ß), PGE2, and iNOS levels were increased. In each treatment group, body temperature and PGE2 levels returned to normal levels. Compared with those in the model group, the gastric emptying rates in the positive group and the low- and medium-dose groups increased; the intestinal propulsion rates were higher in the medium- and high-dose groups, whereas the endotoxin and IL-1ß levels were lower; and the nNOS level was higher in the high-dose group, whereas the iNOS level was lower. Conclusions: ZQCLD may treat FAF by regulating jejunal IL-1ß and nNOS, serum endotoxin, and hypothalamic PGE2 and iNOS levels.

Modular metabolic engineering of Bacillus amyloliquefaciens for high-level production of green biosurfactant iturin A.

As a kind of biosurfactants, iturin A has attracted people's wide attentions due to their features of biodegradability, environmentally friendly, etc.; however, high production cost limited its extensive application, and the aim of this research wants to improve iturin A production in Bacillus amyloliquefaciens. Firstly, dual promoter was applied to strengthen iturin A synthetase expression, and its yield was increased to 1.25 g/L. Subsequently, original 5'-UTRs of downstream genes (ituA, ituB, and ituC) in iturin A synthetase cluster were optimized, which significantly increased mRNA secondary stability, and iturin A yield produced by resultant strain HZ-T3 reached 2.32 g/L. Secondly, synthetic pathway of α-glucosidase inhibitor 1-deoxynojirimycin was blocked to improve substrate corn starch utilization, and iturin A yield was increased by 34.91% to 3.13 g/L. Thirdly, efficient precursor (fatty acids, Ser, and Pro) supplies were proven as the critical role in iturin A synthesis, and 5.52 g/L iturin A was attained by resultant strain, through overexpressing yngH, serC, and introducing ocD. Meanwhile, genes responsible for poly-γ-glutamic acid, extracellular polysaccharide, and surfactin syntheses were deleted, which led to a 30.98% increase of iturin A yield. Finally, lipopeptide transporters were screened, and iturin A yield was increased by 17.98% in SwrC overexpression strain, reached 8.53 g/L, which is the highest yield of iturin A ever reported. This study laid a foundation for industrial production and application development of iturin A, and provided the guidance of metabolic engineering breeding for efficient production of other metabolites synthesized by non-ribosomal peptide synthetase. KEY POINTS: • Optimizing 5'-UTR is an effective tactics to regulate synthetase cluster expression. • Blocking 1-DNJ synthesis benefited corn starch utilization and iturin A production. • The iturin A yield attained in this work was the highest yield reported so far.

Recognition of 3D Images by Fusing Fractional-Order Chebyshev Moments and Deep Neural Networks.

In order to achieve efficient recognition of 3D images and reduce the complexity of network parameters, we proposed a novel 3D image recognition method combining deep neural networks with fractional-order Chebyshev moments. Firstly, the fractional-order Chebyshev moment (FrCM) unit, consisting of Chebyshev moments and the three-term recurrence relation method, is calculated separately using successive integrals. Next, moment invariants based on fractional order and Chebyshev moments are utilized to achieve invariants for image scaling, rotation, and translation. This design aims to enhance computational efficiency. Finally, the fused network embedding the FrCM unit (FrCMs-DNNs) extracts depth features to analyze the effectiveness from the aspects of parameter quantity, computing resources, and identification capability. Meanwhile, the Princeton Shape Benchmark dataset and medical images dataset are used for experimental validation. Compared with other deep neural networks, FrCMs-DNNs has the highest accuracy in image recognition and classification. We used two evaluation indices, mean square error (MSE) and peak signal-to-noise ratio (PSNR), to measure the reconstruction quality of FrCMs after 3D image reconstruction. The accuracy of the FrCMs-DNNs model in 3D object recognition was assessed through an ablation experiment, considering the four evaluation indices of accuracy, precision, recall rate, and F1-score.

NeRF-Texture: Synthesizing Neural Radiance Field Textures.

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.

Polystyrene microplastics induce pulmonary fibrosis by promoting alveolar epithelial cell ferroptosis through cGAS/STING signaling.

Polystyrene microplastics (PS-MPs) are new types of environmental pollutant that have garnered significant attention in recent years since they were found to cause damage to the human respiratory system when they are inhaled. The pulmonary fibrosis is one of the serious consequences of PS-MPs inhalation. However, the impact and underlying mechanisms of PS-MPs on pulmonary fibrosis are not clear. In this study, we studied the potential lung toxicity and PS-MPs-developed pulmonary fibrosis by long-term intranasal inhalation of PS-MPs. The results showed that after exposing to the PS-MPs, the lungs of model mouse had different levels of damage and fibrosis. Meanwhile, exposing to the PS-MPs resulted in a markedly decrease in glutathione (GSH), an increase in malondialdehyde (MDA), and iron overload in the lung tissue of mice and alveolar epithelial cells (AECs). These findings suggested the occurrence of PS-MP-induced ferroptosis. Inhibitor of ferroptosis (Fer-1) had alleviated the PS-MPs-induced ferroptosis. Mechanically, PS-MPs triggered cell ferroptosis and promoted the development of pulmonary fibrosis via activating the cGAS/STING signaling pathway. Inhibition of cGAS/STING with G150/H151 attenuated pulmonary fibrosis after PS-MPs exposure. Together, these data provided novel mechanistic insights of PS-MPs-induced pulmonary fibrosis and a potential therapeutic paradigm.

Protective effect of liver X receptor on cigarette smoke and lipopolysaccharide induced airway inflammation and emphysema in mice.

OBJECTIVE: The aim of this study is to assess the impact of Liver X receptors (LXRs) on airway inflammation, airway remodeling, and lipid deposition induced by cigarette smoke and lipopolysaccharide (LPS) exposure in the lung. METHODS: Wild mice and LXR-deficient mice were exposed to cigarette smoke and LPS to induce airway inflammation and remodeling. In addition, some wild mice received intraperitoneal treatment with the LXR agonist GW3965 before exposure to cigarette smoke and LPS. Lung tissue and bronchoalveolar lavage fluid were collected to evaluate airway inflammation, airway remodeling and lipid deposition. RESULTS: Exposure to cigarette smoke and LPS resulted in airway inflammation, emphysema and lipid accumulation in wild mice. These mice also exhibited downregulated LXRα and ABCA1 in the lung. Treatment with GW3965 mitigated inflammation, remodeling and lipid deposition, while the deletion of LXRs exacerbated these effects. Furthermore, GW3965 treatment following exposure to cigarette smoke and LPS increased LXRα and ABCA1 expression and attenuated MyD88 expression in wild mice. CONCLUSION: LXRs demonstrate the potential to mitigate cigarette smoke and LPS- induced airway inflammation, emphysema and lipid disposition in mice.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

MOTIVATION: The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. RESULTS: In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. AVAILABILITY AND IMPLEMENTATION: https://github.com/SaisaiSun/RLaffinity.

Treatment outcome, toxicity, and quality of life of patients with bronchus-associated lymphoid tissue lymphoma.

The disease failure patterns and optimal treatment of bronchus-associated lymphoid tissue (BALT) lymphoma are unknown. This retrospective study involved 71 patients with primary BALT lymphoma who had received radiotherapy (RT), surgery, immunochemotherapy (IC), or observation. The median follow-up time was 66 months. The 5-year overall survival and lymphoma-specific survival were 91.2% and 96.1%, respectively, and were not significantly different among treatments. The 5-year cumulative incidence of overall failure for RT, surgery, IC, and observation was 0%, 9.7% (p = .160), 30.8% (p = .017), and 31.3% (p = .039). There was no grade ≥3 toxicity in RT group according to the CTCAE 5.0 reporting system. Quality of life (QoL) was at similarly good levels among the treatment groups. BALT lymphoma had a favorable prognosis but persistent risk of relapse after IC or observation. Given the very low disease failure risk and good QoL, RT remains an effective initial treatment for BALT lymphoma.Key PointsBALT lymphoma has a favorable prognosis but a persistent progression and relapse risk.Radiotherapy is associated with lower failure of disease progression and relapse, low toxicity and good quality of life.

Current clinical evidence is insufficient to support HMME-PDT as the first choice of treatment for young children with port wine birthmarks.

BACKGROUND: Port wine birthmark (PWB) is a congenital vascular malformation of the skin. Pulsed dye laser (PDL) is the "gold standard" for the treatment of PWB globally. Hematoporphyrin monomethyl ether (HMME or hemoporfin)-mediated photodynamic therapy (HMME-PDT) has emerged as the first choice for PWB treatment, particularly for young children, in many major hospitals in China during the past several decades. AIM: To evaluate whether HMME-PDT is superior to PDL by comparing the clinical efficacies of both modalities. METHOD: PubMed records were searched for all relevant studies of PWB treatment using PDL (1988-2023) or HMME-PDT (2007-2023). Patient characteristics and clinical efficacies were extracted. Studies with a quartile percentage clearance or similar scale were included. A mean color clearance index (CI) per study was calculated and compared among groups. An overall CI (C0), with data weighted by cohort size, was used to evaluate the final efficacy for each modality. RESULT: A total of 18 HMME-PDT studies with 3910 patients in China were eligible for inclusion in this analysis. Similarly, 40 PDL studies with 5094 patients from nine different countries were eligible for inclusion in this analysis. Over 58% of patients in the HMME-PDT studies were minors (<18 years old). A significant portion (21.3%) were young children (<3 years old). Similarly, 33.2% of patients in the PDL studies were minors. A small proportion (9.3%) was young children. The overall clearance rates for PDL were slightly, but not significantly, higher than those for HMME-PDT in cohorts with patients of all ages (C0, 0.54 vs. 0.48, p = 0.733), subpopulations with only minors (C0, 0.54 vs. 0.46, p = 0.714), and young children (C0, 0.67 vs. 0.50, p = 0.081). Regrettably, there was a lack of long-term data on follow-up evaluations for efficacy and impact of HMME-PDT on young children in general, and central nervous system development in particular, because their blood-brain barriers have a greater permeability as compared to adults. CONCLUSION: PDL shows overall albeit insignificantly higher clearance rates than HMME-PDT in patients of all ages; particularly statistical significance is nearly achieved in young children. Collectively, current evidence is insufficient to support HMME-PDT as the first choice of treatment of PWBs in young children given: (1) overall inferior efficacy as compared to PDL; (2) risk of off-target exposure to meningeal vasculature during the procedure; (3) administration of steriods for mitigation of side effects; -and (4) lack of long-term data on the potential impact of HMME on central nervous system development in young children.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.