Efficient Self-cleaning and antibacterial ceramics with active sites fully exposed obtained from rare earth waste.

The utilization of rare earth polishing powder waste (RPW) to prepare antibacterial ceramics can effectively avoid problems of pollution in the recycling process and waste of rare earth resources. Herein, a novel RPW-based antibacterial ceramics was developed, which possesses the core-shell structure with ceramics as the cores and the CeO2/BiOCl as the superficial coating. The antibacterial ceramics display notable antibacterial activity, and the inactivation rates of 3.3 log under visible light irradiation in 30 min and 2.4 log under darkness in 1 h were achieved, and the zone of inhibition values was found to be 16.6 mm for E.coil. The hardness of antibacterial ceramics was measured to be 897 (±38) HV, higher than commercial porcelain's hardness (600 HV). The antibacterial mechanism was verified by the Ce ion release, reactive species, and fluorescence-based live/dead cells. This study presents a novel antibacterial ceramic structure and green economic reuse method of rare earth waste.

From pixels to patient care: deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer.

BACKGROUND: Immunotherapy has significantly improved survival of esophageal squamous cell cancer (ESCC) patients, however the clinical benefit was limited to only a small portion of patients. This study aimed to perform a deep learning signature based on H&E-stained pathological specimens to accurately predict the clinical benefit of PD-1 inhibitors in ESCC patients. METHODS: ESCC patients receiving PD-1 inhibitors from Shandong Cancer Hospital were included. WSI images of H&E-stained histological specimens of included patients were collected, and randomly divided into training (70%) and validation (30%) sets. The labels of images were defined by the progression-free survival (PFS) with the interval of 4 months. The pretrained ViT model was used for patch-level model training, and all patches were projected into probabilities after linear classifier. Then the most predictive patches were passed to RNN for final patient-level prediction to construct ESCC-pathomics signature (ESCC-PS). Accuracy rate and survival analysis were performed to evaluate the performance of ViT-RNN survival model in validation cohort. RESULTS: 163 ESCC patients receiving PD-1 inhibitors were included for model training. There were 486,188 patches of 1024*1024 pixels from 324 WSI images of H&E-stained histological specimens after image pre-processing. There were 120 patients with 227 images in training cohort and 43 patients with 97 images in validation cohort, with balanced baseline characteristics between two groups. The ESCC-PS achieved an accuracy of 84.5% in the validation cohort, and could distinguish patients into three risk groups with the median PFS of 2.6, 4.5 and 12.9 months (P < 0.001). The multivariate cox analysis revealed ESCC-PS could act as an independent predictor of survival from PD-1 inhibitors (P < 0.001). A combined signature incorporating ESCC-PS and expression of PD-L1 shows significantly improved accuracy in outcome prediction of PD-1 inhibitors compared to ESCC-PS and PD-L1 anlone, with the area under curve value of 0.904, 0.924, 0.610 for 6-month PFS and C-index of 0.814, 0.806, 0.601, respectively. CONCLUSIONS: The outcome supervised pathomics signature based on deep learning has the potential to enable superior prognostic stratification of ESCC patients receiving PD-1 inhibitors, which convert the images pixels to an effective and labour-saving tool to optimize clinical management of ESCC patients.

Update of latest data for combined therapy for esophageal cancer using radiotherapy and immunotherapy: A focus on efficacy, safety, and biomarkers.

Esophageal cancer usually has a poor prognosis. Given the significant breakthrough with tumor immunotherapy, an increasing number of clinical studies have demonstrated that the combination of radiotherapy and immune checkpoint inhibitors (ICIs) may have a synergistic effect and good outcome in esophageal cancer. Clinical studies of immunoradiotherapy (iRT) for esophageal cancer have proliferated enormously from 2021 to the present. However, a summary of the efficacy and toxicity of combined therapy to guide esophageal cancer treatment in clinical practice is lacking. For this review, we integrate the latest data to analyze and assess the efficacy and safety of iRT for esophageal cancer. In addition, we discuss better predictive biomarkers, therapeutic options for specific populations, and other challenges to identify directions for future research design.

Outcome-Supervised Deep Learning on Pathologic Whole Slide Images for Survival Prediction of Immunotherapy in Patients with Non-Small Cell Lung Cancer.

Although programmed death-(ligand) 1 (PD-(L)1) inhibitors are marked by durable efficacy in patients with non-small cell lung cancer (NSCLC), approximately 60% of the patients still suffer from recurrence and metastasis after PD-(L)1 inhibitor treatment. To accurately predict the response to PD-(L)1 inhibitors, we presented a deep learning model using a Vision Transformer (ViT) network based on hematoxylin and eosin (H&E)-stained specimens of patients with NSCLC. Two independent cohorts of patients with NSCLC receiving PD-(L)1 inhibitors from Shandong Cancer Hospital and Institute and Shandong Provincial Hospital were enrolled for model training and external validation, respectively. Whole slide images (WSIs) of H&E-stained histologic specimens were obtained from these patients and patched into 1024 × 1024 pixels. The patch-level model was trained based on ViT to identify the predictive patches, and patch-level probability distribution was performed. Then, we trained a patient-level survival model based on the ViT-Recursive Neural Network framework and externally validated it in the Shandong Provincial Hospital cohort. A total of 291 WSIs of H&E-stained histologic specimens from 198 patients with NSCLC in Shandong Cancer Hospital and 62 WSIs from 30 patients with NSCLC in Shandong Provincial Hospital were included in the model training and validation. The model achieved an accuracy of 88.6% in the internal validation cohort and 81% in the external validation cohort. The survival model also remained a statistically independent predictor of survival from PD-(L)1 inhibitors. In conclusion, the outcome-supervised ViT-Recursive Neural Network survival model based on pathologic WSIs could be used to predict immunotherapy efficacy in patients with NSCLC.

Integrated multi-dimensional deep neural network model improves prognosis prediction of advanced NSCLC patients receiving bevacizumab.

Background: The addition of bevacizumab was found to be associated with prolonged survival whether in combination with chemotherapy, tyrosine kinase inhibitors or immune checkpoint inhibitors in the treatment landscape of advanced non-small cell lung cancer (NSCLC) patients. However, the biomarkers for efficacy of bevacizumab were still largely unknown. This study aimed to develop a deep learning model to provide individual assessment of survival in advanced NSCLC patients receiving bevacizumab. Methods: All data were retrospectively collected from a cohort of 272 radiological and pathological proven advanced non-squamous NSCLC patients. A novel multi-dimensional deep neural network (DNN) models were trained based on clinicopathological, inflammatory and radiomics features using DeepSurv and N-MTLR algorithm. And concordance index (C-index) and bier score was used to demonstrate the discriminatory and predictive capacity of the model. Results: The integration of clinicopathologic, inflammatory and radiomics features representation was performed using DeepSurv and N-MTLR with the C-index of 0.712 and 0.701 in testing cohort. And Cox proportional hazard (CPH) and random survival forest (RSF) models were also developed after data pre-processing and feature selection with the C-index of 0.665 and 0.679 respectively. DeepSurv prognostic model, indicated with best performance, was used for individual prognosis prediction. And patients divided in high-risk group were significantly associated with inferior PFS (median PFS: 5.4 vs 13.1 months, P<0.0001) and OS (median OS: 16.4 vs 21.3 months, P<0.0001). Conclusions: The integration of clinicopathologic, inflammatory and radiomics features representation based on DeepSurv model exhibited superior predictive accuracy as non-invasive method to assist in patients counseling and guidance of optimal treatment strategies.

The first salicylaldehyde Schiff base organic-inorganic hybrid lead iodide perovskite ferroelectric.

A salicylaldehyde Schiff base hybrid lead iodide perovskite [SAPD]PbI3 (SAPD = 1-((2-hydroxybenzylidene)amino)pyridin-1-ium) was found to show a robust nonlinear optical response and large spontaneous polarization. We expect this work to inspire researchers to investigate the optical control of ferroelectricity in hybrid perovskites.

Optically Induced Ferroelectric Polarization Switching in a Molecular Ferroelectric with Reversible Photoisomerization.

Ferroelectrics usually exhibit temperature-triggered structural changes, which play crucial roles in controlling their physical properties. However, although light is very striking as a non-contact, non-destructive, and remotely controlled external stimuli, ferroelectric crystals with light-triggered structural changes are very rare, which holds promise for optical control of ferroelectric properties. Here, an organic molecular ferroelectric, N-salicylidene-2,3,4,5,6-pentafluoroaniline (SA-PFA), which shows light-triggered structural change of reversible photoisomerization between cis-enol and trans-keto configuration is reported. SA-PFA presents clear ferroelectricity with the saturate polarization of 0.84 µC cm-2 , larger than those of some typical organic ferroelectrics with thermodynamically structural changes. Benefit from the reversible photoisomerization, the dielectric real part of SA-PFA can be reversibly switched by light. More strikingly, the photoisomerization enables SA-PFA to show reversible optically induced ferroelectric polarization switching. Such intriguing behaviors make SPFA a potential candidate for application in next-generation photo-controlled ferroelectric devices. This work sheds light on further exploration of more excellent molecular ferroelectrics with light-triggered structural changes for optical control of ferroelectric properties.

Effective multi-functional biosorbent derived from corn stalk pith for dyes and oils removal.

In this study, malic acid-modified corn stalk pith (MA-CSP) was prepared as an environmentally friendly multi-functional bio-sorbent for adsorbing of dyes and oils. The sorption capacity of the MA-CSP for single and binary dyes is 328.46 mg/g - 566.27 mg/g. In addition, the MA-CSP also had good sorption for lubricating oil, soybean oil, diesel oil, and isopropyl alcohol, which were 37.2 g/g, 44.1 g/g, 33.8 g/g, and 29.3 g/g, respectively. Physical and statistical models were used to analyze the adsorption behavior of methylene blue (MB) and crystal violet (CV). And its sorption behavior for dyes was also affected by the co-existing salts in water. The sorption mechanism of the dye was mainly electrostatic attraction and hydrogen bonding action. The sorption of oil was primarily via the role of van der Waals force and hydrophobic interaction. The MA-CSP, as an eco-friendly, economical and efficient multi-functional sorbent, holds promise for effective dyes and oil removal from contaminated water, and its application in other fields is also highly anticipated.

DM-RPIs: Predicting ncRNA-protein interactions using stacked ensembling strategy.

ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and time-consuming to identify the ncRPIs, we proposed a computational method, Deep Mining ncRNA-Protein Interactions (DM-RPIs), for identifying the ncRPIs. In order to descending dimension and excavating hidden information from k-mer frequency of RNA and protein sequences, using the Deep Stacking Auto-encoders Networks (DSANs) model refined the raw data. Three common machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN), were separately trained as individual predictors and then the three individual predictors were integrated together using stacked ensembling strategy. Based on the RPI2241 dataset, DM-RPI obtains an accuracy of 0.851, precision of 0.852, sensitivity of 0.873, specificity of 0.826, and MCC of 0.701, which is promising and pioneering for the prediction of ncRPIs.

Separation and confirmation of nine Enterobacteriaceae strains that carry the blaNDM-1 gene.

The aim of the present study was to confirm the existence of carbapenem-resistant Enterobacteriaceae carrying the blaNDM-1 gene in clinics in Hainan province, China. Collected clinical bacterial isolates that were Enterobacteriaceae strains suspected of producing carbapenemase were used as experimental strains. Drug resistance to imipenem, meropenem and other antibacterial agents was tested. Imipenem/imipenem inhibitor (IP/IPI) E-testing was conducted to identify the bacterial strains that produced metallo-ß-lactamases. The blaNDM-1 drug resistance gene was amplified by polymerase chain reaction (PCR), and agarose gel electrophoresis (AGE) and sequencing were conducted to identify the products. The species of the strains carrying the blaNDM-1 gene were determined using a biochemical identification system. Through the IP/IPI E-test, 21 of the 30 collected Enterobacteriaceae strains were found to be positive, indicating that 70% of the strains produced metallo-ß-lactamases. Following blaNDM-1 gene PCR amplification, AGE and sequencing tests confirmed that nine of the strains carried the blaNDM-1 drug resistance gene. The biochemical identification system indicated that four of the strains were Klebsiella pneumoniae, two were Escherichia coli, two were Enterobacter cloacae and one was Enterobacter aerogenes. Drug susceptibility testing in vitro demonstrated that the strains were 100% resistant to a broad spectrum antibiotic plus lactamase inhibitor, cephalosporins and carbapenems. However, they had high sensitivity rates to polymyxin B and tigecycline of 100 and 88.9%, respectively. The sensitivity rate to amikacin was also high at 77.8%, whereas sensitivity to ciprofloxacin and gentamicin was moderate at rates of 44.4 and 33.3% respectively. This clinical study of Enterobacteriaceae strains that carry the blaNDM-1 gene in Hainan shows a bacterial tolerance that is different from that in previous studies, which requires further in-depth study.

The potato developer (D) locus encodes an R2R3 MYB transcription factor that regulates expression of multiple anthocyanin structural genes in tuber skin.

A dominant allele at the D locus (also known as I in diploid potato) is required for the synthesis of red and purple anthocyanin pigments in tuber skin. It has previously been reported that D maps to a region of chromosome 10 that harbors one or more homologs of Petunia an2, an R2R3 MYB transcription factor that coordinately regulates the expression of multiple anthocyanin biosynthetic genes in the floral limb. To test whether D acts similarly in tuber skin, RT-PCR was used to evaluate the expression of flavanone 3-hydroxylase (f3h), dihydroflavonol 4-reductase (dfr) and flavonoid 3',5'-hydroxylase (f3'5'h). All three genes were expressed in the periderm of red- and purple-skinned clones, while dfr and f3'5'h were not expressed, and f3h was only weakly expressed, in white-skinned clones. A potato cDNA clone with similarity to an2 was isolated from an expression library prepared from red tuber skin, and an assay developed to distinguish the two alleles of this gene in a diploid potato clone known to be heterozygous Dd. One allele was observed to cosegregate with pigmented skin in an F(1) population of 136 individuals. This allele was expressed in tuber skin of red- and purple-colored progeny, but not in white tubers, while other parental alleles were not expressed in white or colored tubers. The allele was placed under the control of a doubled 35S promoter and transformed into the light red-colored cultivar Désirée, the white-skinned cultivar Bintje, and two white diploid clones known to lack the functional allele of D. Transformants accumulated pigment in tuber skin, as well as in other tissues, including young foliage, flower petals, and tuber flesh.

The potato R locus codes for dihydroflavonol 4-reductase.

The potato R locus is required for the production of red pelargonidin-based anthocyanin pigments in potato (Solanum tuberosum L.). Red color also requires tissue-specific regulatory genes, such as D (for expression in tuber skin) and F (expression in flowers). A related locus, P, is required for production of blue/purple anthocyanins; P is epistatic to R. We have previously reported that the dihydroflavonol 4-reductase gene (dfr) co-segregates with R. To test directly whether R corresponds to dfr, we placed the allele of dfr associated with red color under the control of the CaMV 35S promoter and introduced it into the potato cultivar Prince Hairy (genotype dddd rrrr P-), which has white tubers and pale blue flowers. Transgenic Prince Hairy tubers remained white, but flower color changed to purple. Three independent transgenic lines, as well as a vector-transformed line, were then crossed with the red-skinned variety Chieftain (genotype D-R-pppp), to establish populations that segregated for D, R, P, and the dfr transgene or empty vector. Markers were used to genotype progeny at D and R. Progeny carrying the empty vector in the genetic background D-rrrr produced white or purple tubers, while progeny with the same genotype and the dfr transgene produced red or purple tubers. HPLC and LC-MS/MS analyses of anthocyanins present in Chieftain and in a red-skinned progeny clone with the dfr transgene in a D-rrrr background revealed no qualitative differences. Thus, dfr can fully complement R, both in terms of tuber color and anthocyanin composition.

The potato P locus codes for flavonoid 3',5'-hydroxylase.

The potato P locus is required for the production of blue/purple anthocyanin pigments in any tissue of the potato plant such as tubers, flowers, or stems. We have previously reported, based on RFLP mapping in tomato, that the gene coding for the anthocyanin biosynthetic enzyme flavonoid 3',5'-hydroxylase (f3'5'h) maps to the same region of the tomato genome as P maps in potato. To further evaluate this association a Petunia f3'5'h gene was used to screen a potato cDNA library prepared from purple-colored flowers and stems. Six positively hybridizing cDNA clones were sequenced and all appeared to be derived from a single gene that shares 85% sequence identity at the amino acid level with Petunia f3'5'h. The potato gene cosegregated with purple tuber color in a diploid F1 sub-population of 37 purple and 25 red individuals and was found to be expressed in tuber skin only in the presence of the anthocyanin regulatory locus I. A potato f3'5'h cDNA clone was placed under the control of a doubled CaMV 35S promoter and introduced into the red-skinned cultivar 'Desiree'. Tuber and stem tissues that are colored red in Desiree were purple in nine of 17 independently transformed lines.