Functional study of the ST6GAL2 gene regulating skeletal muscle growth and development.

ST6GAL2, a member of the sialoglycosyltransferase family, primarily localizes within the cellular Golgi apparatus. However, the role of the ST6GAL2 gene in skeletal muscle growth and development remains elusive. In this study, the impact of the ST6GAL2 gene on the proliferation, differentiation, and apoptosis of primary chicken myoblasts at the cellular level was investigated. Quantitative fluorescent PCR was used to measure the expression levels of genes. Subsequently, using gene knockout mice, we assessed its effects on skeletal muscle growth and development in vivo. Our findings reveal that the ST6GAL2 gene promotes the expression of cell cycle and proliferation-related genes, including CCNB2 and PCNA, and apoptosis-related genes, such as Fas and Caspase-9. At the individual level, double knockout of ST6GAL2 inhibited the formation of both fast and slow muscle fibers in the quadriceps, extensor digitorum longus, and tibial anterior muscle, while promoting their formation in the gastrocnemius and soleus. These results collectively demonstrate that the ST6GAL2 gene facilitates the proliferation, apoptosis, and fusion processes of primary chicken myoblasts. Additionally, it promotes the enlargement of cross-sectional muscle fiber areas and regulates the formation of fast and slow muscle fibers at the individual level, albeit inhibiting muscle fusion. This study provides valuable insights into the role of the ST6GAL2 gene in promoting proliferation of skeletal muscle.

Elucidating the Molecular Interactions and Immune Modulation of Bisphenols Exposure in the Pathogenesis of Polycystic Ovary Syndrome.

Bisphenols (BPs) are known endocrine disruptors potentially contributing to the pathogenesis of Polycystic Ovary Syndrome (PCOS). This study aims to elucidate the molecular interactions between BPs and PCOS-related genes and their combined effects on PCOS development. We identified common genes associated with BPs and PCOS using the CTD. Differential expression analysis was performed on three GEO datasets, leading to the identification of differentially expressed genes (DEGs). Protein-Protein Interaction (PPI) network construction, enrichment analysis, single-gene Gene Set Enrichment Analysis (GSEA), and immune cell infiltration analysis were carried out. A nomogram was developed for PCOS risk prediction, and molecular docking studies were performed using AutoDock Vina, with interaction visualizations via PyMOL. We identified 139 common genes between BPs exposure and PCOS, enrichment analysis highlighted pathways related to hormone metabolism, ovarian steroidogenesis, and p53 signaling. Four hub DEGs (PBK, CCNE2, LPCAT2, S100P) were identified, and a nomogram incorporating these genes demonstrated excellent predictive accuracy. GSEA revealed roles in cell adhesion, immune response, and metabolism. ssGSEA analysis showed significant differences in immune cell infiltration between PCOS and control groups, with notable correlations between hub DEGs and immune cells. Molecular docking indicated strong binding affinities between the hub DEGs and BPAF, suggesting potential disruptions induced by BPs. BPs exposure is associated with significant molecular and immunological changes in PCOS, impacting genes involved in hormone regulation, immune response, and metabolic pathways. The strong binding affinities between BPs and key PCOS-related genes reveal their potential role in exacerbating PCOS, providing insights for targeted therapeutic strategies.

Circularly Polarized Lasing from Helical Superstructures of Chiral Organic Molecules.

Chiral supramolecular aggregates have the potential to explore circularly polarized lasing with large dissymmetry factors. However, the controllable assembly of chiral superstructures towards deterministic circularly polarized laser emission remains elusive. Here, we design a pair of chiral organic molecules capable of stacking into a pair of definite helical superstructures in microcrystals, which enables circularly polarized lasing with deterministic chirality and high dissymmetry factors. The microcrystals function as optical cavities and gain media simultaneously for laser oscillations, while the supramolecular helices endow the laser emission with strong and opposite chirality. As a result, the microcrystals of two enantiomers allow for circularly polarized laser emission with opposite chirality and high dissymmetry factors up to ~1.0. This work demonstrates the chiral supramolecular assemblies as an excellent platform for high-performance circularly polarized lasers.

Balancing volumetric and gravimetric capacity for hydrogen in supramolecular crystals.

The storage of hydrogen is key to its applications. Developing adsorbent materials with high volumetric and gravimetric storage capacities, both of which are essential for the efficient use of hydrogen as a fuel, is challenging. Here we report a controlled catenation strategy in hydrogen-bonded organic frameworks (RP-H100 and RP-H101) that depends on multiple hydrogen bonds to guide catenation in a point-contact manner, resulting in high volumetric and gravimetric surface areas, robustness and ideal pore diameters (~1.2-1.9 nm) for hydrogen storage. This approach involves assembling nine imidazole-annulated triptycene hexaacids into a secondary hexagonal superstructure containing three open channels through which seven of the hexagons interpenetrate to form a seven-fold catenated superstructure. RP-H101 exhibits high deliverable volumetric (53.7 g l-1) and gravimetric (9.3 wt%) capacities for hydrogen under a combined temperature and pressure swing (77 K/100 bar → 160 K/5 bar). This work illustrates the virtues of supramolecular crystals as promising candidates for hydrogen storage.

Hydrogen-Bonded Fibrous Nanotubes Assembled from Trigonal Prismatic Building Blocks.

In reticular chemistry, molecular building blocks are designed to create crystalline open frameworks. A key principle of reticular chemistry is that the most symmetrical networks are the likely outcomes of reactions, particularly when highly symmetrical building blocks are involved. The strategy of synthesizing low-dimensional networks aims to reduce explicitly the symmetry of the molecular building blocks. Here we report the spontaneous formation of hydrogen-bonded fibrous structures from trigonal prismatic building blocks, which were designed to form three-dimensional crystalline networks on account of their highly symmetrical structures. Utilizing different microscopic and spectroscopic techniques, we identify the structures at the early stages of the assembly process in order to and understand the growth mechanism. The symmetrical molecular building blocks are incorporated preferentially in the longitudinal direction, giving rise to anisotropic hydrogen-bonded porous organic nanotubes. Entropy-driven anisotropic growth provides micrometer-scale unidirectional nanotubes with high porosity. By combining experimental evidence and theoretical modeling, we have obtained a deep understanding of the nucleation and growth processes. Our findings offer fundamental insight into the molecular design of tubular structures. The nanotubes evolve further in the transverse directions to provide extended higher-order fibrous structures [nano- and microfibers], ultimately leading to large-scale interconnected hydrogen-bonded fiber-like structures with twists and turns. Our work provides fundamental understanding and paves the way for innovative molecular designs in low-dimensional networks.

A Geometrically Flexible Three-Dimensional Nanocarbon.

The development of architecturally unique molecular nanocarbons by bottom-up organic synthesis is essential for accessing functional organic materials awaiting technological developments in fields such as energy, electronics, and biomedicine. Herein, we describe the design and synthesis of a triptycene-based three-dimensional (3D) nanocarbon, GFN-1, with geometrical flexibility on account of its three peripheral π-panels being capable of interconverting between two curved conformations. An effective through-space electronic communication among the three π-panels of GFN-1 has been observed in its monocationic radical form, which exhibits an extensively delocalized spin density over the entire 3D π-system as revealed by electron paramagnetic resonance and UV-vis-NIR spectroscopies. The flexible 3D molecular architecture of GFN-1, along with its densely packed superstructures in the presence of fullerenes, is revealed by microcrystal electron diffraction and single-crystal X-ray diffraction, which establish the coexistence of both propeller and tweezer conformations in the solid state. GFN-1 exhibits strong binding affinities for fullerenes, leading to host-guest complexes that display rapid photoinduced electron transfer within a picosecond. The outcomes of this research could pave the way for the utilization of shape and electronically complementary nanocarbons in the construction of functional coassemblies.

Adaptive Helical Chirality in Supramolecular Microcrystals for Circularly Polarized Lasing.

Chiral organic molecules offer a promising platform for exploring circularly polarized lasing, which, however, faces a great challenge that the spatial separation of molecular chiral and luminescent centers limits chiroptical activity. Here we develop a helically chiral supramolecular system with completely overlapped chiral and luminescent units for realizing high-performance circularly polarized lasing. Adaptive helical chirality is obtained by incorporating chiral agents into organic microcrystals. Benefiting from the efficient coupling of stimulated emission with the adaptive helical chirality, the supramolecular microcrystals enable high-performance circularly polarized lasing emission with dissymmetry factors up to ~0.7. This work opens up the way to rational design of chiral organic materials for circularly polarized lasing.

Vaginal microbiota are associated with in vitro fertilization during female infertility.

The vaginal microbiome plays an essential role in the reproductive health of human females. As infertility increases worldwide, understanding the roles that the vaginal microbiome may have in infertility and in vitro fertilization (IVF) treatment outcomes is critical. To determine the vaginal microbiome composition of 1411 individuals (1255 undergoing embryo transplantation) and their associations with reproductive outcomes, clinical and biochemical features are measured, and vaginal samples are 16S rRNA sequenced. Our results suggest that both too high and too low abundance of Lactobacillus is not beneficial for pregnancy; a moderate abundance is more beneficial. A moderate abundance of Lactobacillus crispatus and Lactobacillus iners (~80%) (with a pregnancy rate of I-B: 54.35% and III-B: 57.73%) is found beneficial for pregnancy outcomes compared with a higher abundance (>90%) of Lactobacillus (I-A: 44.81% and III-A: 51.06%, respectively). The community state type (CST) IV-B (contains a high to moderate relative abundance of Gardnerella vaginalis) shows a similar pregnant ratio (48.09%) with I-A and III-A, and the pregnant women in this CST have a higher abundance of Lactobacillus species. Metagenome analysis of 71 samples shows that nonpregnant women are detected with more antibiotic-resistance genes, and Proteobacteria and Firmicutes are the main hosts. The inherent differences within and between women in different infertility groups suggest that vaginal microbes might be used to detect infertility and potentially improve IVF outcomes.

Fc-Fc interactions and immune inhibitory effects of IgG4: implications for anti-PD-1 immunotherapies.

BACKGROUND: The majority of anti-programmed cell-death 1 (PD-1) monoclonal antibodies (mAbs) use S228P mutation IgG4 as the structural basis to avoid the activation of immune cells or complement. However, little attention has been paid to the Fc-Fc interactions between IgG4 and other IgG Fc fragments that could result in adverse effects. Fc-null IgG1 framework is a potential safer alternative to avoid the undesirable Fc-Fc interactions and Fc receptor binding derived effects observed with IgG4. This study provides a comprehensive evaluation of anti-PD-1 mAbs of these two frameworks. METHODS: Trastuzumab and rituximab (both IgG1), wildtype IgG1 and IgG4 were immobilized on nitrocellulose membranes, coated to microplates and biosensor chips, and bound to tumor cells as targets for Fc-Fc interactions. Wildtype IgG1 and IgG4, anti-PD-1 mAb nivolumab (IgG4 S228P), penpulimab (Fc-null IgG1), and tislelizumab (Fc-null IgG4 S228P-R409K) were assessed for their binding reactions to the immobilized IgG proteins and quantitative kinetic data were obtained. To evaluate the effects of the two anti-PD-1 mAbs on immune responses mediated by trastuzumab and rituximab in the context of combination therapy, we employed classic immune models for antibody-dependent cellular cytotoxicity, antibody-dependent cellular phagocytosis, and complement dependent cytotoxicity. Tumor-bearing mouse models, both wildtype and humanized, were used for in vivo investigation. Furthermore, we also examined the effects of IgG1 and IgG4 on diverse immune cell populations RESULTS: Experiments demonstrated that wildtype IgG4 and nivolumab bound to immobilized IgG through Fc-Fc interactions, diminishing antibody-dependent cell-mediated cytotoxicity and phagocytosis reactions. Quantitative analysis of kinetic parameters suggests that nivolumab and wildtype IgG4 exhibit comparable binding affinities to immobilized IgG1 in both non-denatured and denatured states. IgG4 exerted inhibitory effects on various immune cell types. Wildtype IgG4 and nivolumab both promoted tumor growth in wildtype mouse models. Conversely, wildtype IgG1, penpulimab, and tislelizumab did not show similar adverse effects. CONCLUSIONS: Fc-null IgG1 represents a safer choice for anti-PD-1 immunotherapies by avoiding both the adverse Fc-Fc interactions and Fc-related immune inhibitory effects of IgG4. Fc-null IgG4 S228P-R409K and Fc-null IgG1 displayed similar structural properties and benefits. This study contributes to the understanding of immunotherapy resistance and the advancement of safer immune therapies for cancer.

A nomogram to predict the risk of proliferative lupus nephritis in patients with systemic lupus erythematosus involving the kidneys.

Proliferative lupus nephritis (PLN) is a serious organ-threatening manifestation of systemic lupus erythematosus (SLE) that is associated with high mortality and renal failure. Here, we analyzed data from 1287 SLE patients with renal manifestations, including 780 of which were confirmed as proliferative or non-proliferative LN patients by renal biopsy, divided into a training cohort (547 patients) and a validation cohort (233 patients). By applying a least absolute shrinkage and selection operator (LASSO) regression approach combined with multivariate logistic regression analysis to build a nomogram for prediction of PLN that was then assessed by receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves (DCA) in both the training and validation cohorts. The area under the ROC curve (AUC) of the model in the training cohort was 0.921 (95% confidence interval (CI): 0.895-0.946), the AUC of internal validation in the training cohort was 0.909 and the AUC of external validation was 0.848 (95% CI: 0.796-0.900). The nomogram showed good performance as evaluated using calibration and DCA curves. Taken together, our results indicate that our nomogram that comprises 12 significantly relevant variables could be clinically valuable to prognosticate on the risk of PLN in SLE, so as to improve patient prognoses.

Machine learning models predicts risk of proliferative lupus nephritis.

Objective: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe. Methods: This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers. Results: Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN. Conclusion: This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.

The impacts of biodegradable and non-biodegradable microplastic on the performance and microbial community characterization of aerobic granular sludge.

Introduction: Microplastics (MPs), identified as emerging contaminants, have been detected across diverse environmental media. Their enduring presence and small size facilitate the adsorption of organic pollutants and heavy metals, leading to combined pollution effects. MPs also accumulate in the food chain thus pose risks to animals, plants, and human health, garnering significant scholarly attention in recent years. Aerobic granular sludge (AGS) technology emerges as an innovative approach to wastewater treatment. However, the impacts of MPs on the operational efficiency and microbial characteristics of AGS systems has been insufficiently explored. Methods: This study investigated the effects of varying concentration (10, 50, and 100 mg/L) of biodegradable MPs (Polylactic Acid, PLA) and non-biodegradable MPs (Polyethylene Terephthalate, PET) on the properties of AGS and explored the underlying mechanisms. Results and discussions: It was discovered that low and medium concentration of MPs (10 and 50 mg/L) showed no significant effects on COD removal by AGS, but high concentration (100 mg/L) of MPs markedly diminished the ability to remove COD of AGS, by blocking most of the nutrient transport channels of AGS. However, both PLA and PE promoted the nitrogen and phosphorus removal ability of AGS, and significantly increased the removal efficiency of total inorganic nitrogen (TIN) and total phosphorus (TP) at stages II and III (P < 0.05). High concentration of MPs inhibited the growth of sludge. PET noticeably deteriorate the sedimentation performance of AGS, while 50 mg/L PLA proved to be beneficial to sludge sedimentation at stage II. The addition of MPs promoted the abundance of Candidatus_Competibacter and Acinetobacter in AGS, thereby promoting the phosphorus removal capacity of AGS. Both 50 mg/L PET and 100 mg/L PLA caused large amount of white Thiothrix filamentous bacteria forming on the surface of AGS, leading to deterioration of the sludge settling performance and affecting the normal operation of the reactor. Comparing with PET, AGS proved to be more resistant to PLA, so more attention should be paid to the effect of non-biodegradable MPs on AGS in the future.

Unveiling an oxidative stress-linked diagnostic signature and molecular subtypes in preeclampsia: novel insights into pathogenesis.

Preeclampsia (PE) is a complex pregnancy disorder characterized by hypertension and organ dysfunction, affecting both maternal and fetal health. Oxidative stress has been implicated in the pathogenesis of PE, but the underlying molecular mechanisms remain poorly understood. In this study, we aimed to identify a diagnostic signature and molecular subtypes associated with oxidative stress in PE to gain novel insights into its pathogenesis. The ssGSEA algorithm evaluated oxidative stress-related pathway scores using transcriptional data from the GSE75010 dataset. Oxidative stress-related genes (ORGs) were co lected from these pathways, and hub ORGs associated with PE were identified using the LASSO and logistic regression models. A nomogram prediction model was constructed using the identified ORGs. Consensus clustering identified two molecular subgroups related to oxidative stress, labeled as C1 and C2, with unique immune characteristics and inflammatory pathway profiles. Seventy ORGs associated with oxidative stress, ce l death, and inflammation-related pathways were identified in PE. EGFR, RIPK3, and ALAD were confirmed as core ORGs for PE biomarkers. The C1 and C2 subgroups exhibited distinct immune characteristics and inflammatory pathway profiles. This study provides novel insights into the role of oxidative stress in PE pathogenesis. A diagnostic signature and molecular subtypes associated with oxidative stress were identified, which may improve understanding, diagnosis, and management of PE.

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.

BACKGROUND: Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection. METHODS: One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance. RESULTS: The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved. CONCLUSIONS: Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Evaluation of the effect of a novel substrate that is composed of landfill-mined-soil-like-fractions on plant growth and heavy metal accumulation.

In the pursuit of a safe, low-cost, and sustainable method for the reuse of landfill-mined-soil-like-fractions (LFMSFs), pot experiments were conducted using seven growth substrates consisting of LFMSFs, tea residue, and peat for the cultivation of Photinia × fraseri. Six of the substrates had 40 %:60 %, 60 %:40 %, and 80 %:20 % volume ratios of LFMSFs to tea residue or peat, and one substrate consisted entirely of LFMSFs. The physicochemical properties of the substrate, growth parameters of the plants, and heavy metal content in the different pots were determined after one year of growth. The results indicated that the physicochemical properties of the substrate, that was composed of a mixture of LFMSFs and tea residue showed a significant improvement in organic matter, nitrogen, phosphorus, and potassium. However, there was also an increase in the salt and heavy metal contents when compared with those of peat. The plant growth in the LFMSF and tea residue substrate was slightly lower than that in the LFMSF and peat mixture. Notably, the best plant growth and environmentally friendly effects were observed when LFMSFs were added at 40 %. Additionally, most of the heavy metals were primarily removed from the substrate through the leaves of the seedlings, with the heavy metal contents being relatively low. In conclusion, LFMSFs as a cultivation substrate, represent a practical approach for reutilization, which could contribute to the reduction of reliance on traditional resources.

An intronic enhancer of Cebpa regulates adipocyte differentiation and adipose tissue development via long-range loop formation.

Cebpa is a master transcription factor gene for adipogenesis. However, the mechanisms of enhancer-promoter chromatin interactions controlling Cebpa transcriptional regulation during adipogenic differentiation remain largely unknown. To reveal how the three-dimensional structure of Cebpa changes during adipogenesis, we generated high-resolution chromatin interactions of Cebpa in 3T3-L1 preadipocytes and 3T3-L1 adipocytes using circularized chromosome conformation capture sequencing (4C-seq). We revealed dramatic changes in chromatin interactions and chromatin status at interaction sites during adipogenic differentiation. Based on this, we identified five active enhancers of Cebpa in 3T3-L1 adipocytes through epigenomic data and luciferase reporter assays. Next, epigenetic repression of Cebpa-L1-AD-En2 or -En3 by the dCas9-KRAB system significantly down-regulated Cebpa expression and inhibited adipocyte differentiation. Furthermore, experimental depletion of cohesin decreased the interaction intensity between Cebpa-L1-AD-En2 and the Cebpa promoter and down-regulated Cebpa expression, indicating that long-range chromatin loop formation was mediated by cohesin. Two transcription factors, RXRA and PPARG, synergistically regulate the activity of Cebpa-L1-AD-En2. To test whether Cebpa-L1-AD-En2 plays a role in adipose tissue development, we injected dCas9-KRAB-En2 lentivirus into the inguinal white adipose tissue (iWAT) of mice to suppress the activity of Cebpa-L1-AD-En2. Repression of Cebpa-L1-AD-En2 significantly decreased Cebpa expression and adipocyte size, altered iWAT transcriptome, and affected iWAT development. We identified functional enhancers regulating Cebpa expression and clarified the crucial roles of Cebpa-L1-AD-En2 and Cebpa promoter interaction in adipocyte differentiation and adipose tissue development.

Dynamic mRNA expression during chicken ovarian follicle development.

Ovarian follicle development is a complex and well-orchestrated biological process of great economic significance for poultry production. Specifically, understanding the molecular mechanisms underlying follicular development is essential for high-efficiency follicular development can benefit the entire industry. In addition, domestic egg-laying hens often spontaneously develop ovarian cancer, providing an opportunity to study the genetic, biochemical, and environmental risk factors associated with the development of this cancer. Here, we provide high-quality RNA sequencing data for chicken follicular granulosa cells across 10 developmental stages, which resulted in a total of 204.57 Gb of clean sequencing data (6.82 Gb on average per sample). We also performed gene expression, time-series, and functional enrichment analyses across the 10 developmental stages. Our study revealed that SWF (small while follicle), F1 (F1 hierarchical follicles), and POFs (postovulatory follicles) best represent the transcriptional changes associated with the prehierarchical, preovulatory, and postovulatory stages, respectively. We found that the preovulatory stage F1 showed the greatest divergence in gene expression from the POF stage. Our research lays a foundation for further elucidation of egg-laying performance of chicken and human ovarian disease.

Exciton Chirality Transfer Empowers Self-Triggered Spin-Polarized Amplified Spontaneous Emission from 1D-Anchoring-3D Perovskites.

Spin-polarized lasers, arising from stimulated emission of imbalanced spin populations, play a vital role in spin-optoelectronics. It is usually tackled by external spin injection, inevitably suffering from additional losses across the barriers from injection sources to gain materials. Herein, spin-polarized coherent light emission is self-triggered from the 1D-anchoring-3D perovskites, where the imbalanced populations in achiral 3D perovskites are endowed with the spin selectivity of exciton chirality (EC) underpinned by chiral 1D perovskites. Efficient transfer of EC is enabled by rapid energy transfer, thereby creating an imbalance of the spin population of excited states. Stimulated emission of such populations brings self-triggered spin-polarized amplified spontaneous emission in the composite perovskites, yielding a higher degree of polarization (DOP) than that based on optical spin injection into bare achiral 3D perovskites. Chemical diversity of composite perovskites not only enables to adjust band gap for broadband output of spin-polarized light signals but also promises to manipulate radiative decay and spin relaxation toward remarkably increased DOP. These results highlight the importance of EC transfer mechanism for spin-polarized lasing and represent a crucial step toward the development of chiral-spintronics.

A novel staging system derived from natural language processing of pathology reports to predict prognostic outcomes of pancreatic cancer: a retrospective cohort study.

OBJECTIVE: To construct a novel tumor-node-morphology (TNMor) staging system derived from natural language processing (NLP) of pathology reports to predict outcomes of pancreatic ductal adenocarcinoma. METHOD: This retrospective study with 1657 participants was based on a large referral center and The Cancer Genome Atlas Program (TCGA) dataset. In the training cohort, NLP was used to extract and screen prognostic predictors from pathology reports to develop the TNMor system, which was further evaluated with the tumor-node-metastasis (TNM) system in the internal and external validation cohort, respectively. Main outcomes were evaluated by the log-rank test of Kaplan-Meier curves, the concordance index (C-index), and the area under the receiver operating curve (AUC). RESULTS: The precision, recall, and F1 scores of the NLP model were 88.83, 89.89, and 89.21%, respectively. In Kaplan-Meier analysis, survival differences between stages in the TNMor system were more significant than that in the TNM system. In addition, our system provided an improved C-index (internal validation, 0.58 vs. 0.54, P <0.001; external validation, 0.64 vs. 0.63, P <0.001), and higher AUCs for 1, 2, and 3-year survival (internal validation: 0.62 vs. 0.54, P <0.001; 0.64 vs. 0.60, P= 0.017; 0.69 vs. 0.62, P= 0.001; external validation: 0.69 vs. 0.65, P= 0.098; 0.68 vs. 0.64, P= 0.154; 0.64 vs. 0.55, P= 0.032, respectively). Finally, our system was particularly beneficial for precise stratification of patients receiving adjuvant therapy, with an improved C-index (0.61 vs. 0.57, P <0.001), and higher AUCs for 1-year, 2-year, and 3-year survival (0.64 vs. 0.57, P <0.001; 0.64 vs. 0.58, P <0.001; 0.67 vs. 0.61, P <0.001; respectively) compared with the TNM system. CONCLUSION: These findings suggest that the TNMor system performed better than the TNM system in predicting pancreatic ductal adenocarcinoma prognosis. It is a promising system to screen risk-adjusted strategies for precision medicine.

Three Dimensional Shape Reconstruction via Polarization Imaging and Deep Learning.

Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target's surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.