Unlocking amplified magneto-photocurrent using multi-field manipulation in nonfullerene organic bulk heterojunction systems.

An effective manipulation of polaron pairs (PPs) for realizing amplified magneto-photocurrent (AMPC) is of critical importance toward the development of low power consumption and high-performance organic spin-optoelectronic devices, for instance magneto-photo-volatile memories. By far, it is challenging and there is a lack of method to reach AMPC. The typical magneto-photocurrent due to the light-matter interanion is primarily for unveiling the spin-dependent electron-hole dissociation in organic solar cells. Herein, we achieved an AMPC of ∼140% in nonfullerene organic bulk heterojunction systems at room temperature. We found that the amplification can be effectively triggered by a multi-field to a large number of photogenerated PPs at intermediate charge transfer states. We further postulate that, at steady state, they may experience a cyclic photophysical process due to the triplet-exciton polaron interaction. This study paves the way for the realization of AMPC in the organic spin-optoelectronic system.

Synergistically Constructing High-Dielectric Poly(m-phenylene isophthalamide)/Silica/Cellulose Nanofiber Composite Insulation Paper through Dynamic Covalent Chemistry and Hydrogen Bonding.

The rapid advancement of modern power equipment and high-power devices has imposed increasingly stringent demands upon the mechanical and dielectric properties of electrical insulation materials. Herein, we report a poly(m-phenylene isophthalamide) (PMIA) composite insulating paper with excellent dielectric breakdown strength, reliable mechanical properties, and high thermal stability. Enhanced surface activity of PMIA is achieved through surface modification, facilitating the synergistic integration of modified PMIA (MPMIA), bovine serum albumin (BSA)/silica (SiO2), and cellulose nanofiber (CNF) into composite paper using dynamic covalent bonds and hydrogen bonding. The prepared MPMIA-BSA/SiO2-CNF composite paper exhibits a laminated stacked structure with high tensile strength (32.68 MPa) and strain at break (9.57%). Meanwhile, MPMIA-BSA/SiO2-CNF composites have excellent dielectric breakdown strength (24.75 kV/mm) and good temperature resistance. Therefore, the MPMIA-BSA/SiO2-CNF composite paper has a broad application prospect in the field of high-voltage and high-power electrical equipment insulation.

Recombinant Lactococcus lactis secreting FliC protein nanobodies for resistance against Salmonella enteritidis invasion in the intestinal tract.

Salmonella Enteritidis is a major foodborne pathogen throughout the world and the increase in antibiotic resistance of Salmonella poses a significant threat to public safety. Natural nanobodies exhibit high affinity, thermal stability, ease of production, and notably higher diversity, making them widely applicable for the treatment of viral and bacterial infections. Recombinant expression using Lactococcus lactis leverages both acid resistance and mucosal colonization properties of these bacteria, allowing the effective expression of exogenous proteins for therapeutic effects. In this study, nine specific nanobodies against the flagellar protein FliC were identified and expressed. In vitro experiments demonstrated that FliC-Nb-76 effectively inhibited the motility of S. Enteritidis and inhibited its adhesion to and invasion of HIEC-6, RAW264.7, and chicken intestinal epithelial cells. Additionally, a recombinant L. lactis strain secreting the nanobody, L. lactis-Nb76, was obtained. Animal experiments confirmed that it could significantly reduce the mortality rates of chickens infected with S. Enteritidis, together with alleviating the inflammatory response caused by the pathogen. These results provide a novel strategy for the treatment of antibiotic-resistant S. Enteritidis infection in the intestinal tract.

Bioinformatic-Experimental Screening Uncovers Multiple Targets for Increase of MHC-I Expression through Activating the Interferon Response in Breast Cancer.

Expression of major histocompatibility complex I (MHC-I) on tumor cells is extremely important for the antitumor immune response for its essential role in activating various immune cells, including tumor-specific CD8+ T cells. Cancers of lower MHC-I expression commonly exhibit less immune cell infiltration and worse prognosis in clinic. In this study, we conducted bioinformatic-experimental screening to identify potential gene targets to enhance MHC-I expression in breast cancer (BRCA). Through a combination of MHC-I scoring, gene expression correlation analysis, survival prognostication, and Cibersort tumor-infiltrated lymphocytes (TILs) scoring, we identify 144 genes negatively correlated with both MHC-I expression and TILs in breast cancer. Furthermore, we verified partially according to KEGG functional enrichment or gene-dependency analysis and figured out multiple genes, including PIP5K1A, NCKAP1, CYFIP1, DIS3, TBP, and EXOC1, as effective gene targets for increasing MHC-I expression in breast cancer. Mechanistically, knockout of each of these genes activated the intrinsic interferon response in breast cancer cells, which not only promoted MHC-I expression but also caused immunogenic cell death of breast cancer. Finally, the scRNA-seq confirmed the negative correlation of PIP5K1A et al. with TILs in breast cancer patients. Collectively, we identified multiple gene targets for an increase in MHC-I expression in breast cancer in this study.

Pyramid-YOLOv8: a detection algorithm for precise detection of rice leaf blast.

Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods' time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model's computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS.

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Examining the audiovisual therapy effects on hospital groups of varying linear canopy landscapes and those with hydrodynamic forces.

Recent research has highlighted the beneficial effects of urban green spaces on physical and mental health. This study focused on the hospital population and innovatively subdivided the population into four groups: doctors, caregivers, patients and nurses. A total of 96 volunteers participated in this virtual reality experiment to assess the restoration of a linear canopy landscape and a landscape with different levels of hydrodynamics through interactive audiovisual immersion. We utilized pre-research method, brainwave monitoring technique, psychological scales, observation and interviews in this experiment. The research identified five key findings. First, both linear canopy landscapes and those with low to medium hydrodynamic forces significantly enhance physiological and psychological restoration for all groups, with the most substantial physiological benefits observed in doctors and patients, and the greatest psychological relief noted in caregivers. Second, landscapes with medium hydrodynamic forces yield higher restorative effects than those with low forces in hospital settings. Third, green landscapes with medium and low-density canopies prove more conducive to patient recovery compared to those with high-density canopies. Fourth, the inclusion of bird songs does not markedly affect physiological restoration across the hospital groups. Finally, landscapes that incorporate elements of water dynamics, open skies, and lightly foliated canopies draw significant interest from all groups involved. This study advocates for the integration of natural blue and green elements into hospital environments as complementary therapeutic interventions, aiming to alleviate stress and promote health recovery among hospital communities.

Isolation and characterization of porcine epidemic diarrhea virus with mutations in the spike gene in China.

Porcine epidemic diarrhea (PED) caused by porcine epidemic diarrhea virus (PEDV) has caused enormous economic losses to the global swine industry. Due to frequent mutations in the spike (S) gene of PEDV, commercial vaccines used today are gradually losing their protective efficacy against variants. It's significant to monitor the S gene of PEDV variants and understand its evolutionary trend. In this study, we report four novel PEDV strains isolated from Sichuan, Guangdong and Shanxi Provinces and determined their S gene sequences. Phylogenetic analysis showed that they all belong to GII genotype. Amino acid alignment revealed a unique mutation pattern. We also predicted their three-dimensional structures and continuous B-cell epitopes and compared them to those of the vaccine strain. Our study provides references for understanding the evolution of S gene and antigenic change of S protein, which are of great significance for formulating the prevention and control of PEDV.

Ultrastrong Nanopapers with Aramid Nanofibers and Silver Nanowires Reinforced by Cellulose Nanofibril-Assisted Dispersed Graphene Nanoplates for Superior Electromagnetic Interference Shielding.

High-strength, lightweight, ultrathin, and flexible electromagnetic interference (EMI) shielding materials with a high shielding effectiveness (SE) are essential for modern integrated electronics. Herein, cellulose nanofibrils (CNFs) are employed to homogeneously disperse graphene nanoplates (GNPs) into an aramid nanofiber (ANF) network and silver nanowire (AgNW) network, respectively, producing high-performance nanopapers. These nanopapers, featuring nacre-mimetic microstructures and layered architectures, exhibited high tensile strength (601.11 MPa) and good toughness (103.56 MJ m-3) with a thickness of only 24.58 µm. Their specific tensile strength reaches 447.59 MPa·g-1·cm3, which is 1.74 times that of titanium alloys (257 MPa·g-1·cm3). The AgNW/GNP composite conductive layers exhibit an electrical conductivity of 12010.00 S cm-1, providing the nanopapers with great EMI shielding performance, achieving an EMI SE of 63.87 dB and an EMI SE/t of 25978.80 dB cm-1. The nanopapers also show reliable durability, retaining a tensile strength of 500.96 MPa and an EMI SE of 57.59 dB after 120,000 folding cycles. Additionally, they have a good electrical heating performance with a fast response time, low driving voltage, effective deicing capability, and reliable heating capacity in water. This work presents a strategy to develop a high-performance nanopaper, showing great potential for applications in electromagnetic compatibility, national defense, smart electronics, and human health.

3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation.

Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images and, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models and interactive segmentation models. We also compared our adaptation method with existing popular adapters and observed significant performance improvement on most datasets. Our code and models are available at: https://github.com/med-air/3DSAM-adapter.

Protective effects of degraded Bletilla striata polysaccharides against UVB-induced oxidative stress in skin.

The Bletilla striata polysaccharides (BSP) extracted through alkali-assisted method exhibit significant antioxidant activity, but its bioaccessibility was inadequate due to its tightly filamentous reticulation structure and high molecular weight. The anti-photoaging and anti-melanogenesis effects of degraded BSP (DBSPs) against UVB-induced oxidative stress on the skin were investigated. The molecular weights of the DBSPs were reduced to 153.94 kDa, 66.96 kDa, and 15.54 kDa from an initial value of 298.82 kDa. The degradation treatment altered the branched chain structure of the DBSPs, while the backbone structure, triple-helix structure, and crystallinity remained. DBSPs with a lower molecular weight exhibit better in vitro antioxidant activity. DBSPs did not show cytotoxicity to HSF cells but inhibited B16F10 cell proliferation. The addition of DBSPs protected HSF and B16F10 cells from oxidative stress and reduced ROS levels, B16F10 melanin content, and B16F10 tyrosinase activity after UVB damage, but DBSP-10 particles were slightly less effective due to aggregation. In contrast, DBSP-5 demonstrated effectiveness in reducing MDA levels in cells stressed by oxidative stress, increased total antioxidant capacity, and inhibited melanogenesis in B16F10, suggesting that DBSP-5 has potential as a topical therapeutic agent for the treatment of skin diseases associated with oxidative stress.

Effects of HOX family regulator-mediated modification patterns and immunity characteristics on tumor-associated cell type in endometrial cancer.

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

Impact of microwave ablation on survival rates and recurrence in hepatic malignant tumors.

PURPOSE: This study aimed to evaluate the efficacy of percutaneous microwave ablation (MWA) for treating hepatic malignant tumors and to identify factors influencing tumor recurrence post-treatment. METHODS: A total of 249 patients with hepatic malignant tumors treated at the Shandong Cancer Hospital and Institute were included, and 101 patients were analyzed. Disease-free and overall survival rates were assessed at 1, 2, and 3 years post-MWA. Correlations between tumor recurrence and factors such as Child-Pugh B classification and lesion count were examined, and a meta-analysis was conducted to identify independent risk factors for recurrence. RESULTS: The study found disease-free survival rates of 80.2%, 72.3%, and 70.3% at 1, 2, and 3 years post-MWA, with overall survival rates at 99%, 97%, and 96%. Significant correlations were observed between tumor recurrence, Child-Pugh B classification, and the number of lesions. Meta-analysis confirmed lesion count and Child-Pugh B classification as independent risk factors for recurrence following MWA treatment. CONCLUSION: The study underscores the importance of considering Child-Pugh B classification and lesion count in predicting tumor recurrence after MWA for hepatic malignant tumors. These findings offer valuable insights for clinicians in decision-making and post-treatment monitoring.

Bacterial membrane vesicles from swine farm microbial communities harboring and safeguarding diverse functional genes promoting horizontal gene transfer.

Antibiotic resistance (AMR) poses a significant global health challenge, with swine farms recognized as major reservoirs of antibiotic resistance genes (ARGs). Recently, bacterial membrane vesicles (BMVs) have emerged as novel carriers mediating horizontal gene transfer. However, little is known about the ARGs carried by BMVs in swine farm environments and their transfer potential. This study investigated the distribution, sources, and microbiological origins of BMVs in three key microbial habitats of swine farms (feces, soil, and fecal wastewater), along with the ARGs and mobile genetic elements (MGEs) they harbor. Characterization of BMVs revealed particle sizes ranging from 20 to 500 nm and concentrations from 108 to 1012 particles/g, containing DNA and proteins. Metagenomic sequencing identified BMVs predominantly composed of members of the Proteobacteria phyla, including Pseudomonadaceae, Moraxellaceae, and Enterobacteriaceae, carrying diverse functional genes encompassing resistance to 14 common antibiotics and 74,340 virulence genes. Notably, multidrug resistance, tetracycline, and chloramphenicol resistance genes were particularly abundant. Furthermore, BMVs harbored various MGEs, primarily plasmids, and demonstrated the ability to protect their DNA cargo from degradation and facilitate horizontal gene transfer, including the transmission of resistance genes. In conclusion, this study reveals widespread presence of BMVs carrying ARGs and potential virulence genes in swine farm feces, soil, and fecal wastewater. These findings not only provide new insights into the role of extracellular DNA in the environment but also highlight concerns regarding the gene transfer potential mediated by BMVs and associated health risks.

Microscale Lateral Perovskite Light Emitting Diode Realized by Self-Doping Phenomenon.

High-definition near-eye display technology has extremely close sight distance, placing a higher demand on the size, performance, and array of light-emitting pixel devices. Based on the excellent photoelectric performance of metal halide perovskite materials, perovskite light-emitting diodes (PeLEDs) have high photoelectric conversion efficiency, adjustable emission spectra, and excellent charge transfer characteristics, demonstrating great prospects as next-generation light sources. Despite their potential, the solubility of perovskite in photoresist presents a hurdle for conventional micro/nano processing techniques, resulting in device sizes typically exceeding 50 µm. This limitation impedes the further downsizing of perovskite-based components. Herein, we propose a plane-structured PeLED device that can achieve microscale light-emitting diodes with a single pixel device size < 2 µm and a luminescence lifetime of approximately 3 s. This is accomplished by fabricating a patterned substrate and regulating ion distribution in the perovskite through self-doping effects to form a PN junction. This breakthrough overcomes the technical challenge of perovskite-photoresist incompatibility, which has hindered the development of perovskite materials in micro/nano optoelectronic devices. The strides made in this study open up promising avenues for the advancement of PeLEDs within the realm of micro/nano optoelectronic devices.

Efficacy and Safety Analysis of Transarterial Chemoembolization Plus Donafenib With or Without Immune Checkpoint Inhibitors for Unresectable Hepatocellular Carcinoma: A Prospective, Single-Arm, Single-Center, Phase II Clinical Study.

Purpose: To observe and assess the efficacy and safety of donafenib combined with transarterial chemoembolization (TACE) to treat unresectable hepatocellular carcinoma (HCC). Patients and Methods: This prospective, single-arm, single-center, phase II clinical study enrolled 36 patients with initial unresectable HCC who had not undergone any systemic treatment. The patients received donafenib plus TACE (n = 26) or donafenib plus TACE plus programmed death receptor 1 inhibitors (n = 10). The primary endpoint was short-term efficacy, with secondary endpoints including progression-free survival (PFS), time to response (TTR), disease control rate (DCR), and adverse events. The tumor feeding artery diameter was also measured. Results: Efficacy evaluation of all 36 patients revealed 6 cases of complete response, 19 of partial response, 8 of stable disease, and 3 of progressive disease. Six (16.7%) patients successfully underwent conversion surgery, all achieving R0 resection, and 2 (5.6%) achieved a complete pathological response. The objective response rate (ORR) was 69.4% and the DCR was 91.7%. The median PFS was 10.7 months, the median overall survival was not reached, and the median TTR was 1.4 months. The median survival rates at 6, 12, and 18 months were 85.0%, 77.6%, and 71.3%, respectively. The median PFS rates at 6, 12, and 18 months were 65.3%, 45.6%, and 34.2%, respectively. Treatment-related adverse events (TRAEs) occurred in all 25 subjects, including 4 (11.3%) grade 3 TRAEs. No grade 4 or 5 TRAEs occurred. The tumor feeding artery diameter was significantly decreased following treatment (P = 0.036). Multivariable analysis revealed the sum of baseline target lesion diameters, best tumor response, and combined immunotherapy as independent predictors of PFS. Conclusion: TACE plus donafenib reduced the tumor feeding artery diameter in patients with unresectable HCC. The safety profile was good, and a high ORR was achieved.

Inappropriate use of antibiotic enhances antibiotic resistance dissemination in ESBL-EC: Role of ydcz in outer membrane vesicles biogenesis and protein transport.

Extended-spectrumß-lactam producing Escherichia coli (ESBL-EC) readily colonizes live poultry and serves as a major source of contamination in retail chicken meat, posing significant threats to public health. This study aims to investigate the impact of inappropriate antibiotic use on the dissemination and exacerbation of antibiotic resistance in ESBL-EC and explore the underlying molecular mechanisms. Through experimental analysis, we propose a hypothesis that inappropriate antibiotic use may exacerbate resistance by affecting vesicle formation and protein secretion. Experimental results demonstrate that under the influence of amoxicillin, the concentration of proteins secreted in outer membrane vehicles (OMVs) by ESBL-EC significantly increases, along with a significant upregulation in the expression of the CTX-M-55-type Extended-spectrum beta-lactamase (CTX-M-55). Proteomic analysis and differential gene knockout experiments identified the key protein YdcZ, associated with OMVs formation and protein transportation in ESBL-EC under amoxicillin treatment. Further investigations reveal direct interactions between YdcZ and other proteins (YdiH and BssR). Upon ydcz gene knockout, a significant decrease in protein concentration within OMVs is observed, accompanied by a noticeable reduction in protection against sensitive bacteria. These findings suggest a critical role of YdcZ in regulating the process of protein transportation to OMVs in ESBL-EC under the influence of amoxicillin. In summary, our research uncovers the significant role of inappropriate antibiotic use in promoting the secretion of OMVs by ESBL-EC, aiding the survival of antibiotic-sensitive bacteria in the vicinity of infection sites. These findings provide new insights into the mechanisms underlying antibiotic-induced bacterial resistance dissemination and offer novel avenues for exploring prevention and control strategies against bacterial resistance propagation.

Development and validation of matrix metalloproteinase for predicting prognosis and tumour microenvironment immune profiles in uterine corpus endometrial carcinoma.

Background: Matrix metalloproteinases (MMPs) are involved in many processes of tumour progression and invasion. However, few studies have analysed the effects of MMP expression patterns on endometrial cancer (EC) development from the perspective of the tumour microenvironment (TME). we quantified MMP expression in individual by constructing an MMP score and found MMP score effectively predict the prognosis of EC patients. Methods: MMPs expression profiles were determined based on the differential expression of 12 MMP-related regulators. Principal component analysis (PCA) was used to construct an MMP scoring system which can quantify the MMPs expression patterns individually of EC patients. Kaplan-Meier analysis, the log-rank test, and time-dependent receiver operating characteristic (ROC) curve analysis were used to evaluate the value of MMPs expression in predicting prognosis. Single-cell RNA sequencing (scRNA-seq) dataset was used to verify correlation between MMPs and progression of EC. Gene Ontology (GO) analysis was used to investigate the pathways and functions underlying MMPs expression. Tumour immune dysfunction, exclusion prediction, and pharmacotherapy response analyses were performed to assess the potential response to pharmacotherapy based on MMPs patterns. Results: We downloaded the MMPs expression data, somatic mutation data and corresponding clinical information of EC patients from the TCGA website and ICGC portal. Based on the MMP-related differentially expressed genes (DEGs), the MMP score was constructed, and EC patients were divided into high and low MMP score groups. There was a positive correlation between MMP score and prognosis of EC patients. Patients with high MMP scores had better prognosis, more abundant immune cell infiltration and stronger antitumoor immunity. Although prognosis is worse with the lower group than the high, patients with low MMP score had better response to immunotherapy, which means they could prolong the survival time through Immunological checkpoint blockade (ICB) therapy. scRNA-seq analysis identified significant heterogeneity between MMP score and classical pathways in EC. Conclusion: Our work indicates that the MMP score could be a potential tool to evaluate MMP expression patterns, immune cell infiltration, response to pharmacotherapy, clinicopathological features, and survival outcomes in EC. This will provide the more effective guide to select immunotherapeutic strategies of EC in the future.

Leveraging existing 16S rRNA gene surveys to decipher microbial signatures and dysbiosis in cervical carcinogenesis.

The presence of dysbiotic cervicovaginal microbiota has been observed to be linked to the persistent development of cervical carcinogenesis mediated by the human papillomavirus (HPV). Nevertheless, the characteristics of the cervical microbiome in individuals diagnosed with cervical cancer (CC) are still not well understood. Comprehensive analysis was conducted by re-analyzing the cervical 16S rRNA sequencing datasets of a total of 507 samples from six previously published studies. We observed significant alpha and beta diversity differences in between CC, cervical intraepithelial neoplasia (CIN) and normal controls (NC), but not between HPV and NC in the combined dataset. Meta-analysis revealed that opportunistic pernicious microbes Streptococcus, Fusobacterium, Pseudomonas and Anaerococcus were enriched in CC, while Lactobacillus was depleted compared to NC. Members of Gardnerella, Sneathia, Pseudomonas, and Fannyhessea have significantly increased relative abundance compared to other bacteria in the CIN group. Five newly identified bacterial genera were found to differentiate CC from NC, with an area under the curve (AUC) of 0.8947. Moreover, co-occurrence network analysis showed that the most commonly encountered Lactobacillus was strongly negatively correlated with Prevotella. Overall, our study identified a set of potential biomarkers for CC from samples across different geographic regions. Our meta-analysis provided significant insights into the characteristics of dysbiotic cervicovaginal microbiota undergoing CC, which may lead to the development of noninvasive CC diagnostic tools and therapeutic interventions.

Freeprotmap: waiting-free prediction method for protein distance map.

BACKGROUND: Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed. RESULT: In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue-residue distances in tens of milliseconds and has higher precision than the best structure prediction method. CONCLUSION: Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue-residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.