Two-Step Perovskite Solar Cells with > 25% Efficiency: Unveiling the Hidden Bottom Surface of Perovskite Layer.

While significant efforts in surface engineering have been devoted to the conversion process of lead iodide (PbI2) into perovskite and top surface engineering of perovskite layer with remarkable progress, the exploration of residual PbI2 clusters and the hidden bottom surface on perovskite layer have been limited. In this work, a new strategy involving 1-butyl-3-methylimidazolium acetate (BMIMAc) ionic liquid (IL) additives is developed and it is found that both the cations and the anions in ILs can interact with the perovskite components, thereby regulating the crystallization process and diminishing the residue PbI2 clusters as well as filling vacancies. The introduction of BMIMAc ILs induces the formation of a uniform porous PbI2 film, facilitating better penetration of the second-step organic salt and fostering a more extensive interaction between PbI2 and the organic salt. Surprisingly, the oversized residual PbI2 clusters at the bottom surface of the perovskite layer completely diminish. In addition, advanced depth analysis techniques including depth-resolved grazing-incidence wide-angle X-ray scattering (GIWAXS) and bottom thinning technology are employed for a comprehensive understanding of the reduction in residual PbI2. Leveraging effective PbI2 management and regulation of the perovskite crystallization process, the champion devices achieve a power conversion efficiency (PCE) of 25.06% with long-term stability.

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promising therapeutic modality, demonstrating remarkable potential for substantially prolonging the overall survival of individuals afflicted with LUAD. However, there is currently a lack of reliable signatures for identifying patients who would benefit from immunotherapy. We conducted a comparative analysis of two immunotherapy cohorts (OAK and POPLAR) and utilized single-factor COX regression to identify genes that significantly impact the prognosis of LUAD. Based on the TCGA-LUAD dataset, we employed a combination of 101 machine learning algorithms to construct a model and selected the optimal model. The model was validated on five GEO datasets and compared with 144 previously published signatures to assess its performance. Subsequently, we explored the underlying biological mechanisms through tumor mutation burden analysis, enrichment analysis, and immune infiltration analysis. An immunotherapy prognostic prediction signature (IPPS) was constructed based on 13 genes, showing robust performance in the TCGA-LUAD dataset. IPPS exhibited consistent predictive accuracy in the validation cohorts. Compared to 144 previously published signatures, IPPS consistently ranked among the top in terms of C-index values. Further exploration revealed differences between high and low-IPPS groups in terms of tumor mutation burden, pathway enrichment, and immune infiltration. IPPS demonstrates strong predictive capabilities for the prognosis of LUAD patients, offering the potential to identify suitable candidates for immunotherapy and contribute to precision treatment strategies for LUAD.

Facile Tailoring of Metal-Organic Frameworks for Förster Resonance Energy Transfer-Driven Enhancement in Perovskite Photovoltaics.

Förster resonance energy transfer (FRET) has demonstrated its potential to enhance the light energy utilization ratio of perovskite solar cells by interacting with metal-organic frameworks (MOFs) and perovskite layers. However, comprehensive investigations into how MOF design and synthesis impact FRET in perovskite systems are scarce. In this work, nanoscale HIAM-type Zr-MOF (HIAM-4023, HIAM-4024, and HIAM-4025) is meticulously tailored to evaluate FRET's existence and its influence on the perovskite photoactive layer. Through precise adjustments of amino groups and acceptor units in the organic linker, HIAM-MOFs are synthesized with the same topology, but distinct photoluminescence (PL) emission properties. Significant FRET is observed between HIAM-4023/HIAM-4024 and the perovskite, confirmed by spectral overlap, fluorescence lifetime decay, and calculated distances between HIAM-4023/HIAM-4024 and the perovskite. Conversely, the spectral overlap between the PL emission of HIAM-4025 and the perovskite's absorption spectrum is relatively minimal, impeding the energy transfer from HIAM-4025 to the perovskite. Therefore, the HIAM-4023/HIAM-4024-assisted perovskite devices exhibit enhanced EQE via FRET processes, whereas the HIAM-4025 demonstrates comparable EQE to the pristine. Ultimately, the HIAM-4023-assisted perovskite device achieves an enhanced power conversion efficiency (PCE) of 24.22% compared with pristine devices (PCE of 22.06%) and remarkable long-term stability under ambient conditions and continuous light illumination.

Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears.

OBJECTIVE: To compare the diagnostic power among various machine learning algorithms utilizing multi-sequence magnetic resonance imaging (MRI) radiomics in detecting anterior cruciate ligament (ACL) tears. Additionally, this research aimed to create and validate the optimal diagnostic model. METHODS: In this retrospective analysis, 526 patients were included, comprising 178 individuals with ACL tears and 348 with a normal ACL. Radiomics features were derived from multi-sequence MRI scans, encompassing T1-weighted imaging and proton density (PD)-weighted imaging. The process of selecting the most reliable radiomics features involved using interclass correlation coefficient (ICC) testing, t tests, and the least absolute shrinkage and selection operator (LASSO) technique. After the feature selection process, five machine learning classifiers were created. These classifiers comprised logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). A thorough performance evaluation was carried out, utilizing diverse metrics like the area under the receiver operating characteristic curve (ROC), specificity, accuracy, sensitivity positive predictive value, and negative predictive value. The classifier exhibiting the best performance was chosen. Subsequently, three models were developed: the PD model, the T1 model, and the combined model, all based on the optimal classifier. The diagnostic performance of these models was assessed by employing AUC values, calibration curves, and decision curve analysis. RESULTS: Out of 2032 features, 48 features were selected. The SVM-based multi-sequence radiomics outperformed all others, achieving AUC values of 0.973 and 0.927, sensitivities of 0.933 and 0.857, and specificities of 0.930 and 0.829, in the training and validation cohorts, respectively. CONCLUSION: The multi-sequence MRI radiomics model, which is based on machine learning, exhibits exceptional performance in diagnosing ACL tears. It provides valuable insights crucial for the diagnosis and treatment of knee joint injuries, serving as an accurate and objective supplementary diagnostic tool for clinical practitioners.

Growth Factors-Loaded Temperature-Sensitive Hydrogel as Biomimetic Mucus Attenuated Murine Ulcerative Colitis via Repairing the Mucosal Barriers.

The pathogenesis of ulcerative colitis (UC) is associated with the shedding of the gut mucus. Herein, inspired by the biological functions of mucus, growth factors-loaded in situ hydrogel (PHE-EK) was designed for UC treatment by integrating dihydrocaffeic acid-modified poloxamer as a thermosensitive material with hyaluronic acid (colitis-specific adhesive), epigallocatechin-3-gallate (antibacterial agent), and bioactive factors (KPV tripeptide and epidermal growth factor). PHE-EK presented good thermosensitive properties, as a flowable liquid at room temperature and gelled within 10 s when exposed to body temperature. PHE-EK hydrogel presented good mechanical strength with a strain of 77.8%. Moreover, PHE-EK hydrogel displayed antibacterial activity against Escherichia coli. Importantly, in vitro and in vivo adhesive tests showed that the PHE-EK hydrogel could specifically adhere to the inflamed colon via electrostatic interaction. When PHE-EK as a biomimetic mucus was rectally administrated to colitis rats, it effectively hindered the body weight loss, reduced the disease activity index and improved the colonic shorting. Moreover, the expression of pro-inflammatory cytokines (e.g., IL-1ß, IL-6, and TNF-α) at the laminae propria or epitheliums of the colon for colitis rats was substantially inhibited by PHE-EK. Besides, the colonic epitheliums were well rearranged, and the tight junction proteins (Zonula-1 and Claudin-5) between them were greatly upregulated after PHE-EK treatment. Collectively, PHE-EK might be a promising therapy for UC.

Mechanism of deltamethrin biodegradation by Brevibacillus parabrevis BCP-09 with proteomic methods.

Ester-containing deltamethrin pesticides are widely used in farmland and have inevitable side effects on the biosphere and human health. Microbia have been used for efficient degradation of deltamethrin, but the related mechanism and enzyme characteristics have not been elucidated. In this study, a species Brevibacillus parabrevis BCP-09 could degrade up to 75 mg L-1 deltamethrin with a degradation efficiency of 95.41%. Proteomic and genomic methods were used to explore its degradation mechanism. Enzymes belonged to hydrolases, oxidases and aromatic compound degrading enzymes were expressed enhanced and might participate in the deltamethrin degradtion. RT-PCR experiment and enzyme activity analysis verified the degradation of deltamethrin by bacterial protein. Additionally, the formation of endospores can help strain BCP-09 resist the toxicity of deltamethrin and enhance its degradation. This study supplies a scientific evidence for the application of Brevibacillus parabrevis BCP-09 in the bioremediation of environmental pollution and enriches the resources of deltamethrin-biodegradable proteins.

Thiol-Functionalized Conjugated Metal-Organic Frameworks for Stable and Efficient Perovskite Photovoltaics.

Metal-organic frameworks (MOFs) have been investigated recently in perovskite photovoltaics owing to their potential to boost optoelectronic performance and device stability. However, the impact of variations in the MOF side chain on perovskite characteristics and the mechanism of MOF/perovskite film formation remains unclear. In this study, three nanoscale thiol-functionalized UiO-66-type Zr-based MOFs (UiO-66-(SH)2 , UiO-66-MSA, and UiO-66-DMSA) are systematically employed and examined in perovskite solar cells (PSCs). Among these MOFs, UiO-66-(SH)2 , with its rigid organic ligands, exhibited a strong interaction with perovskite materials with more efficient suppression of perovskite vacancy defects. More importantly, A detailed and in-depth discussion is provided on the formation mechanism of UiO-66-(SH)2 -assisted perovskite film upon in situ GIWAXS performed during the annealing process. The incorporation of UiO-66-(SH)2 additives substantially facilitates the conversion of PbI2 into the perovskite phase, prolongs the duration of stage I, and induces a delayed phase transformation pathway. Consequently, the UiO-66-(SH)2 -assisted device demonstrates reduced defect density and superior optoelectronic properties with optimized power conversion efficiency of 24.09% and enhanced long-term stability under ambient environment and continuous light illumination conditions. This study acts as a helpful design guide for desired MOF/perovskite structures, enabling further advancements in MOF/perovskite optoelectronic devices.

Revealing Size-Dependency of Ionic Liquid to Assist Perovskite Film Formation Mechanism for Efficient and Durable Perovskite Solar Cells.

Ionic liquids (ILs) are extensively utilized for the manipulation of crystallization kinetics of perovskite, morphology optimization, and defect passivation for the fabrication of highly efficient and stable devices. However, comparing ILs with different chemical structures and selecting the appropriate ILs from the many types available to enhance perovskite device performance remains a challenge. In this study, a range of ILs containing different sizes of anions are introduced as additives for assisting in film formation in perovskite photovoltaics. Specifically, ILs with various sizes significantly affects the strength of chemical interaction between ILs and perovskite composition, inducing varying degrees of conversion of lead iodide to perovskite as well as the formation of perovskite films with markedly disparate grain sizes and morphology. Theoretical calculations in conjunction with experimental measurements revealed that small-sized anion can more effectively reduce defect density by filling halide vacancies within perovskite bulk materials, resulting in suppression of charge-carrier recombination, an extended photoluminescence lifetime, and significantly improved device performance. Boosted by ILs with appropriate size, the champion power conversion efficiency of 24.09% for the ILs-treated device is obtained, and the unencapsulated devices retain 89.3% of its original efficiency under ambient conditions for 2000 h.

UNC93B1 facilitates the localization and signaling of TLR5M in Epinephelus coioides.

Toll-like receptor 5 (TLR5), serving as a sensor of bacterial flagellin, mediates the innate immune response to actively engage in the host's immune processes against pathogen invasion. However, the mechanism underlying TLR5-mediated immune response in fish remains unclear. Despite the presumed cell surface expression of TLR5 member form (TLR5M), its trafficking dynamics remain elusive. Here, we have identified Epinephelus coioides TLR5M as a crucial mediator of Vibrio flagellin-induced cytokine expression in grouper cells. EcTLR5M facilitated the activation of NF-κB signaling pathway in response to flagellin stimulation and exerted a modest influence on the mitogen-activated protein kinase (MAPK)-extracellular regulated kinase (ERK) signaling. The trafficking chaperone Unc-93 homolog B1 (EcUNC93B1) participated in EcTLR5M-mediated NF-κB signaling activation and downstream cytokine expression. In addition, EcUNC93B1 combined with EcTLR5M to mediate its exit from the endoplasmic reticulum, and also affected its post-translational maturation. Collectively, these findings first discovered that EcTLR5M mediated the flagellin-induced cytokine expression primarily by regulating the NF-κB signaling pathway, and EcUNC93B1 mediated EcTLR5M function through regulating its trafficking and post-translational maturation. This research expanded the understanding of fish innate immunity and provided a novel concept for the advancement of anti-vibrio immunity technology.

Tailoring Ionic Liquid Chemical Structure for Enhanced Interfacial Engineering in Two-Step Perovskite Photovoltaics.

Ionic liquids (ILs) have emerged as versatile tools for interfacial engineering in perovskite photovoltaics. Their multifaceted application targets defect mitigation at SnO2 -perovskite interfaces, finely tuning energy level alignment, and enhancing charge transport, meanwhile suppressing non-radiative recombination. However, the diverse chemical structures of ILs present challenges in selecting suitable candidates for effective interfacial modification. This study adopted a systematic approach, manipulating IL chemical structures. Three ILs with distinct anions are introduced to modify perovskite/SnO2 interfaces to elevate the photovoltaic capabilities of perovskite devices. Specifically, ILs with different anions exhibited varied chemical interactions, leading to notable passivation effects, as confirmed by Density Functional Theory (DFT) calculation. A detailed analysis is also conducted on the relationship between the ILs' structure and regulation of energy level arrangement, work function, perovskite crystallization, interface stress, charge transfer, and device performance. By optimizing IL chemical structures and exploiting their multifunctional interface modification properties, the champion device achieved a PCE of 24.52% with attentional long-term stability. The study establishes a holistic link between IL structures and device performance, thereby promoting wider application of ILs in perovskite-based technologies.

Alignment strategy for different types of varus knee with generic instruments: Mechanical alignment or kinematic alignment?

OBJECTIVE: A thorough examination of the available approaches is crucial to comprehensively understand the variance among the alignment strategies employed in total knee arthroplasty (TKA). In this study, we assessed the functional outcomes during the perioperative and postoperative periods of TKA in patients using generic instruments with varus knee to compare the mechanical alignment (MA) and kinematic alignment (KA) procedures. METHODS: A total of 127 patients from the First Affiliated Hospital of Wannan Medical College who had undergone unilateral TKA between November 2019 and April 2021 were included. The patients with varus knee deformity were categorized into two groups [type I (n = 64) and type IV (n = 63)] based on the modified coronal plane alignment of the knee (mCPAK) classification. The type I and IV groups were further subdivided into MA (n = 30 and n = 32) and KA subgroups (n = 34 and n = 21), respectively. The clinical information collected included sex, surgical side, age, body mass index, and perioperative data [including operation time, intraoperative blood loss, length of hospital stay, and the American Society of Anesthesiologists (ASA) classification]. All patients were monitored for 12 months post-surgery to evaluate the recovery of knee joint function. During this period, the Knee Disability and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and the active range of motion (AROM) and visual analog scale (VAS) pain scores were compared at different time points, i.e., before the operation and 6 weeks, 6 months, and 12 months post-operation. Additionally, the patients' subjective experiences were assessed at 6 and 12 months post-surgery using Forgotten Joint Score Knee (FJS-12 Knee), while complications were recorded throughout the monitoring period. RESULTS: No significant variances were observed in ASA classification, operation duration, blood loss volume during surgery, and hospital stay length between the patients who underwent KA TKA and those who received MA TKA (P > 0.05). During the initial 6 weeks post-operation, the KA group exhibited a significantly reduced average VAS pain score (P < 0.05), with no such differences at 6 months and 1 year after the surgery (P > 0.05). Furthermore, the KA group had significantly higher scores on the KOOS JR at 6 weeks, 6 months, and 1 year following the surgery (P < 0.05). Moreover, the AROM score of the KA group significantly improved only at 6 weeks after the surgery (P < 0.05); however, no prominent differences were found at 6 months and 1 year after the operation (P > 0.05). The KA cohort also exhibited a significant increase in FJS-12 Knee at 1 year following the operation (P < 0.05), whereas no such difference was detected at 6 months following the surgery (P > 0.05). Thus, compared to the MA method, the KA procedure provided pain relief and improved active motion range within 6 weeks after the surgery in patients undergoing TKA. Further, the KOOS JR exhibited significant increases at 6 weeks, 6 months, and 1 year while the FJS-12 Knee demonstrated a significant increase at 1 year after the KA TKA procedure. CONCLUSION: Therefore, our study results suggest that the KA approach can be considered in patients using generic instruments with varus alignment of the knee, particularly those with mCPAK type I and IV varus knees, to help improve patient satisfaction.

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy.

BACKGROUND: Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer. METHODS: Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape. RESULTS: The constructed signature reveals a pronounced disparity in prognosis between the high- and low-risk groups of LUAD patients. The statistical discrepancies in survival times among LUAD patients from both the TCGA and GEO databases further corroborate this observation. Additionally, heightened levels of immune cell infiltration expression are evidenced in the low-risk group, suggesting a potential benefit from immunotherapeutic interventions for these patients. The expression levels of pivotal risk-associated genes in tissue samples were assessed utilizing qRT-PCR, thereby unveiling PITX3 as a plausible therapeutic target in the context of LUAD. CONCLUSIONS: Our constructed ICD-related signatures provide help in predicting the prognosis and immunotherapy of LUAD patients, and to some extent guide the clinical treatment of LUAD patients.

Deciphering Treg cell roles in esophageal squamous cell carcinoma: a comprehensive prognostic and immunotherapeutic analysis.

Background: Esophageal squamous cell carcinoma (ESCC) is a prevalent and aggressive form of cancer that poses significant challenges in terms of prognosis and treatment. Regulatory T cells (Treg cells) have gained attention due to their influential role in immune modulation within the tumor microenvironment (TME). Understanding the intricate interactions between Treg cells and the tumor microenvironment is essential for unraveling the mechanisms underlying ESCC progression and for developing effective prognostic models and immunotherapeutic strategies. Methods: A combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq analysis was utilized to explore the role of Treg cells within the TME of ESCC. The accuracy and applicability of the prognostic model were assessed through multi-dimensional evaluations, encompassing an examination of the model's performance across various dimensions, such as the mutation landscape, clinical relevance, enrichment analysis, and potential implications for immunotherapy strategies. Results: The pivotal role of the macrophage migration inhibitory factor (MIF) signaling pathway within the ESCC TME was investigated, with a focus on its impact on Treg cells and other subpopulations. Through comprehensive integration of bulk sequencing data, a Treg-associated signature (TAS) was constructed, revealing that ESCC patients with elevated TAS (referred to as high-TAS individuals) experienced significantly improved prognoses. Heightened immune infiltration and increased expression of immune checkpoint markers were observed in high-TAS specimens. The model's validity was established through the IMvigor210 dataset, demonstrating its robustness in predicting prognosis and responsiveness to immunotherapy. Heightened therapeutic benefits were observed in immune-based interventions for high-TAS ESCC patients. Noteworthy differences in pathway enrichment patterns emerged between high and low-TAS cohorts, highlighting potential avenues for therapeutic exploration. Furthermore, the clinical relevance of key model genes was substantiated by analyzing clinical samples from ten paired tumor and adjacent tissues, revealing differential expression levels. Conclusion: The study established a TAS that enables accurate prediction of patient prognosis and responsiveness to immunotherapy. This achievement holds significant implications for the clinical management of ESCC, offering valuable insights for informed therapeutic interventions.

A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer.

BACKGROUND: The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC). METHODS: Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature. RESULTS: Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy. CONCLUSION: The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.

Upregulation of HDAC9 in hippocampal neurons mediates depression-like behaviours by inhibiting ANXA2 degradation.

Major depressive disorder (MDD) is a pervasive and devastating mental disease. Broad spectrum histone deacetylase (HDAC) inhibitors are considered to have potential for the treatment of depressive phenotype in mice. However, due to its non-specific inhibition, it has extensive side effects and can not be used in clinical treatment of MDD. Therefore, finding specific HDAC subtypes that play a major role in the etiology of MDD is the key to develop corresponding specific inhibitors as antidepressants in the future. Copy number variation in HDAC9 gene is thought to be associated with the etiology of some psychiatric disorders. Herein, we found that HDAC9 was highly expressed in the hippocampus of chronic restraint stress (CRS) mouse model of depression. Upregulation of HDAC9 expression in hippocampal neurons of mice induced depression-like phenotypes, including anhedonia, helplessness, decreased dendritic spine density, and neuronal hypoexcitability. Moreover, knockdown or knockout of HDAC9 in hippocampal neurons alleviated depression-like phenotypes caused by chronic restraint stress (CRS) in WT mice. Importantly, using immunoprecipitation-mass spectrometry (IP-MS), we further found that Annexin A2 (ANXA2) was coupled to and deacetylated by HDAC9. This coupling resulted in the inhibition of ubiquitinated ANXA2 degradation and then mediates depression-like behavior. Overall, we discovered a previously unrecognized role for HDAC9 in hippocampal neurons in the pathogenesis of depression, indicating that inhibition of HDAC9 might be a promising clinical strategy for the treatment of depressive disorders.

Genome-wide identification, evolution and expression of TGF-ß signaling pathway members in mandarin fish (Siniperca chuatsi).

Members of the transforming growth factor ß (TGF-ß) signaling pathway regulate diverse cellular biological processes in embryonic and tissue development. We took mandarin fish (Siniperca chuatsi) as the research object to identify all members of the TGF-ß signaling pathway, measure their expression pattern in the key period post hatching, and further explore their possible role in the process of sex regulation. Herein, we identified eighty-three TGF-ß signaling pathway members and located them on chromosomes based on the genome of mandarin fish. TGF-ß signaling pathway members were highly conserved since each TGF-ß subfamily clustered with orthologs from other species. Transcriptome analysis, qRT-PCR and in situ hybridization demonstrated that most mandarin fish TGF-ß signaling pathway members presented stage-specific and/or sex-dimorphic expression during gonadal development, and different members of the TGF-ß signaling pathway participated in different stages of gonadal development. Taken together, our results provide new insight into the role of TGF-ß signaling pathway members in the sex regulation of mandarin fish.

TLR5S negatively regulates the TLR5M-mediated NF-κB signaling pathway in Epinephelus coioides.

Nuclear factor kappa-B (NF-κB) pathway is a key mediator of inflammation response that plays a role in host defense for pathogen elimination, but excessive activation may lead to tissue damage or pathogen transmission. The negative regulation of NF-κB in lower vertebrates is largely unknown, hindering further understanding of immune signaling evolution. Here, we provided evidence that Epinephelus coioides soluble toll-like receptor 5 (TLR5S), a member of the TLR5 subfamily, has been newly identified as a negative regulator of NF-κB signaling. EcTLR5S was a cytoplasmic protein consisting of 17 leucine-rich repeat domains, which specifically responded to Vibrio flagellin and suppressed flagellin-induced NF-κB signaling activation and cytokine expression. The amino-terminal LRR 1-5 region was necessary for its negative regulatory function. Dual-luciferase reporter assay showed that EcTLR5S significantly inhibited the NF-κB-luc activity induced by inhibitor of NF-κB kinase α (IKKα) and IKKß. Subsequently, the functional relationship between EcTLR5M and EcTLR5S was analyzed, revealing that the negative regulatory function of EcTLR5S targeted the activation of the NF-κB pathway mediated by EcTLR5M. The above results reveal that EcTLR5S negatively regulates the flagellin-induced EcTLR5M-NF-κB pathway activation, which may prevent over-activation of immune signaling and restore homeostasis.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

Background: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). Methods: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. Results: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Conclusion: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer.

Background: Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients. Methods: The GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC. Results: Six CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC. Conclusion: The prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts.

Background: Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD. Methods: We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD. Results: We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments. Conclusions: The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.