Spontaneous Transition between Multiple Conductance States and Rectifying Behaviors in an Artificial Single-Molecule Funnel.

It has long been an aspirational goal to create artificial channel structures that replicate the feat achieved by ion channel proteins. Biological ion channels occasionally demonstrate multiple conductance states (known as subconductance), remaining a challenging property to achieve in artificial channel molecules. We report a funnel-shaped single-molecule channel constructed by an electron-deficient macrocycle and two electron-deficient aromatic imide arms. Planar lipid bilayer measurements reveal distinct current recordings, including a closed state, two conducting states, and spontaneous transitions between the three states, resembling the events seen in biological ion channels. The transitions result from conformational changes induced by chloride transport in the channel molecule. Both opening states show a non-linear and rectifying I-V relationship, indicating voltage-dependent transport due to the asymmetrical channel structure. This work could enhance our understanding of ion permeation and channel opening mechanism.

Effects of walking on epigenetic age acceleration: a Mendelian randomization study.

INTRODUCTION: Walking stands as the most prevalent physical activity in the daily lives of individuals and is closely associated with physical functioning and the aging process. Nonetheless, the precise cause-and-effect connection between walking and aging remains unexplored. The epigenetic clock emerges as the most promising biological indicator of aging, capable of mirroring the biological age of the human body and facilitating an investigation into the association between walking and aging. Our primary objective is to investigate the causal impact of walking with epigenetic age acceleration (EAA). METHODS: We conducted a two-sample two-way Mendelian randomization (MR) study to investigate the causal relationship between walking and EAA. Walking and Leisure sedentary behavior data were sourced from UK Biobank, while EAA data were gathered from a total of 28 cohorts. The MR analysis was carried out using several methods, including the inverse variance weighted (IVW), weighted median, MR-Egger, and robust adjusted profile score (RAPS). To ensure the robustness of our findings, we conducted sensitivity analyses, which involved the MR-Egger intercept test, Cochran's Q test, and MR-PRESSO, to account for and mitigate potential pleiotropy. RESULTS: The IVW MR results indicate a significant impact of usual walking pace on GrimAge (BETA = - 1.84, 95% CI (- 2.94, - 0.75)), PhenoAge (BETA = - 1.57, 95% CI (- 3.05, - 0.08)), Horvath (BETA = - 1.09 (- 2.14, - 0.04)), and Hannum (BETA = - 1.63, 95% CI (- 2.70, - 0.56)). Usual walking pace is significantly associated with a delay in epigenetic aging acceleration (EAA) (P < 0.05). Moreover, the direction of effect predicted by the gene remained consistent across RAPS outcomes and sensitivity MR analyses. There is a lack of robust causal relationships between other walking conditions, such as walking duration and walking frequency, on EAA (P > 0.05). CONCLUSION: Our evidence demonstrates that a higher usual walking pace is associated with a deceleration of the acceleration of all four classical epigenetic clocks acceleration.

Anion-Carbonyl Interactions.

Presented herein is the exploration of a novel non-covalent anion-carbonyl (X-···C═O) interaction using aromatic imides as receptors and halides as lone pair donors. Combined theoretical calculations and experimental methods including 13C NMR, IR, and crystallographic analyses were performed to provide the physical origin and experimental evidence of anion-carbonyl interactions. The EDA analysis (energy decomposition analysis) based on DFT calculation indicates that electrostatic terms are the dominant contributions for the binding energy while electron delocalization also significantly contributes alongside the electrostatic attraction. Orbital interaction (n → π*) involving the delocalization of halide lone pairs on the carbonyl antibonding orbitals was visualized with NBO (Natural Bond Orbital) analysis. 13C NMR and IR spectra demonstrated upfield chemical shifts and red-shift frequency of hosts upon the addition of halides, reflecting the effect of orbital overlap between the halide lone pairs and π* of carbonyl (n → π* contribution). The anion-carbonyl interactions were directly revealed by X-ray structural analysis of anion and benzene triimide complexes.

Relationships Among Serological Indicators and In Vitro High-Throughput Drug Sensitivity Screening Results in Patients with Hepatocellular Carcinoma.

AIM: The purpose of this study was to analyze the relationship between serum indicators and high-throughput drug screening (HDS) results, aiming to achieve specific therapy for hepatocellular carcinoma (HCC). METHODS: This study recruited patients with HCC who underwent surgical resection at the Hepatobiliary Surgery Center of the First Affiliated Hospital of Chongqing Medical University from December 2019 to December 2021. HCC tissues were obtained from patients during surgery and subjected to in vitro cell culture, and then HDS testing was performed on the cultured tissue samples. We used Spearman's correlation analysis to examine the relationships between drug sensitivity results for anti-hepatocellular carcinoma drugs, other antitumor drugs, and serological indicators, the Neutrophil Lymphocyte Ratio (NLR), Platelet Lymphocyte Ratio (PLR), Systemic Immune Inflammatory Index (SII), Systemic Inflammatory Response Index (SIRI), Prognostic Nutritional Index (PNI), and Lymphocyte Monocyte Ratio (LMR). A significant correlation was considered when P<0.05 and |r|>0.40. Furthermore, linear regression analysis was conducted to elucidate the relationship between serological indicators and drug susceptibility, with significant results indicated by P<0.05 and R²≥0.50. RESULTS: In this study, 82 patients with HCC who had undergone hepatectomy and completed in vitro cell culture and HDS testing were evaluated. Using Spearman's correlation with a significance threshold of P<0.05 and |r|>0.40, we identified significant associations between serological indicators and specific drug regimens: NLR correlated with 5-Fluorouracil, 5- Fluorouracil+Calcium folinate (FOLFOX4), and Capecitabine + Cisplatin (XP); PLR with FOLFOX4; SII with XP, FOLFOX4, Doxorubicin + Oxaliplatin (ADM+L-OHP); and SIRI with XP and FOLFOX4. No correlations were found between PNI or LMR and any drug inhibition rates. A comprehensive evaluation using linear regression analysis-which included variables such as sex, age, hepatitis B virus and liver cirrhosis status, size and number of lesions, alphafetoprotein, total bilirubin, albumin, alanine aminotransferase, aspartate aminotransferase, and prothrombin time, alongside NLR, PLR, SII, and SIRI was conducted in relation to drug regimens. This analysis revealed that NLR, SII, and SIRI are significant predictors of FOLFOX4 inhibition rate, while NLR predicts the inhibition rate of XP effectively. However, no significant links were established between molecular targeted drugs, other antitumor drugs, and serological indicators. CONCLUSIONS: NLR, SII, and SIRI were correlated with FOLFOX4, and the higher the values of NLR, SII, and SIRI, the higher the in vitro inhibition of FOLFOX. Also, NLR was correlated with XP, and the higher the value of NLR, the higher the in vitro inhibition of XP.

Targeting metabolism to enhance immunotherapy within tumor microenvironment.

Cancer metabolic reprogramming has been considered an emerging hallmark in tumorigenesis and the antitumor immune response. Like cancer cells, immune cells within the tumor microenvironment or premetastatic niche also undergo extensive metabolic reprogramming, which profoundly impacts anti-tumor immune responses. Numerous evidence has illuminated that immunosuppressive TME and the metabolites released by tumor cells, including lactic acid, Prostaglandin E2 (PGE2), fatty acids (FAs), cholesterol, D-2-Hydroxyglutaric acid (2-HG), adenosine (ADO), and kynurenine (KYN) can contribute to CD8+ T cell dysfunction. Dynamic alterations of these metabolites between tumor cells and immune cells can similarly initiate metabolic competition in the TME, leading to nutrient deprivation and subsequent microenvironmental acidosis, which impedes immune response. This review summarizes the new landscape beyond the classical metabolic pathways in tumor cells, highlighting the pivotal role of metabolic disturbance in the immunosuppressive microenvironment, especially how nutrient deprivation in TME leads to metabolic reprogramming of CD8+ T cells. Likewise, it emphasizes the current therapeutic targets or strategies related to tumor metabolism and immune response, providing therapeutic benefits for tumor immunotherapy and drug development in the future. Cancer metabolic reprogramming has been considered an emerging hallmark in tumorigenesis and the antitumor immune response. Dynamic alterations of metabolites between tumor cells and immune cells initiate metabolic competition in the TME, leading to nutrient deprivation and subsequent microenvironmental acidosis, which impedes immune response.

Comprehensive pan-cancer analysis: essential role of ABCB family genes in cancer.

Background: The adenosine triphosphate-binding-cassette (ABC) transporter orchestrates the transmembrane transport of diverse substrates with the aid of ATP as an energy source. ABC transporter constitutes a widespread superfamily of transporters prominently present on the cellular membrane of organisms. Advancements in understanding have unveiled additional roles beyond mere intracellular or extracellular transport functions for the ABC protein family, encompassing involvement in DNA repair, protein translation, and gene expression regulation. Yet its role in tumors is still unknown. Methods: This study drew support from multiple databases, including Gene Expression Omnibus (GEO), European Genome-phenome Archive (EGA), The Cancer Genome Atlas (TCGA), and employed multidimensional bioinformatics analyses, incorporating online databases and the R-project. Through a comprehensive analysis, we seek to discern transcriptional-level disparities among genes and their consequential impacts on prognosis, tumor microenvironment (TME), stemness score, immune subtypes, clinical characteristics, and drug sensitivity across human cancers. Results: ABC transporter subfamily B (ABCB) family genes exhibited heightened expression across diverse tumors, demonstrating a significant correlation with overall prognosis in pan-cancer contexts. Notably, gene expression levels manifested substantial associations with TME, stemness score, immune subtypes, clinical characteristics, and drug sensitivity in specific cancers, including kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD). Within this subset, transporter associated with antigen processing 1 (TAP1), TAP2, and ABCB6 emerged as noteworthy oncogenes. Conclusions: The outcomes of this study contribute to a comprehensive understanding of the implications of ABCB family genes in tumor progression, offering insights into potential therapeutic targets for cancer. Notably, the identification of ABCB6 as a significant oncogene suggests promising avenues for targeted therapies in KIRP, LIHC, and PAAD.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS: The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION: The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Dbl family RhoGEFs in cancer: different roles and targeting strategies.

Small Ras homologous guanosine triphosphatase (Rho GTPase) family proteins are highly associated with tumorigenesis and development. As intrinsic exchange activity regulators of Rho GTPases, Rho guanine nucleotide exchange factors (RhoGEFs) have been demonstrated to be closely involved in tumor development and received increasing attention. They mainly contain two families: the diffuse B-cell lymphoma (Dbl) family and the dedicator of cytokinesis (Dock) family. More and more emphasis has been paid to the Dbl family members for their abnormally high expression in various cancers and their correlation to poor prognosis. In this review, the common and distinctive structures of Dbl family members are discussed, and their roles in cancer are summarized with a focus on Ect2, Tiam1/2, P-Rex1/2, Vav1/2/3, Trio, KALRN, and LARG. Significantly, the strategies targeting Dbl family RhoGEFs are highlighted as novel therapeutic opportunities for cancer.

Benefits of Hypothermic Oxygenated Perfusion Versus Static Cold Storage in Liver Transplant: A Comprehensive Systematic Review and Meta-analysis.

Background: The magnitude of potential benefits that hypothermic oxygenated perfusion (HOPE) may provide for liver transplantation (LT) patients compared to static cold storage (SCS) remains uncertain. In this systematic review and meta-analysis, we aimed to investigate the therapeutic effect that HOPE can offer LT recipients relative to SCS by synthesizing available evidence. Methods: A literature search was conducted in Embase, Medline, Web of Science, and the Cochrane database up to 1 June, 2023. The included studies were pooled for meta-analysis to synthesize their findings. Subgroup analysis was performed to investigate potential differences between HOPE and SCS for specific subgroups. Results: A total of 11 studies comprising 1765 patients were included. Compared with SCS, HOPE was associated with a significant reduction in the incidence of early allograft dysfunction (EAD) (OR: 0.36, 95% CI: 0.26-0.50), as well as a noteworthy decrease in graft loss rate within one year (OR: 0.57, 95% CI: 0.33-0.97) and a lower occurrence of Clavien-Dindo grade IIIa or higher complications (OR: 0.62, 95% CI: 0.43-0.89). Subgroup analysis revealed that HOPE significantly reduced the one-year mortality rate, any biliary complications incidence, and acute rejection of transplanted liver rate in patients who received organs from donation after cardiac death (DCD). Conclusions: HOPE has demonstrated efficacy in reducing the incidence of EAD after LT and shows some potential in diminishing postoperative complications such as biliary complications and acute rejection. This ultimately leads to improved patient prognosis, particularly among those receiving DCD grafts.

MS-TCNet: An effective Transformer-CNN combined network using multi-scale feature learning for 3D medical image segmentation.

Medical image segmentation is a fundamental research problem in the field of medical image processing. Recently, the Transformer have achieved highly competitive performance in computer vision. Therefore, many methods combining Transformer with convolutional neural networks (CNNs) have emerged for segmenting medical images. However, these methods cannot effectively capture the multi-scale features in medical images, even though texture and contextual information embedded in the multi-scale features are extremely beneficial for segmentation. To alleviate this limitation, we propose a novel Transformer-CNN combined network using multi-scale feature learning for three-dimensional (3D) medical image segmentation, which is called MS-TCNet. The proposed model utilizes a shunted Transformer and CNN to construct an encoder and pyramid decoder, allowing six different scale levels of feature learning. It captures multi-scale features with refinement at each scale level. Additionally, we propose a novel lightweight multi-scale feature fusion (MSFF) module that can fully fuse the different-scale semantic features generated by the pyramid decoder for each segmentation class, resulting in a more accurate segmentation output. We conducted experiments on three widely used 3D medical image segmentation datasets. The experimental results indicated that our method outperformed state-of-the-art medical image segmentation methods, suggesting its effectiveness, robustness, and superiority. Meanwhile, our model has a smaller number of parameters and lower computational complexity than conventional 3D segmentation networks. The results confirmed that the model is capable of effective multi-scale feature learning and that the learned multi-scale features are useful for improving segmentation performance. We open-sourced our code, which can be found at https://github.com/AustinYuAo/MS-TCNet.

Chiral Bis-phosphate Macrocycles for Catalytic, Efficient, and Enantioselective Electrophilic Fluorination.

Incorporation of privileged catalytic scaffolds into a macrocyclic skeleton represents an attractive strategy to furnish supramolecular catalysis systems with enzyme-mimetic cavity and multi-site cooperation. Herein we reported the synthesis, structure, binding properties and catalytic application of a series of chiral bis-phosphate macrocycles toward the challenging asymmetric electrophilic fluorination. With a large, integrated chiral cavity and two cooperative phosphate sites, these macrocycles exhibited good inclusion toward 1,4-diazabicyclo[2.2.2]octane (DABCO) dicationic ammoniums through complementary ion-pair and C-H⋅⋅⋅O interactions, as confirmed by crystallographic and solution binding studies. In fluorocyclization of tryptamines with Selectfluor reagent which has a similar DABCO-based dicationic structure, only 2 mol% macrocycle catalyst afforded the desired pyrroloindoline products in moderate yields and up to 91 % ee. For comparison, the acyclic mono-phosphate analogue gave obviously lower reactivity and enantioselectivity (<20 % ee), suggesting a remarkable macrocyclic effect. The high catalytic efficiency and superior stereocontrol were ascribed to the tight ion-pair binding and cavity-directed noncovalent interaction cooperation.

Mimicking the Shape and Function of the ClC Chloride Channel Selective Pore by Combining a Molecular Hourglass Shape with Anion-π Interactions.

ClC is the main family of natural chloride channel proteins that transport Cl- across the cell membrane with high selectivity. The chloride transport and selectivity are determined by the hourglass-shaped pore and the filter located in the central and narrow region of the pore. Artificial unimolecular channel that mimics both the shape and function of the ClC selective pore is attractive, because it could provide simple molecular model to probe the intriguing mechanism and structure-function relevance of ClC. Here we elaborated upon the concept of molecular hourglass plus anion-π interactions for this purpose. The concept was validated by experimental results of molecular hourglasses using shape-persistent 1,3-alternate tetraoxacalix[2]arene[2]triazine as the central macrocyclic skeleton to control the conductance and selectivity, and anion-π interactions as the driving force to facilitate the chloride dehydration and movement along the channel.

Chiral Benzene Triimide (BTI) Radical Anions for Probing the Interplay of Unpaired Electron Spin and Chirality.

Herein a series of chiral BTI radical anions bearing different chiral substituents were efficiently prepared by chemical reduction. X-ray crystallography revealed finely-tuned packing and helix assemblies of the radicals by the size of chiral substituents in crystalline state. In accordance with the crystalline-state packing, the powder ESR spectra indicate that 4 a- ⋅CoCp2 + and 4 c- ⋅CoCp2 + π-dimers exhibit thermally excited triplet states arising from strong spin-spin interactions, while discrete 4 b- ⋅CoCp2 + shows a broad doublet-state signal reflecting weak spin-spin interactions. The interplay between the unpaired electron spin and chiral substituents was studied by UV-Vis-NIR spectra, electronic circular dichroism (ECD) and TD DFT calculations. Different NIR absorptions of the radicals attributing to isolated SOMO→LUMO+1 (~889 nm) transitions were recorded. The emergence of Cotton effects (CEs) at the NIR region for 4 c- ⋅CoCp2 + radical enantiomers suggest the interplay between chirality and unpaired electron spin. The origin of the different circularly polarized light absorptions regarding SOMO derived transitions (around 880 nm) was attributed to chiral substitutes regulated electric and magnetic transition dipole moments of the unpaired electron participated transition.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Reversing the ion transport selectivity through arm modification of an artificial molecular hourglass.

An arm modification strategy, by replacing relatively rigid, electron-deficient side arms with flexible ether chain arms and linking them onto a tetraoxacalix[2]arene[2]triazine skeleton, was utilized to design an artificial molecular hourglass. The planar bilayer experiments confirmed the unimolecular channel mechanism and suggested reversed ion selectivity from the previously reported anion selectivity to weak cation selectivity.

Comparison of different adjuvant therapy regimen efficacies in patients with high risk of recurrence after radical resection of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high recurrence rate even after radical surgery. Postoperative adjuvant transhepatic arterial chemoembolization (PA-TACE), postoperative adjuvant hepatic arterial infusion chemotherapy (PA-HAIC), postoperative adjuvant radiotherapy (PA-RT), and postoperative adjuvant molecular targeted therapy have been demonstrated to be effective in reducing the postoperative recurrence rate. The present network meta-analysis was conducted to compare the effects of PA-TACE, PA-HAIC, PA-RT and postoperative adjuvant molecular targeted therapy on the overall survival (OS) and disease-free survival (DFS) in HCC patients after radical resection and to determine the optimal treatment strategy. METHODS: Network meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed, Embase, Cochrane Library, and Web of Science were used to collect eligible studies up to December 25, 2022. Studies related to PA-TACE, PA-HAIC, and postoperative adjuvant molecular targeted therapy after radical HCC resection was included. The endpoints were OS and DFS, and the effect size was determined using hazard ratio with a 95% confidence interval. R software and "gemtc" package were employed to analyze the results. RESULTS: A total of 38 studies involving 7079 patients with HCC after radical resection were ultimately enrolled to be analyzed. Four postoperative adjuvant therapy measures and two oncology indicators were evaluated. In this study, OS-related investigations validated that PA-Sorafenib and PA-RT markedly enhanced the OS rates in patients after radical resection when compared to PA-TACE and PA-HAIC. However, statistical analysis revealed no significant difference between PA-Sorafenib and PA-RT, as well as PA-TACE and PA-HAIC. In the DFS-related investigations, PA-RT demonstrated superior efficacy over PA-Sorafenib, PA-TACE, and PA-HAIC. Additionally, PA-Sorafenib displayed better efficacy than PA-TACE. Nevertheless, there was no statistical significance between PA-Sorafenib and PA-HAIC, as well as PA-TACE and PA-HAIC. We also performed a subgroup analysis of studies focusing on HCC complicated by microvascular invasion after radical resection. In terms of OS, both PA-RT and PA-Sorafenib demonstrated a noteworthy improvement over PA-TACE, whereas no statistical significance was detected between PA-RT and PA-Sorafenib. Likewise, for DFS, both PA-Sorafenib and PA-RT exhibited superior efficacy compared to PA-TACE. CONCLUSION: In patients with HCC after radical resection and a high risk of recurrence, both PA-Sorafenib and PA-RT significantly improved OS and DFS when compared to PA-TACE and PA-HAIC. Notably, PA-RT exhibited superior efficacy over PA-Sorafenib, PA-TACE, and PA-HAIC in terms of DFS. Similarly, PA-Sorafenib appeared to be more effective than PA-TACE for DFS.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation.

OBJECTIVES: This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA). MATERIALS AND METHODS: M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis. RESULTS: A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA. CONCLUSION: Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

An Artificial Single Molecular Channel Showing High Chloride Transport Selectivity and pH-Responsive Conductance.

Inspired by the unique structure and function of the natural chloride channel (ClC) selectivity filter, we present herein the design of a ClC-type single channel molecule. This channel displays high ion transport activity with half-maximal effective concentration, EC50 , of 0.10 µM, or 0.075 mol % (channel molecule to lipid ratio), as determined by fluorescent analysis using lucigenin-encapsulated vesicles. Planar bilayer lipid membrane conductance measurements indicated an excellent Cl- /K+ selectivity with a permeability ratio P Cl - ${{_{{\rm Cl}{^{- }}}}}$ /P K + ${{_{{\rm K}{^{+}}}}}$ up to 12.31, which is comparable with the chloride selectivity of natural ClC proteins. Moreover, high anion/anion selectivity (P Cl - ${{_{{\rm Cl}{^{- }}}}}$ /P Br - ${{_{{\rm Br}{^{- }}}}}$ =66.21) and pH-dependent conductance and ion selectivity of the channel molecule were revealed. The ClC-like transport behavior is contributed by the cooperation of hydrogen bonding and anion-π interactions in the central macrocyclic skeleton, and by the existence of pH-responsive terminal phenylalanine residues.

Structure- and Data-Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity.

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high-quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000-fold) as well as reversed stereoselectivity by a structure-dependent rational design and collected a high-quality dataset in this process. Subsequently, we designed a modified one-hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data-driven design of optimized variants which then showed improved activity (up to 3-fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.

Cryptococcus tetragattii Meningitis Associated with Travel, Taiwan.

Meningitis caused by Cryptococcus tetragattii fungus is rare and has been found in specific geographic regions. We report a case of meningitis caused by C. tetragattii (molecular type VGIV) in an immunocompetent patient in Taiwan. The patient had traveled to Egypt and was positive for granulocyte-macrophage colony-stimulating factor autoantibody.