A simple predictor for donor-specific anti-HLA antibody desensitisation in haploidentical haematopoietic stem cell transplantation.

Donor-specific HLA antibody (DSA) has been recognised as an independent risk factor for graft failure in patients undergoing haploidentical haematopoietic stem cell transplantation (HID HSCT). Therapeutic plasma exchange (TPE), as a first-line strategy for DSA desensitisation, can promptly reduce serum DSA levels. This study aimed to investigate DSA characteristics and identify a biomarker predicting the efficacy of DSA desensitisation in patients proceeding to HID HSCT. We retrospectively enrolled 32 patients with DSA from April 2021 to January 2024, and analysed the mean fluorescence intensity (MFI) value of DSA at the different time points of desensitisation treatment. Compared with baseline DSA level before TPE, the median MFI of HLA class I DSA was reduced from 8178.6 to 795.3 (p < 0.001), and HLA class II DSA decreased from 6210.9 to 808.8 (p < 0.001) after TPE. The DSA level in 1:16 diluted pre-TPE serum correlated well with DSA value in post-TPE serum (class I, r = 0.85, p < 0.0001; class II, r = 0.94, p < 0.0001), predicting TPE efficacy in 84.4% of patients. Based on the degree of DSA reduction after TPE, patients were divided into complete responders (decreased by >70%), partial responders (decreased by 30 to 70%) and non-responders (decreased by <30%) and the percentages were 43.8%, 25% and 31.2%, respectively. Non-responders receiving aggressive immunotherapy had longer overall survival compared to those receiving standard strategies (p < 0.05). The 1:16 diluted pre-TPE serum may predict the efficacy of TPE and allow for more rational immunotherapy strategy for patients with DSA proceeding to HID HSCT.

A pan-cancer gamma delta T cell repertoire.

This report presents the largest collection of gamma-delta T cell receptor (γδ TCR) reads in human cancer to date, analyzing about 11,000 patient tumor samples across 33 cancer types using the TRUST4 algorithm. Despite γδ T cells being a small fraction of the T cell population, they play a key role in both innate and adaptive immunity. Our comprehensive analysis reveals their significant presence across all cancer types, specifically highlighting the diverse spectrum and clonality patterns of their γδ receptors. This research highlights the complex roles of γδ T cells in tumor tissues and their potential as prognostic biomarkers. We also demonstrate the utility of T cell receptor gamma (TRG) and delta (TRD) gene expression values from standard RNA-seq data. Ultimately, our work establishes a fundamental resource for future tumor-infiltrating γδ T cell research and may facilitate the development of novel γδ-T-cell-based therapeutic strategies. Together, we demonstrate the strong diversity and prognostic potential of γδ T cells in multiple cancer types.

Anisotropic phonon dynamics in Dirac semimetal PtTe2 thin films enabled by helicity-dependent ultrafast light excitation.

Coherent phonons have aroused considerable attention in condensed matter physics owing to their extraordinary capacity of reflecting and controlling the physical properties of matter. However, the investigation on the interaction between coherent phonons and other microscopic particles on the ultrafast timescale within topological systems continues to be an active and unresolved area. Here, we show the energy transfer of coherent optical phonons (COP) in Dirac semimetal PtTe2 thin films using ultrafast optical pump-probe spectroscopy. Specifically, the helicity-dependent light-driven anisotropic COP signals disclose their direct connection with the light-excited anisotropic spin-polarized electrons via an angular momentum transfer. Furthermore, we observe the notable decreases in the COP oscillation frequency and the decay rate with increasing temperatures due to the anharmonic phonon-phonon scattering and electron-phonon scattering in the COP dissipation process, respectively. Our work paves the way for uncovering the coherent phonons in Dirac semimetals for the potential applications in optoelectronics and opto-spintronics.

CGPA: multi-context insights from the cancer gene prognosis atlas.

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies.

Proportion of confluent B-Lines predicts respiratory support in term infants shortly after birth.

OBJECTIVE: To develop and evaluate the predictive value of a simplified lung ultrasound (LUS) method for forecasting respiratory support in term infants. METHODS: This observational, prospective, diagnostic accuracy study was conducted in a tertiary academic hospital between June and December 2023. A total of 361 neonates underwent LUS examination within 1 h of birth. The proportion of each LUS sign was utilized to predict their respiratory outcomes and compared with the LUS score model. After identifying the best predictive LUS sign, simplified models were created based on different scan regions. The optimal simplified model was selected by comparing its accuracy with both the full model and the LUS score model. RESULTS: After three days of follow-up, 91 infants required respiratory support, while 270 remained healthy. The proportion of confluent B-lines demonstrated high predictive accuracy for respiratory support, with an area under the curve (AUC) of 89.1% (95% confidence interval [CI]: 84.5-93.7%). The optimal simplified model involved scanning the R/L 1-4 region, yielding an AUC of 87.5% (95% CI: 82.6-92.3%). Both the full model and the optimal simplified model exhibited higher predictive accuracy compared to the LUS score model. The optimal cut-off value for the simplified model was determined to be 15.9%, with a sensitivity of 76.9% and specificity of 91.9%. CONCLUSIONS: The proportion of confluent B-lines in LUS can effectively predict the need for respiratory support in term infants shortly after birth and offers greater reliability than the LUS score model.

Synergistic impact of plasma albumin and cognitive function on all-cause mortality in Chinese older adults: a prospective cohort study.

Background: Hypoalbuminemia and cognitive impairment (CI) each independently increase the mortality risk in older adults. However, these two geriatric syndromes can occur simultaneously. In community-dwelling older adults, is the combination of hypoalbuminemia and CI linked to a higher mortality risk than either condition alone? Objective: We aimed to investigate the association between plasma albumin, cognitive function, and their synergistic effect on mortality in Chinese community-dwelling older adults. Methods: Data from the Chinese Longitudinal Healthy Longevity Survey (2012) included 1,858 participants aged ≥65. Baseline assessments comprised albumin levels and cognitive status. All-cause mortality was confirmed through 2014-2018 surveys. Cox proportional hazards models assessed associations, and restricted cubic splines explored albumin-mortality relationship. Results: During a median follow-up of 48.85 months, 921 deaths. Albumin≥35 g/L vs < 35g/L [HR: 1.33 (95%CI, 1.10, 1.62)] and CI vs normal cognition [HR: 1.69 (95%CI, 1.43, 1.99)] independently predicted mortality. A dose-response relationship with mortality was observed for albumin quartiles (p < 0.001). Each SD increase in MMSE or albumin correlated with 22% and 15% lower mortality risk, respectively. Combined hypoproteinemia and CI increased the mortality risk by 155%, with a notably higher risk in males, those aged <85 years, and individuals living in rural areas. Interaction effects of albumin and CI on mortality were observed (p < 0.001). In the single CI group, older adults had a 61% increased risk of mortality in the hypoproteinaemia group compared with the albumin-normal group. Restricted cubic spline revealed a reverse J-shaped association, particularly for participants without CI. For individuals with CI, albumin levels were inversely associated with mortality risk. Conclusion: Hypoproteinemia and CI, individually and combined, increased all-cause mortality risk in Chinese older adults, with stronger effects observed in males, younger older adults, and those living in rural areas. These findings emphasize the importance of targeted adjustments and early nutrition programs in health prevention and clinical care for older adults.

Novel genetic loci in early-onset gout derived from whole genome sequencing of an adolescent gout cohort.

OBJECTIVE: Mechanisms underlying the adolescent-onset and early-onset gout are unclear. This study aimed to discover variants associated with early-onset gout. METHODS: We conducted whole genome sequencing in a discovery adolescent-onset gout cohort of 905 individuals (gout onset 12-19 years) to discover common and low frequency SNVs associated with gout. Candidate common SNVs were genotyped in an early-onset gout cohort of 2834 individuals (gout onset ≤ 30 years old) and meta-analysis was performed with the discovery and replication cohorts to identify loci associated with early-onset gout. Transcriptome and epigenomic analyses, RT-qPCR and RNA-seq in human peripheral blood leukocytes, and knock-down experiments in human THP-1 macrophage cells investigated the regulation and function of candidate gene RCOR1. RESULTS: In addition to ABCG2, a urate transporter previously linked to pediatric-onset and early-onset gout, we identified two novel loci (Pmeta < 5.0 × 10-8): rs12887440 (RCOR1) and rs35213808 (FSTL5-MIR4454). Additionally, we found associations at ABCG2 and SLC22A12 that were driven by low frequency SNVs. SNVs in RCOR1 were linked to elevated blood leukocyte mRNA levels. THP-1 macrophage culture studies revealed the potential of decreased RCOR1 to suppress gouty inflammation. CONCLUSION: This is the first comprehensive genetic characterization of adolescent-onset gout. The identified risk loci of early-onset gout mediate inflammatory responsiveness to crystals that could mediate gouty arthritis. This study will contribute to risk prediction and therapeutic interventions to prevent adolescent-onset gout.

Quantitative Electroencephalography in Term Neonates During the Early Postnatal Period Across Various Sleep States.

Background: Neonatal sleep is pivotal for their growth and development, yet manual interpretation of raw images is time-consuming and labor-intensive. Quantitative Electroencephalography (QEEG) presents significant advantages in terms of objectivity and convenience for investigating neonatal sleep patterns. However, research on the sleep patterns of healthy neonates remains scarce. This study aims to identify QEEG markers that distinguish between different neonatal sleep cycles and analyze QEEG alterations across various sleep stages in relation to postmenstrual age. Methods: From September 2023 to February 2024, full-term neonates admitted to the neonatology department at the Obstetrics and Gynecology Hospital of Fudan University were enrolled in this study. Electroencephalographic (EEG) recordings were obtained from neonates aged 37-42 weeks, within 1-7 days post-birth. The ROC curve was employed to evaluate QEEG features related to amplitude, range EEG (rEEG), spectral density, and connectivity across different sleep stages. Furthermore, regression analyses were performed to investigate the association between these QEEG characteristics and postmenstrual age. Results: The alpha frequency band's spectral_diff_F3 emerged as the most potent discriminator between active sleep (AS) and quiet sleep (QS). In distinguishing AS from wakefulness (W), the theta frequency's spectral_diff_C4 was the most effective, whereas the delta frequency's spectral_diff_P4 excelled in differentiating QS from W. During AS and QS phases, there was a notable increase in entropy within the delta frequency band across all monitored brain regions and in the spectral relative power within the theta frequency band, correlating with postmenstrual age (PMA). Conclusion: Spectral difference showcases the highest discriminative capability across awake and various sleep states. The observed patterns of neonatal QEEG alterations in relation to PMA are consistent with the maturation of neonatal sleep, offering insights into the prediction and evaluation of brain development outcomes.

Radiogenomics nomogram based on MRI and microRNAs to predict microvascular invasion of hepatocellular carcinoma.

Purpose: This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs). Materials and methods: This cohort study included 168 patients (training cohort: n = 116; validation cohort: n = 52) with pathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful. Results: The independent risk factors for MVI were maximum tumor length, rad-score, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of any other single factor model (maximum tumor length, rad-score, and miRNA-21). Conclusion: The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions.

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis.

BACKGROUND: The field of neonatal sleep analysis is burgeoning with devices that purport to offer alternatives to polysomnography (PSG) for monitoring sleep patterns. However, the majority of these devices are limited in their capacity, typically only distinguishing between sleep and wakefulness. This study aims to assess the efficacy of a novel wearable electroencephalographic (EEG) device, the LANMAO Sleep Recorder, in capturing EEG data and analyzing sleep stages, and to compare its performance against the established PSG standard. METHODS: The study involved concurrent sleep monitoring of 34 neonates using both PSG and the LANMAO device. Initially, the study verified the consistency of raw EEG signals captured by the LANMAO device, employing relative spectral power analysis and Pearson correlation coefficients (PCC) for validation. Subsequently, the LANMAO device's integrated automated sleep staging algorithm was evaluated by comparing its output with expert-generated sleep stage classifications. RESULTS: Analysis revealed that the PCC between the relative spectral powers of various frequency bands during different sleep stages ranged from 0.28 to 0.48. Specifically, the correlation for delta waves was recorded at 0.28. The automated sleep staging algorithm of the LANMAO device demonstrated an overall accuracy of 79.60 %, Cohen kappa of 0.65, and F1 Score of 76.93 %. Individual accuracy for Wake at 87.20 %, NREM at 85.70 %, and REM Sleep at 81.30 %. CONCLUSION: While the LANMAO Sleep Recorder's automated sleep staging algorithm necessitates further refinement, the device shows promise in accurately recording neonatal EEG during sleep. Its potential for minimal invasiveness makes it an appealing option for monitoring sleep conditions in newborns, suggesting a novel approach in the field of neonatal sleep analysis.

Pre-existing skin-resident CD8 and gd T cell circuits mediate immune response in Merkel cell carcinoma and predict immunotherapy efficacy.

Merkel cell carcinoma (MCC) is an aggressive neuroendocrine skin cancer with a ~50% response rate to immune checkpoint blockade (ICB) therapy. To identify predictive biomarkers, we integrated bulk and single-cell RNA-seq with spatial transcriptomics from a cohort of 186 samples from 116 patients, including bulk RNA-seq from 14 matched pairs pre- and post-ICB. In non-responders, tumors show evidence of increased tumor proliferation, neuronal stem cell markers, and IL-1. Responders have increased type I/II interferons and pre-existing tissue resident (Trm) CD8 or Vd1 gd T cells that functionally converge with overlapping antigen-specific transcriptional programs and clonal expansion of public TCRs. Spatial transcriptomics demonstrated co-localization of T cells with B and dendritic cells, which supply chemokines and co-stimulation. Lastly, ICB significantly increased clonal expansion or recruitment of Trm and Vd1 cells in tumors specifically in responders, underscoring their therapeutic importance. These data identify potential clinically actionable biomarkers and therapeutic targets for MCC.

The density of the inner cell mass is a new indicator of the quality of a human blastocyst: a valid supplement to the Gardner scoring system.

STUDY QUESTION: Can the density of the inner cell mass (ICM) be a new indicator of the quality of the human blastocyst? SUMMARY ANSWER: The densification index (DI) developed in this study can quantify ICM density and provide positive guidance for ploidy, pregnancy, and live birth. WHAT IS KNOWN ALREADY: In evaluating the quality of ICM, reproductive care clinics still use size indicators without further evaluation. The main disadvantage of this current method is that the evaluation of blastocyst ICM is relatively rough and cannot meet the needs of clinical embryologists, especially when multiple blastocysts have the same ICM score, which makes them difficult to evaluate further. STUDY DESIGN, SIZE, DURATION: This observational study included data from 2272 blastocysts in 1991 frozen-thawed embryo transfer (FET) cycles between January 2018 to November 2021 and 1105 blastocysts in 430 preimplantation genetic testing cycles between January 2019 and February 2023. PARTICIPANTS/MATERIALS, SETTING, METHODS: FET, ICSI, blastocyst culture, trophectoderm biopsy, time-lapse (TL) monitoring, and next-generation sequencing were performed. After preliminary sample size selection, the 11 focal plane images captured by the TL system were normalized and the spatial frequency was used to construct the DI of the ICM. MAIN RESULTS AND THE ROLE OF CHANCE: This study successfully constructed a quantitative indicator DI that can reflect the degree of ICM density in terms of fusion and texture features. The higher the DI value, the better the density of the blastocyst ICM, and the higher the chances that the blastocyst was euploid (P < 0.001) and that pregnancy (P < 0.001) and live birth (P = 0.005) were reached. In blastocysts with ICM graded B and blastocysts graded 4BB, DI was also positively associated with ploidy, pregnancy, and live birth (P < 0.05). ROC analysis showed that combining the Gardner scoring system with DI can more effectively predict pregnancy and live births, when compared to using the Gardner scoring system alone. LIMITATIONS, REASONS FOR CAUTION: Accurate calculation of the DI value places high demands on image quality, requiring manual selection of the clearest focal plane and exposure control. Images with the ICM not completely within the field of view cannot be used. The association between the density of ICM and chromosomal mosaicism was not evaluated. The associations between the density of ICM and different assisted reproductive technologies and different culture conditions in embryo laboratories were also not evaluated. Prospective studies are needed to further investigate the impact of ICM density on clinical outcomes. WIDER IMPLICATIONS OF THE FINDINGS: ICM density assessment is a new direction in blastocyst assessment. This study explores new ways of assessing blastocyst ICM density and develops quantitative indicators and a corresponding qualitative evaluation scheme for ICM density. The DI of the blastocyst ICM developed in this study is easy to calculate and requires only TL equipment and image processing, providing positive guidance for clinical outcomes. The qualitative evaluation scheme of ICM density can assist embryologists without TL equipment to manually evaluate ICM density. ICM density is a simple indicator that can be used in practice and is a good complement to the blastocyst scoring systems currently used in most centers. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the National Key Research & Development Program of China (2021YFC2700603). The authors report no financial or commercial conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Dilute Electrolytes with Fluorine-free Ether Solvents for 4.5 V Lithium Metal Batteries.

The limited oxidation stability of ether solvents has posed significant challenges for their applications in high-voltage lithium metal batteries (LMBs). To tackle this issue, the prevailing strategy either adopts a high concentration of fluorinated salts or relies on highly fluorinated solvents, which will significantly increase the manufacturing cost and create severe environmental hazards. Herein, an alternative and sustainable salt engineering approach is proposed to enable the utilization of dilute electrolytes consisting of fluorine (F)-free ethers in high-voltage LMBs. The proposed 0.8 M electrolyte supports stable lithium plating-stripping with a high Coulombic efficiency of 99.47% and effectively mitigates the metal dissolution, phase transition, and gas release issues of the LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode upon charging to high voltages. Consequently, the 4.5 V high-loading Li||NCM 811 cell shows a capacity retention of 75.2% after 300 cycles. Multimodal experimental characterizations coupled with theoretical investigations demonstrate that the boron-containing salt plays a pivotal role in forming the passivation layers on both anode and cathode. The present simple and cost-effective electrolyte design strategy offers a promising and alternative avenue for using commercially mature, environmentally benign, and low-cost F-free ethers in high-voltage LMBs.

GPS-Net: discovering prognostic pathway modules based on network regularized kernel learning.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics.

SmartImpute: A Targeted Imputation Framework for Single-cell Transcriptome Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and tissue transcriptomic complexity. However, the high frequency of dropout events in scRNA-seq data complicates downstream analyses such as cell type identification and trajectory inference. Existing imputation methods address the dropout problem but face limitations such as high computational cost and risk of over-imputation. We present SmartImpute, a novel computational framework designed for targeted imputation of scRNA-seq data. SmartImpute focuses on a predefined set of marker genes, enhancing the biological relevance and computational efficiency of the imputation process while minimizing the risk of model misspecification. Utilizing a modified Generative Adversarial Imputation Network architecture, SmartImpute accurately imputes the missing gene expression and distinguishes between true biological zeros and missing values, preventing overfitting and preserving biologically relevant zeros. To ensure reproducibility, we also provide a function based on the GPT4 model to create target gene panels depending on the tissue types and research context. Our results, based on scRNA-seq data from head and neck squamous cell carcinoma and human bone marrow, demonstrate that SmartImpute significantly enhances cell type annotation and clustering accuracy while reducing computational burden. Benchmarking against other imputation methods highlights SmartImpute's superior performance in terms of both accuracy and efficiency. Overall, SmartImpute provides a lightweight, efficient, and biologically relevant solution for addressing dropout events in scRNA-seq data, facilitating deeper insights into cellular heterogeneity and disease progression. Furthermore, SmartImpute's targeted approach can be extended to spatial omics data, which also contain many missing values.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Develop Tandem Tension Sensor to Gauge Integrin-Transmitted Molecular Forces.

DNA-based tension sensors have innovated the imaging and calibration of mechanosensitive receptor-transmitted molecular forces, such as integrin tensions. However, these sensors mainly serve as binary reporters, only indicating if molecular forces exceed one predefined threshold. Here, we have developed tandem tension sensor (TTS), which comprises two consecutive force-sensing units, each with unique force detection thresholds and distinct fluorescence spectra, thereby enabling the quantification of molecular forces with dual reference levels. With TTS, we revealed that vinculin is not required for transmitting integrin tensions at approximately 10 pN (piconewtons) but is essential for elevating integrin tensions beyond 20 pN in focal adhesions (FAs). Such high tensions have emerged during the early stage of FA formation. TTS also successfully detected changes in integrin tensions in response to disrupted actin formation, inhibited myosin activity, and tuned substrate elasticity. We also applied TTS to examine integrin tensions in platelets and revealed two force regimes, with integrin tensions surpassing 20 pN at cell central regions and 13-20 pN integrin tensions at the cell edge. Overall, TTS, especially the construct consisting of a hairpin DNA (13 pN opening force) and a shearing DNA (20 pN opening force), stands as a valuable tool for the quantification of receptor-transmitted molecular forces within living cells.

Formation of Delocalized Linear M-B-M Covalent Bonds: A Combined Experimental and Theoretical Study of BM2(CO)8+ (M = Co, Rh, Ir) Complexes.

Investigations of transition-metal boride clusters not only lead to novel structures but also provide important information about the metal-boron bonds that are critical to understanding the properties of boride materials. The geometric structures and bonding features of heteronuclear boron-containing transition metal carbonyl cluster cations BM(CO)6+ and BM2(CO)8+ (M = Co, Rh, and Ir) are studied by a combination of the infrared photodissociation spectroscopy and density functional calculations at B3LYP/def2-TZVP level. The completely coordinated BM2(CO)8+ complexes are characterized as a sandwich structure composed of two staggered M(CO)4 fragments and a boron cation, featuring a D3d symmetry and 1Eg electronic ground state as well as metal-anchored carbonyls in an end-on manner. In conjunction with theoretical calculations, multifold metal-boron-metal bonding interactions in BM2(CO)8+ complexes involving the filled d orbitals of the metals and the empty p orbitals of the boron cation were unveiled, namely, one σ-type M-B-M bond and two π-type M-B-M bonds. Accordingly, the BM2(CO)8+ complexes can be described as a linear conjugated (OC)4M═B═M(CO)4 skeleton with a formal B-M bond index of 1.5. The three delocalized d-p-d covalent bonds render compensation for the electron deficiency of the cationic boron center and endow both metal centers with the favorable 18-electron structure, thus contributing much to the overall structural stability of the BM2(CO)8+ cations. As a comparison, the saturated BRh(CO)6+ and BIr(CO)6+ complexes are determined to be a doublet Cs-symmetry structure with an unbridged (OC)2B-M(CO)4 pattern, involving a two-center σ-type (OC)2B → M(CO)4+ dative single bond along with a weak covalent B-M half bond. This work offers important insight into the structure and bonding of late transition metal boride carbonyl cluster cations.

Machine learning-aided unveiling the relationship between chemical pretreatment and methane production of lignocellulosic waste.

Chemical pretreatment is a common method to enhance the cumulative methane yield (CMY) of lignocellulosic waste (LW) but its effectiveness is subject to various factors, and accurate estimation of methane production of pretreated LW remains a challenge. Here, based on 254 LW samples, a machine learning (ML) model to predict the methane production performance of pretreated feedstock was constructed using two automated ML platforms (tree-based pipeline optimization tool and neural network intelligence). Furthermore, the interactive effects of pretreatment conditions, feedstock properties, and digestion conditions on methane production of pretreated LW were studied through model interpretability analysis. The optimal ML model performed well on the validation set, and the digestion time, pretreatment agent, and lignin content (LC) were found to be key factors affecting the methane production of pretreated LW. If the LC in the raw LW was lower than 15%, the maximum CMY might be achieved using the NaOH, KOH, and alkaline hydrogen peroxide (AHP) with concentrations of 3.8%, 4.4%, and 4.5%, respectively. On the other hand, if LC was higher than 15%, only high concentrations of AHP exceeding 4% could significantly increase methane production. This study provides valuable guidance for optimizing pretreatment process, comparing different chemical pretreatment approaches, and regulating the operation of large-scale biogas plants.

B7-H3 enhances colorectal cancer progression by regulating HB-EGF via HIF-1α.

Background: B7-H3 (or CD276) represents an important costimulatory molecule expressed in many malignant solid tumors, including colorectal cancer (CRC). The receptor of B7-H3 is not known, and the intracellular function of B7-H3 remains obscure. Herein, we report that B7-H3 upregulated the epidermal growth factor heparin-binding epidermal growth factor (HB-EGF), likely by regulating hypoxia-inducible factor 1α (HIF-1α) and thereby promoting the progression of CRC. Methods: Lentiviral transfection was performed on CRC cells to establish stable low-B7-H3 expression cells. A mechanistic analysis with an Agilent human gene expression profiling chip was conducted on them. Clinical data and specimens were collected to detect the connection between B7-H3 and HB-EGF in CRC. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to detect the messenger RNA (mRNA) level of B7-H3, HB-EGF, and HIF-1α. Chromatin immunoprecipitation (ChIP) quantitative real-time PCR was conducted. The protein level of HIF-1α and the phosphatidylinositide 3-kinases (PI3K)-protein kinase B (AKT) pathway were detected by western blot. HIF-1α was recovered by lentiviral transfection, and the HB-EGF mRNA levels, proliferation, invasion, and angiogenesis ability were detected. Results: B7-H3 promoted tumor progression through HB-EGF and the PI3K-AKT pathway. As B7-H3 was downregulated, HB-EGF levels were significantly reduced simultaneously, a growth trend that was shown by both CRC cell lines and cancer tissues. In addition, B7-H3 and HB-EGF had significant associations with tumor-node-metastasis (TNM) stage and lymph node metastasis in 50 CRC patients. The binding ability of HIF-1α to the HB-EGF promoter region was significantly decreased in the shB7-H3 RKO group. Western blot revealed that PI3K, AKT, and mammalian target of rapamycin (mTOR) protein amounts and p-AKT and p-mTOR phosphorylation were also downregulated in shB7-H3 RKO cells, suggesting that B7-H3 may regulate HIF-1α via PI3K-AKT signaling. After recovery of the HIF-1α level by lentiviral transfection, the HB-EGF mRNA levels, proliferation, invasion, and angiogenesis in CRC cells recovered as well. Conclusions: B7-H3 may transmit intracellular signals through PI3K-AKT-mTOR-HIF-1α signaling, upregulating HB-EGF. As the final transcription factor of the pathway, HIF-1α regulates the transcription of the HB-EGF gene, thereby promoting HB-EGF expression, which eventually mediates cell proliferation, invasion, and angiogenesis and promotes the progression of CRC.