The association between body mass index and bone mineral density in older adults: a cross-sectional study of community population in Beijing.

BACKGROUND: Older subjects have a higher risk for vertebral compression fracture. Maintaining a higher bone mineral density (BMD) at this age can protect individuals from osteoporosis-related events. Body mass index (BMI) has been found to have a robust association with BMD. However, excessive BMI is detrimental to bone health and may cause systemic disorders. Therefore, the present study aimed to determine the association between BMI and BMD, and identify a reasonable BMI range. METHODS: A total of 961 participants were recruited from community-dwelling residents between August 2021 and May 2022. A weighted multivariate linear regression model was applied to identify the relationship between BMI and BMD. Meanwhile, subgroup stratified analysis by BMI quartile and gender was also performed. A non-linear relationship and threshold value were determined based on the smooth curve fittings and threshold effects analysis model. RESULTS: A robust relationship was found between BMI and BMD, which remained significant in subgroups stratified by gender and BMI quartile. The BMI inflection point values in lumbar BMD and femoral neck BMD were 25.2 kg/m2 and 27.3 kg/m2, respectively. For individuals with BMI < 25.2 kg/m2, an increase in BMI was related to an increase in lumbar BMD. For BMI > 25.2 kg/m2, an increase in BMI was associated with a decrease in lumbar BMD. For subjects with BMI < 27.3 kg/m2, the femoral neck BMD rose by 0.008 kg/m2 for each unit rise in BMI. However, when BMI exceeded 27.3 kg/m2, the femoral neck BMD increased only by 0.005 kg/m2. Fracture risk assessment based on the spinal deformity index (SDI) failed to determine the optimal BMI range. CONCLUSIONS: This study found an inflection point between BMI and lumbar/ femoral neck BMD in older community-dwelling subjects. An appropriate BMI but not an excessive BMI may allow older adults to have a better BMD.

Development and validation of an integrated system for lung cancer screening and post-screening pulmonary nodules management: a proof-of-concept study (ASCEND-LUNG).

Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case-control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models' development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869-0.950), followed by the protein model (0.891 [95% CI, 0.845-0.938]) and lastly the mutation model (0.577 [95% CI, 0.482-0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942-0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957-1.000) at a specificity of 56.3% (95% CI, 0.472-0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732-0.875), a specificity of 76.0% (95% CI, 0.549-0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

Viral and thermal lysis facilitates transmission of antibiotic resistance genes during composting.

While the distribution of extracellular ARGs (eARGs) in the environment has been widely reported, the factors governing their release remain poorly understood. Here, we combined multi-omics and direct experimentation to test whether the release and transmission of eARGs are associated with viral lysis and heat during cow manure composting. Our results reveal that the proportion of eARGs increased 2.7-fold during composting, despite a significant and concomitant reduction in intracellular ARG abundances. This relative increase of eARGs was driven by composting temperature and viral lysis of ARG-carrying bacteria based on metagenome-assembled genome (MAG) analysis. Notably, thermal lysis of mesophilic bacteria carrying ARGs was a key factor in releasing eARGs at the thermophilic phase, while viral lysis played a relatively stronger role during the non-thermal phase of composting. Furthermore, MAG-based tracking of ARGs in combination with direct transformation experiments demonstrated that eARGs released during composting pose a potential transmission risk. Our study provides bioinformatic and experimental evidence of the undiscovered role of temperature and viral lysis in co-driving the spread of ARGs in compost microbiomes via the horizontal transfer of environmentally released DNA. IMPORTANCE: The spread of antibiotic resistance genes (ARGs) is a critical global health concern. Understanding the factors influencing the release of extracellular ARGs (eARGs) is essential for developing effective strategies. In this study, we investigated the association between viral lysis, heat, and eARG release during composting. Our findings revealed a substantial increase in eARGs despite reduced intracellular ARG abundance. Composting temperature and viral lysis were identified as key drivers, with thermal lysis predominant during the thermophilic phase and viral lysis during non-thermal phases. Moreover, eARGs released during composting posed a transmission risk through horizontal gene transfer. This study highlights the significance of temperature and phage lysis in ARG spread, providing valuable insights for mitigating antibiotic resistance threats.

O-Glycosyltransferase Gene BnaC09.OGT Involved in Regulation of Unsaturated Fatty Acid Biosynthesis for Enhancing Osmotic Stress Tolerance in Brassica napus L.

Osmotic stress is a major threaten to the growth and yield stability of Brassica napus. Post-translational modification with O-linked ß-N-acetylglucosamine (O-GlcNAc) is ubiquitous in plants, and participates in a variety of signal transduction and metabolic regulation. However, studies on the role of O-GlcNAc transferase (OGT) in osmotic stress tolerance of plants are limited. In previous study, a O-glycosyltransferase, named BnaC09.OGT, was identified from the B. napus variety 'Zhongshuang 11' by yeast one hybrid with promoter of BnaA01.GPAT9. It was found that BnaC09.OGT localized in both nucleus and cytoplasm. The spatiotemporal expression pattern of BnaC09.OGT exhibited tissue specificity in developmental seed, especially in 15 days after pollination. In view of osmotic stress inducing, the BnaC09.OGT overexpression and knockout transgenic lines were constructed for biological function study. Phenotypic analysis of BnaC09.OGT overexpression seedlings demonstrated that BnaC09.OGT could enhance osmotic stress tolerance than WT and knockout lines in euphylla stage under 15% PEG6000 treatment after 7 days. In addition, compared with WT and knockout lines, overexpression of BnaC09.OGT had significantly higher activities of antioxidant enzymes (SOD and POD), higher content of soluble saccharide, and while significantly less content of malondialdehyde, proline and anthocyanidin under 15% PEG6000 treatment after 7 days. On the other hand, the unsaturated fatty acid content of BnaC09.OGT overexpression was significantly higher than that of WT and knockout lines, so it is speculated that the BnaC09.OGT could increase unsaturated fatty acid biosynthesis for osmotic stress tolerance by promoting the expression of BnaA01.GPAT9 in glycerolipid biosynthesis. In summary, the above results revealed that the function of BnaC09.OGT provides new insight for the analysis of the pathway of O-glycosylation in regulating osmotic stress tolerance in B. napus.

SLC14A1 and TGF-ß signaling: a feedback loop driving EMT and colorectal cancer metachronous liver metastasis.

BACKGROUND: Colorectal cancer (CRC) metachronous liver metastasis is a significant clinical challenge, largely attributable to the late detection and the intricate molecular mechanisms that remain poorly understood. This study aims to elucidate the role of Solute Carrier Family 14 Member 1 (SLC14A1) in the pathogenesis and progression of CRC metachronous liver metastasis. METHODS: We conducted a comprehensive analysis of CRC patient data from The Cancer Genome Atlas and GSE40967 databases, focusing on the differential expression of genes associated with non-metachronous liver metastasis and metachronous liver metastasis. Functional assays, both in vitro and in vivo, were performed to assess the biological impact of SLC14A1 modulation in CRC cells. Gene set enrichment analysis, molecular assays and immunohistochemical analyses on clinical specimens were employed to unravel the underlying mechanisms through which SLC14A1 exerts its effects. RESULTS: SLC14A1 was identified as a differentially expressed gene, with its overexpression significantly correlating with poor relapse-free and overall survival. Mechanistically, elevated SLC14A1 levels enhanced CRC cell invasiveness and migratory abilities, corroborated by upregulated TGF-ß/Smad signaling and Epithelial-Mesenchymal Transition. SLC14A1 interacted with TßRII and stabilized TßRII protein, impeding its Smurf1-mediated K48-linked ubiquitination and degradation, amplifying TGF-ß/Smad signaling. Furthermore, TGF-ß1 reciprocally elevated SLC14A1 mRNA expression, with Snail identified as a transcriptional regulator, binding downstream of SLC14A1's transcription start site, establishing a positive feedback loop. Clinically, SLC14A1, phosphorylated Smad2, and Snail were markedly upregulated in CRC patients with metachronous liver metastasis, underscoring their potential as prognostic markers. CONCLUSIONS: Our findings unveil SLC14A1 as a critical regulator in CRC metachronous liver metastasis, providing novel insights into the molecular crosstalk between SLC14A1 and TGF-ß/Smad signaling. These discoveries not only enhance our understanding of CRC metachronous liver metastasis pathogenesis, but also highlight SLC14A1 as a promising target for therapeutic intervention and predictive marker.

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks.

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.

Precisely Constructing Molecular Junctions in Hydrogen-Bonded Organic Frameworks for Efficient Artificial Photosynthetic CO2 Reduction.

The development of artificial photocatalysts to convert CO2 into renewable fuels and H2O into O2 is a complex and crucial task in the field of photosynthesis research. The current challenge is to enhance photogenerated charge separation, as well as to increase the oxidation capability of materials. Herein, a molecular junction-type porphyrin-based crystalline photocatalyst (Ni-TCPP-TPyP) was successfully self-assembled by incorporating a nickel porphyrin complex as a reduction site and pyridyl porphyrin as an oxidation site via hydrogen bonding and π-π stacking interactions. The resulting material has a highly crystalline structure, and the formation of inherent molecular junctions can accelerate photogenerated charge separation and transport. Thus, Ni-TCPP-TPyP achieved an excellent CO production rate of 309.3 µmol g-1 h-1 (selectivity, ~100%) without the use of any sacrificial agents, which is more than ten times greater than that of single-component photocatalyst (Ni-TCPP) and greater than that of the most organic photocatalysts. The structure-function relationship was investigated by femtosecond transient absorption spectroscopy and density functional theory calculations. Our work provides new insight for designing efficient artificial photocatalysts, paving the way for the development of clean and renewable fuels through the conversion of CO2 using solar energy.

Engineering electronic structures and oxygen vacancies of manganese-doped nickel molybdate porous nanosheets for efficient oxygen evolution reaction.

The design strategy of designing effective local electronic structures of active sites to improve the oxygen evolution reaction (OER) performance is the key to the success of sustainable alkaline water electrolysis processes. Herein, a series of manganese-doped nickel molybdate porous nanosheets with rich oxygen vacancies on the nickel foam (Mn-NiMoO4/NF PNSs) synthesized by the facile hydrothermal and following annealing routes are used as high-efficiency and robust catalysts towards OER. By virtue of unique nanosheets architectures, more exposed active site, rich oxygen vacancies, tailored electronic structures, and improved electrical conductivity induced by Mn incorporation, as predicted, the optimized Mn0.10-NiMoO4/NF PNSs catalyst exhibits superior the OER performance with a low overpotential of 211 mV at 10 mA‧cm-2, a small Tafel slope of 41.7 mV‧dec-1, and an excellent stability for 100 h operated at 100 mA‧cm-2 in 1.0 M KOH electrolyte. The in-situ Raman measurements reveal the surface dynamic reconstruction. Besides, the results of density functional theory (DFT) calculations unveil the reaction mechanism. This study provides an effective design strategy via Mn incorporation to synergistically engineer electronic structures and oxygen vacancies of metal oxides for efficiently boosting the OER performance.

Comparison of femoral tunnel position and knee function in anterior cruciate ligament reconstruction: a retrospective cohort study using measuring-fluoroscopy method versus bony marker method.

BACKGROUND: Previous studies have shown that surgical technique errors especially the wrong bone tunnel position are the primary reason for the failure of anterior cruciate ligament (ACL) reconstruction. In this study, we aimed to compare the femoral tunnel position and impact on knee function during the ACL reconstruction using measuring combined with fluoroscopy method and bony marker method for femoral tunnel localization. METHODS: A retrospective cohort study of patients undergoing ACL reconstruction using the bony marker method or measuring combined with fluoroscopy for femoral tunnel localization was conducted between January 2015 and January 2020. A second arthroscopic exploration was performed more than 1 year after surgery. Data regarding patient demographics, the femoral tunnel position, results of the Lysholm score, the International Knee Documentation Committee (IKDC) score, KT-1000 side-to-side difference, pivot shift grade, and Lachman grade of the knee were collected. RESULTS: A total of 119 patients were included in the final cohort. Of these, 42 cases were in the traditional method group, and 77 cases were in the measuring method group. The good tunnel position rate was 26.2% in the traditional method group and 81.8% in the measuring method group (p < 0.001). At the final follow-up, the Lysholm and IKDC scores were significantly greater in the measuring method group than the traditional method group (IKDC: 84.9 ± 8.4 vs. 79.6 ± 6.4, p = 0.0005; Lysholm: 88.8 ± 6.4 vs. 81.6 ± 6.4, p < 0.001). Lachman and pivot shift grades were significantly greater in the measuring method group (p = 0.01, p = 0008). The results of KT-1000 side-to-side differences were significantly better in the measuring method group compared with those in the traditional method group (p < 0.001). CONCLUSIONS: The combination of the measuring method and intraoperative fluoroscopy resulted in a concentrated tunnel position on the femoral side, a high rate of functional success, improved knee stability, and a low risk of tunnel deviation. This approach is particularly suitable for surgeons new to ACL reconstructive surgery.

Inhibiting perovskite decomposition by a creeper-inspired strategy enables efficient and stable perovskite solar cells.

The commercialization of perovskite solar cells is badly limited by stability, an issue determined mainly by perovskite. Herein, inspired by a natural creeper that can cover the walls through suckers, we adopt polyhexamethyleneguanidine hydrochloride as a molecular creeper on perovskite to inhibit its decomposition starting from the annealing process. The molecule possesses a long-line molecular structure where the guanidinium groups can serve as suckers that strongly anchor cations through multiple hydrogen bonds. These features make the molecular creeper can cover perovskite grains and inhibit perovskite decomposition by suppressing cations' escape. The resulting planar perovskite solar cells achieve an efficiency of 25.42% (certificated 25.36%). Moreover, the perovskite film and device exhibit enhanced stability even under harsh damp-heat conditions. The devices can maintain >96% of their initial efficiency after 1300 hours of operation under 1-sun illumination and 1000 hours of storage under 85% RH, respectively.

Swallowing rehabilitation of patients undergoing T-tube implantation for the treatment of laryngotracheal stenosis.

OBJECTIVE: Patients with laryngotracheal stenosis (LTS) often have dysphagia after laryngotracheal reconstruction with T-tube insertion, which affects the quality of life. The purpose of this study is to observe the effect of swallowing rehabilitation therapy on the improvement of quality of life in patients of otolaryngology-head and neck surgery with dysphagia undergoing T-tube implantation treatment through longitudinal study. METHODS: Thirty-eight patients with LTS who experienced dysphagia after laryngotracheal reconstruction and T-tube implantation were recruited. All patients received swallowing rehabilitation therapy. The assessment of swallowing function was performed using the 10-item Eating Assessment Tool (EAT-10), the 30 mL water swallow test (WST), and flexible endoscopic evaluation of swallow (FEES). RESULTS: After swallowing rehabilitation therapy, timing of swallowing, grade of dysphagia, performance on FEES and 30 mL WST, and EAT-10 score all improved. Thirty-eight patients successfully transitioned to oral feeding and were able to remove their nasogastric tubes without experiencing any complications, including aspiration pneumonia. CONCLUSION: For patients with LTS who experienced dysphagia after laryngotracheal reconstruction and T-tube implantation, swallowing rehabilitation therapy could improve swallowing function of the patients, so as to reduce the potential harm caused by the pain and complications of surgery experienced by patients.

Effect of Bridging Manner on the Transport Behaviors of Dimethyldihydropyrene/Cyclophanediene Molecular Devices.

A molecule-electrode interface with different coupling strengths is one of the greatest challenges in fabricating reliable molecular switches. In this paper, the effects of bridging manner on the transport behaviors of a dimethyldihydropyrene/cyclophanediene (DHP/CPD) molecule connected to two graphene nanoribbon (GNR) electrodes have been investigated by using the non-equilibrium Green's function combined with density functional theory. The results show that both current values and ON/OFF ratios can be modulated to more than three orders of magnitude by changing bridging manner. Bias-dependent transmission spectra and molecule-projected self-consistent Hamiltonians are used to illustrate the conductance and switching feature. Furthermore, we demonstrate that the bridging manner modulates the electron transport by changing the energy level alignment between the molecule and the GNR electrodes. This work highlights the ability to achieve distinct conductance and switching performance in single-molecular junctions by varying bridging manners between DHP/CPD molecules and GNR electrodes, thus offering practical insights for designing molecular switches.

Harnessing synergistic effects in GQD@Pt(II) nanocomposites for enhanced photovoltaic performance: a computational study.

CONTEXT: The development of efficient solar energy conversion technologies is crucial for addressing global energy challenges and reducing reliance on fossil fuels. Platinum(II) complexes are promising materials for photovoltaic applications due to their strong light absorption and long-lived excited states. However, their narrow absorption in the visible spectrum and stability issues limit their performance. Combining platinum(II) complexes with graphene quantum dots (GQDs) can enhance photovoltaic performance by leveraging the complementary light harvesting and charge transfer characteristics of the two components. This study utilizes density functional theory (DFT) calculations to explore their electronic structures, charge transfer dynamics, and photoelectric performance. Specifically, it investigates the effects of incorporating different substituents, either electron-donating or electron-withdrawing, onto the fluorene motif of the Pt(II) complex. The findings reveal that combining GQDs with Pt(II) complexes extends light absorption into the UV range, enabling comprehensive solar utilization. Upon photoexcitation, electrons migrate between the GQD conduction band and the Pt(II) complex, stabilizing charges and enhancing extraction. Substituents significantly influence charge transfer dynamics: electron-withdrawing groups promote transfer to the GQD, while electron-donating groups encourage charge separation and delocalization. Nanocomposites featuring electron-donating substituents achieve the highest energy conversion efficiencies, with GQD@Pt(II)-NPh2 reaching 24.6%. This is attributed to improved light harvesting, efficient charge injection, and reduced recombination. These insights guide the rational design of GQD-Pt(II) nanocomposites, optimizing charge separation and transfer processes for enhanced photovoltaic performance. The computational approach employed here provides a robust tool for developing advanced materials in renewable energy technologies. METHODS: The computational studies reported in this work were performed using the DFT approach, specifically employing the hybrid functional PBE0. The PBE0 functional's accuracy in describing electronic structures and excited-state properties is essential for understanding charge transfer processes, photoabsorption, and emission characteristics in metal-organic complexes. Geometry optimizations and time-dependent DFT (TD-DFT) calculations were carried out to investigate the properties of the nanocomposites. The effects of solvents were replicated using the conductor-like polarizable continuum model (CPCM). The charge transfer length (ΔL) and interfragment charge transfer (ΔQ) were calculated using the Multiwfn software package, and all calculations were performed using the BDF software package.

Deep Amplicon Sequencing Reveals Culture Selection of Mycobacterium Tuberculosis by Clinical Samples.

The commonly-used drug susceptibility testing (DST) relies on bacterial culture and faces shortcomings such as long turnaround time and clone/subclone selection. We developed a targeted deep amplification sequencing (DAS) method directly applied to clinical specimens. In this DAS panel, we examined 941 drug-resistant mutations associated with 20 anti-tuberculosis drugs with an initial amount of 4 pg DNA and reduced clinical testing time from 20 days to two days. A prospective study was conducted using 115 clinical specimens mainly with Xpert® Mycobacterium tuberculosis/rifampicin (Xpert MTB/RIF) assay positive to evaluate drug-resistant mutation detection. DAS was performed on culture-free specimens, while culture-dependent isolates were used for phenotypic DST, DAS, and whole-genome sequencing (WGS). For in silico molecular DST, our result based on DAS panel revealed the similar accuracy to three published reports based on WGS. For 82 isolates, application of DAS showed better sensitivity (93.03% vs. 92.16%), specificity (96.10% vs. 95.02%), and accuracy (91.33% vs. 90.62%) than Mykrobe software using WGS. Compared to culture-dependent WGS, culture-free DAS provides a full picture of sequence variation at population level, exhibiting in detail the gain-and-loss variants caused by bacterial culture. Our study performs a systematic verification of the advantages of DAS in clinical applications and comprehensively illustrates the discrepancy in Mycobacterium tuberculosis before and after culture.

Quantification of gut microbiome metabolites using chemical isotope derivatization strategy combined with LC-MS/MS: Application in neonatal hypoxic-ischemic encephalopathy rat model.

The gut microbiome plays pivotal roles in various physiological and pathological processes, with key metabolites including short chain fatty acids (SCFAs), bile acids (BAs), and tryptophan (TRP) derivatives gaining significant attention for their diverse physiological roles. However, quantifying these metabolites presents challenges due to structural similarity, low abundance, and inherent technical limitations in traditional detection methods. In this study, we developed a precise and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method utilizing a chemical isotope derivatization technique employing 4-(aminomethyl)-N,N-dimethylaniline-d0/d6 (4-AND-d0/d6) reagents to quantify 37 typical gut microbiome-derived metabolites. This method achieved an impressive 1500-fold enhancement in sensitivity for detecting metabolites, compared to methods using non-derivatized, intact molecules. Moreover, the quantitative accuracy of our chemical isotope derivatization strategy proved comparable to the stable isotope labeled internal standards (SIL-IS) method. Subsequently, we successfully applied this newly developed method to quantify target metabolites in plasma, brain, and fecal samples obtained from a neonatal hypoxic-ischemic encephalopathy (HIE) rat model. The aim was to identify crucial metabolites associated with the progression of HIE. Overall, our sensitive and reliable quantification method holds promise in elucidating the role of gut microbiome metabolites in the pathogenesis of various diseases.

Formation and reactivity of a unique M⋯C-H interaction stabilized by carborane cages.

Broadening carborane applications has consistently been the goal of chemists in this field. Herein, compared to alkyl or aryl groups, a carborane cage demonstrates an advantage in stabilizing a unique bonding interaction: M⋯C-H interaction. Experimental results and theoretical calculations have revealed the characteristic of this two-center, two-electron bonding interaction, in which the carbon atom in the arene ring provides two electrons to the metal center. The reduced aromaticity of the benzene moiety, long distance between the metal and carbon atom in arene, and the upfield shift of the signal of M⋯C-H in the nuclear magnetic resonance spectrum distinguished this interaction from metal⋯C π interaction and metal-C(H) σ bonds. Control experiments demonstrate the unique electronic effects of carborane in stabilizing the M⋯C-H bonding interaction in organometallic chemistry. Furthermore, the M⋯C-H interaction can convert into C-H bond metallization under acidic conditions or via treatment with t-butyl isocyanide. These findings deepen our understanding regarding the interactions between metal centers and carbon atoms and provide new opportunities for the use of carboranes.

Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance.

Objective: To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance. Methods: Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively. Conclusion: After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.

FPNC Net: A hydrogenation catalyst image recognition algorithm based on deep learning.

The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets.

Stabilizing Zinc Hexacyanoferrate Cathode by Low Contents of Cs Cations for Aqueous Zn-Ion Batteries.

Exploring cathode materials with excellent electrochemical performance is crucial for developing rechargeable aqueous zinc ion batteries (RAZIBs). Zinc hexacyanoferrate (ZnHCF), a promising candidate of cathode materials for RAZIBs, suffers from severe electrochemical instability issues. This work reports using low contents of alkaline metal cations as electrolyte additives to improve the cycle performance of ZnHCF. The cations with large sizes, particularly Cs+, changes the intercalation chemistry of ZnHCF in RAZIBs. During cycling, Cs+ cations co-inserted into ZnHCF stabilize the host structure. Meanwhile, a stable phase of CsZn[Fe(CN)6] forms on the ZnHCF cathode, suppressing the loss of active materials through dissolution. ZnHCF gradually converts to an electrochemically inert Zn-rich phase during long-term cycling in aqueous electrolyte, leading to irreversible capacity loss. Introducing Cs+ in the electrolyte inhibits this conversion reaction, resulting in the extended lifespan. Owing to these advantages, the capacity retention rate of ZnHCF/Zn full batteries increases from the original 7.0 % to a high value of 54.6 % in the electrolyte containing 0.03 M of Cs2SO4 after 300 cycles at 0.25 A ⋅ g-1. This research provides an in-depth understanding of the electrochemical behavior of ZnHCF in aqueous zinc electrolyte, beneficial for further optimizing ZnHCF and other metal hexacyanoferrates.

The impact of frailty on clinical outcomes of older patients undergoing enhanced recovery after lumbar fusion surgery: a prospective cohort study.

BACKGROUND: Frailty is recognized as a surrogate for physiological age and has been established as a valid and independent predictor of postoperative morbidity, mortality, and complications. Enhanced recovery after surgery (ERAS) can enhance surgical safety by minimizing stress responses in frail patients, enabling surgeons to discharge patients earlier. However, the question of whether and to what extent the frailty impacts the post-ERAS outcomes in older patients remains. MATERIALS AND METHODS: An evidence-based ERAS program was implemented in our center from January 2019. This is a prospective cohort study of patients aged ≥75 years who underwent open transforaminal lumbar interbody fusion (TLIF) for degenerative spine disease from April 2019 to October 2021. Frailty was assessed with the Fried frailty scale (FP scale), and patients were categorized as non/prefrail (FP 0-2) or frail (FP ≥ 3). The preoperative variables, operative data, postoperative outcomes, and follow-up information were compared between the two groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for 90-day major complications and prolonged length of hospital stay after surgery. RESULTS: A total of 245 patients (age of 79.8±3.4 year) who had a preoperative FP score recorded and underwent scheduled TLIF surgery were included in the final analysis. Comparisons between nonfrail and prefrail/frail patients revealed no significant difference in age, sex, and surgery-related variables. Even after adjusting for multiple comparisons, the association between Fried frailty and ADL-dependency, IADL-dependency, and malnutrition remained significant. Preoperative frailty was associated with increased rates of postoperative adverse events. A higher CCI grade was an independent predictor for 90-day major complications, while Fried frailty and MNA-SF scores <12 were predictive of poor postoperative recovery. CONCLUSION: Frail older patients had more adverse post-ERAS outcomes after TLIF compared to non/prefrail older patients. Continued research and multidisciplinary collaboration will be essential to refine and optimize protocols for surgical care in frail older adults.