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Abstract Metal hydrides are crucial intermediates in numerous catalytic reactions. Intensive efforts have been dedicated to constructing molecular metal hydrides, where toxic precursors and delicate mediators are usually involved. Herein, we demonstrate a facile pressure-induced methodology to generate a cost-effective heterogeneous electrocatalytic metal hydride surface for sustainable hydrogen transfer. Taking carbon dioxide (CO2) electroreduction as a model system and zinc (Zn), a well-known carbon monoxide (CO)-selective catalyst, as a model catalyst, we showcase a homogeneous-type hydrogen atom transfer process induced by heterogeneous hydride surfaces, enabling direct hydrogenation pathways traditionally considered “prohibited”. Specifically, the maximal Faradaic efficiency for formate is enhanced by ~fivefold to 83% under ambient conditions. Experimental and theoretical analyses reveal that unlike the distal hydrogenation route for CO2 to CO over pristine Zn, the Zn hydride surface enables direct hydrogenation at the carbon site of CO2 to form formate. This work provides a promising material platform for sustainable synthesis.

Most of the literature on reviewer assignment problem (RAP) considers fitness or efficiency as the evaluation criterion of an assignment, e.g., the degree of matching between reviewers and proposals. In this study, we consider fairness of an assignment and establish two multi-objective linear integer programming models for proposal assignment. We introduce a definition of fairness based on the closeness of social relationships between scholars. The closeness of social relationships is quantified based on social networks among reviewers and serves as part of objective functions. An improved space-partitioning algorithm is designed to solve the multi-objective models with integer objectives. When the problem size is large, two meta-heuristic algorithms are applied. Experiments on real and randomly generated cases are provided to illustrate the effectiveness of the proposed models and algorithms.

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.

Objective: This study aims to elucidate and quantify the composition of Jiangtang Qingre formula (JQF), delineate the absorbed components in the bloodstream, predict the major biologically active components, and identify potential targets for the treatment of diabetes mellitus (DM). Materials and Methods: The chemical composition and metabolites of JQF were elucidated using ultra-high-performance liquid chromatography (UHPLC)-linear ion trap quadrupole-orbitrap high-resolution mass spectrometry (MS). The various components of JQF were concurrently determined using UHPLC-triple–quadrupole MS. Network pharmacological analysis was employed to explore the bioactive components and potential therapeutic targets in DM. Results: A total of 63 compounds were identified and provisionally characterized, with flavones, organic acids, and alkaloids emerging as the major chemical constituents. A robust analytical method that enables the simultaneous quantification of 24 representative components was successfully developed. The contents of 11 batches of samples were assessed. Ten prototype components were identified in rat plasma. The pathways associated with the efficacy of JQF in DM treatment were linked to signal transduction, endocrine and immune systems, lipid metabolism, and amino acid metabolism. Conclusion: This study systematically and comprehensively characterized the major chemical components and patterns in JQF, laying the groundwork for understanding its pharmacodynamic mechanisms and clinical applications.

Abstract Background To investigate the peripapillary retinal nerve fibre layer (RNFL) thickness changes and analyse factors associated with visual recovery of G11778A Leber hereditary optic neuropathy (LHON) patients. Methods Patients diagnosed with G11778A LHON between July 2017 and December 2020 in Tongji hospital were included in this follow-up study. Patients were grouped according to disease duration. Variations in the RNFL thickness in each quadrant at different disease stages were characterised using optical coherence tomography. According to the absence or presence of significant visual acuity improvements, LHON patients of disease duration ≥ 6 months were divided into two groups. A bivariate logistic regression model was constructed to analyse the potential factors associated with spontaneous visual recovery. Results This study included 56 G11778A LHON patients (112 eyes) and 25 healthy controls (50 eyes), with a mean follow-up of 5.25 ± 1.42 months. All quadrants and mean RNFL thicknesses of LHON patients first increased and then decreased, except for the temporal RNFL. As the disease progressed, RNFL thinning slowed; however, gradual RNFL thinning occurred. Logistic regression revealed that baseline best corrected visual acuity was related to spontaneous visual recovery of LHON patients with disease duration ≥ 6 months. Conclusion The pattern of RNFL involvement could be helpful in the differential diagnosis of LHON and other optic neuropathies. LHON patients with better vision are more likely to experience some degree of spontaneous visual acuity recovery after the subacute phase.

Abstract Plant-sucking insects have intricate associations with a diverse array of microorganisms to facilitate their adaptation to specific ecological niches. The midgut of phytophagous true bugs is generally structured into four distinct compartments to accommodate their microbiota. Nevertheless, there is limited understanding regarding the origins of these gut microbiomes, the mechanisms behind microbial community assembly, and the interactions between gut microbiomes and their insect hosts. In this study, we conducted a comprehensive survey of microbial communities within the midgut compartments of a bean bug Riptortus pedestris, soybean plant, and bulk soil across 12 distinct geographical fields in China, utilizing high-throughput sequencing of the 16 S rRNA gene. Our findings illuminated that gut microbiota of the plant-sucking insects predominantly originated from the surrounding soil environment, and plants also play a subordinate role in mediating microbial acquisition for the insects. Furthermore, our investigation suggested that the composition of the insect gut microbiome was probably shaped by host selection and/or microbe-microbe interactions at the gut compartment level, with marginal influence from soil and geographical factors. Additionally, we had unveiled a noteworthy dynamic in the acquisition of core bacterial taxa, particularly Burkholderia, which were initially sourced from the environment and subsequently enriched within the insect midgut compartments. This bacterial enrichment played a significant role in enhancing insect host reproduction. These findings contribute to our evolving understanding of microbiomes within the insect-plant-soil ecosystem, shedding additional light on the intricate interactions between insects and their microbiomes that underpin the ecological significance of microbial partnerships in host adaptation.

Abstract Background Leiomyosarcoma is a tumor that can develop in any organ that contains smooth muscles. Although leiomyosarcoma is common, its epididymal localization is quite rare. Case presentation A 79-year-old male Chinese Han patient presented with mild pain in the right groin and scrotum for 3 years concomitant with right scrotal swelling. Ultrasonography and magnetic resonance imaging of the scrotum showed a irregular and heterogeneous mass that was extratesticular. Right high orchiectomy was performed, and pathological examination of the resected specimen confirmed the diagnosis of leiomyosarcoma of the epididymis with surgical margins clear of tumor. Conclusion Epididymal leiomyosarcoma is rare and difficult to diagnose preoperatively. The final diagnosis of epididymal leiomyosarcoma requires histologic examination. Resection must be extensive and complete. The effect of chemotherapy and radiation on the epididymal leiomyosarcoma remains unclear. Recurrence is common, so follow-up is necessary.

Abstract Binding of Staphylococcus aureus protein A (SPA) to osteoblasts induces apoptosis and inhibits bone formation. Bone marrow-derived mesenchymal stem cells (BMSCs) have the ability to differentiate into bone, fat and cartilage. Therefore, it was important to analyze the molecular mechanism of SPA on osteogenic differentiation. We introduced transcript sequence data to screen out differentially expressed genes (DEGs) related to SPA-interfered BMSC. Protein–protein interaction (PPI) network of DEGs was established to screen biomarkers associated with SPA-interfered BMSC. Receiver operating characteristic (ROC) curve was plotted to evaluate the ability of biomarkers to discriminate between two groups of samples. Finally, we performed GSEA and regulatory analysis based on biomarkers. We identified 321 DEGs. Subsequently, 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) were identified by hubba algorithm in PPI. ROC analysis showed that six biomarkers could clearly discriminate between normal differentiated and SPA-interfered BMSC. Moreover, we found that these biomarkers were mainly enriched in the pyrimidine metabolism pathway. We also constructed '71 circRNAs-14 miRNAs-5 mRNAs' and '10 lncRNAs-5 miRNAs-2 mRNAs' networks. Kntc1 and Asf1b genes were associated with rno-miR-3571. Nek2 and Asf1b genes were associated with rno-miR-497-5p. Finally, we found significantly lower expression of six biomarkers in the SPA-interfered group compared to the normal group by RT-qPCR. Overall, we obtained 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap, and Kif14) related to SPA-interfered BMSC, which provided a theoretical basis to explore the key factors of SPA affecting osteogenic differentiation.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system’s reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient’s reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.

Understanding how stress hormones induce apoptosis in oviductal epithelial cells (OECs) and mural granulosa cells (MGCs) can reveal the mechanisms by which female stress impairs embryonic development and oocyte competence. A recent study showed that tissue plasminogen activator (tPA) ameliorates corticosterone-induced apoptosis in MGCs and OECs by acting on its receptors low-density lipoprotein receptor-related protein 1 (LRP1) and Annexin A2 (ANXA2), respectively. However, whether tPA is involved in corticotropin-releasing hormone (CRH)-induced apoptosis and whether it uses the same or different receptors to inhibit apoptosis induced by different hormones in the same cell type remains unknown. This study showed that CRH triggered apoptosis in both OECs and MGCs and significantly downregulated tPA expression. Moreover, tPA inhibits CRH-induced apoptosis by acting on ANXA2 in both OECs and MGCs. While ANXA2 inhibits apoptosis via phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling, LRP1 reduces apoptosis via mitogen-activated protein kinase (MAPK) signaling. Thus, tPA used the same receptor to inhibit CRH-induced apoptosis in both OECs and MGCs, however used different receptors to inhibit corticosterone-induced apoptosis in MGCs and OECs. These data helps understand the mechanism by which female stress impairs embryo/oocyte competence and proapoptotic factors trigger apoptosis in different cell types.