Angiopterisnodosipetiolata (Marattiaceae), a new fern species from Yunnan, China.

Angiopterisnodosipetiolata Ting Wang tris, H.F.Chen & Y.H.Yan, a new fern of Marattiaceae, is described and illustrated. Morphologically, A.nodosipetiolata is similar to A.chingii with more than one naked pulvinus on the stipe and numerous jointed hairs on the undersides of the mature pinnae. However, the pinnae of A.nodosipetiolata are lanceolate and can reach up to 4-6 pairs, whereas they are elliptic and occur in 2-3 pairs in A.chingii. Phylogenetic and genetic distance analysis, based on the plastid genomes, also indicates that A.nodosipetiolata is not closely related to A.chingii. Currently, there are ca. 500 mature individuals in Gulinqing Nature Reserve and we suggest A.nodosipetiolata should be categorised as an Endangered (EN) species according to the criteria of IUCN.

3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.

A lightweight network model designed for alligator gar detection.

When using advanced detection algorithms to monitor alligator gar in real-time in wild waters, the efficiency of existing detection algorithms is subject to certain limitations due to turbid water quality, poor underwater lighting conditions, and obstruction by other objects. In order to solve this problem, we developed a lightweight real-time detection network model called ARD-Net, from the perspective of reducing the amount of calculation and obtaining more feature map patterns. We introduced a cross-domain grid matching strategy to accelerate network convergence, and combined the involution operator and dual-channel attention mechanism to build a more lightweight feature extractor and multi-scale detection reasoning network module to enhance the network's response to different semantics. Compared with the yoloV5 baseline model, our method performs equivalently in terms of detection accuracy, but the model is smaller, the detection speed is increased by 1.48 times, When compared with the latest State-of-the-Art (SOTA) method, YOLOv8, our method demonstrates clear advantages in both detection efficiency and model size,and has good real-time performance. Additionally, we created a dataset of alligator gar images for training.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.

Conceptualizing patient interactions eliciting empathy, compassion, reflection, and social projection: A model with analysis.

OBJECTIVES: Conceptualizing the next patient interaction is logical, essential, and largely done intuitively with limited literature. The first objective is to elicit student thought experiences to four questions. The secondary objective is to classify responses for respective questions and to review faculty assessments. METHODS: Forty-two students completed the exercise in the first clinical prosthodontics experience after a simulation course, in the fall of 2023. Four open-ended questions were 1) differentiation from the ideal, 2) desired outcome(s), 3) self-capabilities, and 4) consequences/prognosis. Nine different faculty assessed the exercise. RESULTS: 100% of students responded to all four questions and 83% of responses were judged by faculty to grasp the concept in the question. The authors categorized responses into natural categories for each question. Authors separately assigned responses to categories. The agreement rate was 90%. Little to no overlap in responses was observed among the four questions. The sequence of questions led students to thought experiences from empathy in Question #1, to compassion in Question #2, and to self-reflection in Question #3 to social projection in Question #4. CONCLUSIONS: The main objective was met by engaging students in thought-provoking responses to questions the experienced clinician asks of every patient encounter. The exercise elicited different kinds of thought experiences on four topics. The format was succinct with acceptance by students and faculty. The project has progressed from a concept some years ago to a recent pilot to full implementation with the current project. The next steps will be refinement and follow-up in some years. The project follows an emulation model for critical thinking.

Creating Atomically Iridium-Doped PdOx Nanoparticles for Efficient and Durable Methane Abatement.

The urgent environmental concern of methane abatement, attributed to its high global warming potential, necessitates the development of methane oxidation catalysts (MOC) with enhanced low-temperature activity and durability. Herein, an iridium-doped PdOx nanoparticle supported on silicalite-1 zeolite (PdIr/S-1) catalyst was synthesized and applied for methane catalytic combustion. Comprehensive characterizations confirmed the atomically dispersed nature of iridium on the surface of PdOx nanoparticles, creating an Ir4f-O-Pdcus microstructure. The atomically doped Ir transferred more electrons to adjacent oxygen atoms, modifying the electronic structure of PdOx and thus enhancing the redox ability of the PdIr/S-1 catalysts. This electronic modulation facilitated methane adsorption on the Pd site of Ir4f-O-Pdcus, reducing the energy barrier for C-H bond cleavage and thereby increasing the reaction rate for methane oxidation. Consequently, the optimized PdIr0.1/S-1 showed outstanding low-temperature activity for methane combustion (T50 = 276 °C) after aging and maintained long-term stability over 100 h under simulated exhaust conditions. Remarkably, the novel PdIr0.1/S-1 catalyst demonstrated significantly enhanced activity even after undergoing harsh hydrothermal aging at 750 °C for 16 h, significantly outperforming the conventional Pd/Al2O3 catalyst. This work provides valuable insights for designing efficient and durable MOC catalysts, addressing the critical issue of methane abatement.

Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer.

BACKGROUND: Colorectal surgeons are well aware that performing surgery for rectal cancer becomes more challenging in obese patients with narrow and deep pelvic cavities. Therefore, it is essential for colorectal surgeons to have a comprehensive understanding of pelvic structure prior to surgery and anticipate potential surgical difficulties. AIM: To evaluate predictive parameters for technical challenges encountered during laparoscopic radical sphincter-preserving surgery for rectal cancer. METHODS: We retrospectively gathered data from 162 consecutive patients who underwent laparoscopic radical sphincter-preserving surgery for rectal cancer. Three-dimensional reconstruction of pelvic bone and soft tissue parameters was conducted using computed tomography (CT) scans. Operative difficulty was categorized as either high or low, and multivariate logistic regression analysis was employed to identify predictors of operative difficulty, ultimately creating a nomogram. RESULTS: Out of 162 patients, 21 (13.0%) were classified in the high surgical difficulty group, while 141 (87.0%) were in the low surgical difficulty group. Multivariate logistic regression analysis showed that the surgical approach using laparoscopic intersphincteric dissection, intraoperative preventive ostomy, and the sacrococcygeal distance were independent risk factors for highly difficult laparoscopic radical sphincter-sparing surgery for rectal cancer (P < 0.05). Conversely, the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance was identified as a protective factor (P < 0.05). A nomogram was subsequently constructed, demonstrating good predictive accuracy (C-index = 0.834). CONCLUSION: The surgical approach, intraoperative preventive ostomy, the sacrococcygeal distance, and the anterior-posterior diameter of pelvic inlet/sacrococcygeal distance could help to predict the difficulty of laparoscopic radical sphincter-preserving surgery.

Progress in process parameters and mechanism research of polymer emulsion preparation.

As a new type of concrete admixture, polymer emulsion is mainly used to strengthen the properties of concrete by adhesion and physical and chemical crosslinking with cement in concrete. Under the background of construction in the new era, it is of great significance to elucidate all aspects of concrete performance under the action of polymer emulsion. In this paper, the main formation process of polymer emulsion is reviewed, the influence of synthetic materials required for polymerization on the polymerization process is discussed, and the regulating effects of reaction temperature, reaction time, admixtures, and treatment methods on the synthesis process of polymer emulsion are analyzed. The action mechanism of polymer emulsion on concrete was deeply investigated, and the synthesis method was studied to provide an important experimental and theoretical basis for the preparation of new emulsion materials and the process of emulsion polymerization. The problems of polymer emulsion raw materials, synthetic conditions, and synthetic methods are introduced. The future development trend of polymer emulsion is predicted and the future research ideas are put forward.

Single Atom-Dispersed Silver Incorporated in ZIF-8-Derived Porous Carbon for Enhanced Photothermal Activity and Antibacterial Activities.

Purpose: Recently, Single-atom-loaded carbon-based material is a new environmentally friendly and stable photothermal antibacterial nanomaterial. It is still a great challenge to achieve single-atom loading on carbon materials. Materials and Methods: Herein, We doped single-atom Ag into ZIF-8-derived porous carbon to obtain Ag-doped ZIF-8-derived porous carbon(AgSA-ZDPC). The as-prepared samples were characterized by XRD, XPS, FESEM, EDX, TEM, and HAADF-STEM which confirmed that the single-atom Ag successfully doped into the porous carbon. Further, the photothermal properties and antimicrobial activity of AgSA-ZDPC have been tested. Results: The results showed that the temperature increased by 30 °C after near-infrared light irradiation(1 W/cm2) for 5 min which was better than ZIF-8-derived porous carbon(ZDPC). It also exhibits excellent photothermal stability after the laser was switched on and off 5 times. When the AgSA-ZDPC concentration was greater than 50 µg/mL and the near-infrared irradiation was performed for 5 min, the growth inhibition of S. aureus and E. coli was almost 100%. Conclusion: This work provides a simple method for the preparation of single-atom Ag-doped microporous carbon which has potential antibacterial application.

A simple protocol to establish a conditionally immortalized mouse podocyte cell line.

Podocytes are specialized terminally differentiated cells in the glomerulus that are the primary target cells in many glomerular diseases. However, the current podocyte cell lines suffer from prolonged in vitro differentiation and limited survival time, which impede research progress. Therefore, it is necessary to establish a cell line that exhibits superior performance and characteristics. We propose a simple protocol to obtain an immortalized mouse podocyte cell (MPC) line from suckling mouse kidneys. Primary podocytes were cultured in vitro and infected with the SV40 tsA58 gene to obtain immortalized MPCs. The podocytes were characterized using Western blotting and quantitative real-time PCR. Podocyte injury was examined using the Cell Counting Kit-8 assay and flow cytometry. First, we successfully isolated an MPC line and identified 39 °C as the optimal differentiation temperature. Compared to undifferentiated MPCs, the expression of WT1 and synaptopodin was upregulated in differentiated MPCs. Second, the MPCs ceased proliferating at a nonpermissive temperature after day 4, and podocyte-specific proteins were expressed normally after at least 15 passages. Finally, podocyte injury models were induced to simulate podocyte injury in vitro. In summary, we provide a simple and popularized protocol to establish a conditionally immortalized MPC, which is a powerful tool for the study of podocytes.

Revealing intact neuronal circuitry in centimeter-sized formalin-fixed paraffin-embedded brain.

Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.

Transforming waste into valuables: Preparation and evaluation of dual-ligand hydrophobic charge-induction chromatography using two poor performing ligands.

In conventional chromatographic ligand screening, underperforming ligands are often dismissed. However, this practice may inadvertently overlook potential opportunities. This study aims to investigate whether these underperforming ligands can be repurposed as valuable assets. Hydrophobic charge-induction chromatography (HCIC) is chosen as the validation target for its potential as an innovative chromatographic mode. A novel dual-ligand approach is employed, combining two suboptimal ligands (5-Aminobenzimidazole and Tryptamine) to explore enhanced performance and optimization prospects. Various dual-ligand HCIC resins with different ligand densities were synthesized by adjusting the ligand ratio and concentration. The resins were characterized to assess appearance, functional groups, and pore features using SEM, FTIR, and ISEC techniques. Performance assessments were conducted using single-ligand mode resins as controls, evaluating the selectivity against human immunoglobulin G and human serum albumin. Static adsorption experiments were performed to understand pH and salt influence on adsorption. Breakthrough experiments were conducted to assess dynamic adsorption capacity of the novel resin. Finally, chromatographic separation using human serum was performed to evaluate the purity and yield of the resin. Results indicated that the dual-ligand HCIC resin designed for human antibodies demonstrates exceptional selectivity, surpassing not only single ligand states but also outperforming certain high-performing ligand types, particularly under specific salt and pH conditions. Ultimately, a high yield of 83.9 % and purity of 96.7 % were achieved in the separation of hIgG from human serum with the dual-ligand HCIC, significantly superior to the single-ligand resins. In conclusion, through rational design and proper operational conditions, the dual-ligand mode can revitalize underutilized ligands, potentially introducing novel and promising chromatographic modes.

Copper/O2-Mediated Oxidative C-C Activation of Nitriles for Selective Acylation-Bromination of Anilines.

This study reports selective dual amino acylation and C-H bromination of aniline compounds enabled by Cu/O2 catalyst systems. This method involves crucial oxidation-induced C-CN bond cleavage of α-methylene nitriles to generate an acylcyanide intermediate that is facilely intercepted by anilines. After amino acylation, the Cu(II) precatalyst in combination with NBS generates Cu(III)-Br in situ that engages in selective electrophilic para- or ortho-C-H bromination. The substrate scope, mechanistic aspects, and late-stage functionalization of biologically active anilines are studied. This study shows the synthetic potential of oxidative C-CN bond activation of nitriles for the development of valuable reactions.

Mental health and psychosocial interventions to limit the adverse psychological effects of disasters and emergencies in China: a scoping review.

Exposure to disasters and public health emergencies negatively affects mental health. Research documenting the psychosocial responses to these calamities in China increased dramatically after the 2008 Wenchuan earthquake. However, there is no comprehensive assessment of the available literature on China's mental health and psychosocial support (MHPSS) responses to these events. This scoping review systematically maps existing published research and grey literature sources regarding MHPSS to disasters and emergencies in China. We examined relevant literature in English and Chinese from six databases and official websites from Jan 1, 2000, to Aug 13, 2021, and included 77 full-text records in this review. The main types of interventions reported included a) stepped care intervention models, b) individual structured psychotherapy and pharmacotherapy, c) mental health education, d) psychological counselling, and e) government-based policy interventions. Most interventions were evaluated using quantitative methods that assessed the treatment of common mental disorders. The review found that rapid national mobilization, emphasis on resilience-strengthening interventions, and the widespread use of step-care models were essential components of reducing the adverse psychosocial effects of disasters. The review also identified remaining gaps, including a) a lack of integration of disaster-related services with the pre-existing health care system, b) inadequate supervision of MHPSS providers, and c) limited monitoring and evaluation of the services provided. These results show where additional research is needed in China to improve mental health services. It also provides a framework that other countries can adapt when developing and evaluating MHPSS policies and plans in response to disasters.

Development and validation of a nomogram to predict medication risk based on a knowledge, attitude and practice (KAP) survey of residents in Shanxi Province, China.

Objective: Unsafe medication practices and medication errors are a major cause of harm in healthcare systems around the world. This study aimed to explore the factors that influence the risk of medication and provide medication risk evaluation model for adults in Shanxi province, China. Methods: The data was obtained from the provincial questionnaire from May to December 2022, relying on the random distribution of questionnaires and online questionnaires by four hospitals in Shanxi Province. Multiple linear regression analysis was used to explore the factors affecting the KAP score of residents. Univariate and multivariate logistic regression was used to determine the independent risk factors, and the nomogram was verified by receiver operating characteristic curve, calibration and decision curve analysis. Results: A total of 3,388 questionnaires were collected, including 3,272 valid questionnaires. The average scores of drugs KAP were 63.2 ± 23.04, 33.05 ± 9.60, 23.67 ± 6.75 and 33.16 ± 10.87, respectively. On the evaluation criteria of the questionnaire, knowledge was scored "fair", attitude and practice were scored "good". Sex, monthly income, place of residence, insurance status, education level, and employment were regarded as independent risk factors for medication and a nomogram was established by them. Conclusion: Males, low-income, and low-educated people are important factors affecting the risk of medication. The application of the model can help residents understand the risk of their own medication behavior and reduce the harm of medication.

Population pharmacokinetics of adebrelimab - Support of alternative flat dose regimen in extensive-stage small-cell lung cancer.

Adebrelimab, a novel anti-PD-L1 antibody, has been approved by the National Medical Products Administration of China as an intravenous infusion for use in combination with carboplatin and etoposide as first-line treatment for extensive-stage small-cell lung cancer in 2023. A two-compartment model with empirical time-varying CL for adebrelimab was established based on data from 263 patients receiving body weight-based doses from two clinical studies. Significant covariate effects of baseline body weight, albumin levels, tumor size, neutrophil counts, and presence of anti-drug antibodies were identified on CL of debrelimab, none of which were clinically significant or warranted dose adjustment. The degree of decrease in CL was higher in patients who responded to treatment with adebrelimab than in non-responders. Adebrelimab exposures (AUC, Ctrough, or Cmax) were not identified as a statistically significant factor related to efficacy or safety endpoint in the exposure-response analysis. Distribution of simulated exposure metrics from the flat dose regimen (1200 mg q3w) was similar to the marketed weight-based dosing regimen (20 mg/kg q3w), supporting the alternative flat dose regimen in the clinic.

Overview of AlphaFold2 and breakthroughs in overcoming its limitations.

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.