Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models.

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method's effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs.

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights. However, they may neglect valuable heterophilic information or extract heterophilic information ineffectively, which could cause poor performance of downstream tasks on heterophilic graphs, including node classification and graph classification, etc. Hence, a novel framework named GARN is proposed to effectively extract both homophilic and heterophilic information. First, we analyze the shortcomings of most GNNs in tackling heterophilic graphs from the perspective of graph spectral and spatial theory. Then, motivated by these analyses, a Graph Aggregating-Repelling Convolution (GARC) mechanism is designed with the objective of fusing both low-pass and high-pass graph filters. Technically, it learns positive attention weights as a low-pass filter to aggregate similar adjacent nodes, and learns negative attention weights as a high-pass filter to repel dissimilar adjacent nodes. A learnable integration weight is used to adaptively fuse these two filters and balance the proportion of the learned positive and negative weights, which could control our GARC to evolve into different types of graph filters and prevent it from over-relying on high intra-class similarity. Finally, a framework named GARN is established by simply stacking several layers of GARC to evaluate its graph representation learning ability on both the node classification and image-converted graph classification tasks. Extensive experiments conducted on multiple homophilic and heterophilic graphs and complex real-world image-converted graphs indicate the effectiveness of our proposed framework and mechanism over several representative GNN baselines.

Bibliometric analysis of T-cells immunity in pulmonary hypertension from 1992 to 2022.

BACKGROUND: Adaptive immunity is an important disease mediator of pulmonary vascular remodeling during pulmonary hypertension (PH) development, especially T-cells lymphocytes. However, data for bibliometric analysis of T cell immunity in PH is currently vacant. This aimed to provide a comprehensive and visualized view of T-cells research in PH pathogenesis and to lay a solid foundation for further studies. METHODS: The data was acquired from the Web of Science Core Collection database. Web of Science analytic tool was used to analysis the publication years, authors, journals, countries, and organizations. CiteSpace 6.2.R3, VOSviewer 1.6.16, and Scimago Graphica 1.0.35.0 were applied to conduct a visualization bibliometric analysis about authors, countries, institutions, journals, references, and keywords. RESULTS: Nine hundred and eight publications from 1992 to 2022 were included in the analysis. The results showed that Humbert Marc was the most prolific author. American Journal of Physiology Lung Cellular and Molecular Physiology had the most related articles. The institution with the most articles was Udice French Research University. The United States was far ahead in the article output. Keywords analysis showed that "Pulmonary hypertension" was the most usually appeared keyword in the relevant literature, and included "T-cells", "Regulatory T cells", and "Activated T cell." "miRNA" of reference co-citation clustering analysis demonstrated the possible T-cell immunity activation mechanisms in PH. The most cited literature was published in the European Heart Journal by Galie N in 2016. The strongest citation burst of keyword is "gene expression" and terms such as "vascular remodeling," "growth," "proliferation," and "fibrosis" are among the list, indicating that T-cells interact with stromal vascular cells to induce pulmonary vascular remodeling. The strongest burst of cited reference is "Galie N, 2016." CONCLUSIONS: T-cell immunity is an important pathogenesis mechanism for PH development, which may have interaction with miRNAs and stromal vascular cells, but the possible T-cell immunity activation mechanisms in PH need to be investigated further.

3D-Printed SERS Chips for Highly Specific Detection of Denatured Type I and IV Collagens in Blood for Early Hepatic Fibrosis Diagnosis.

Hepatic fibrosis, the insidious progression of chronic liver scarring leading to life-threatening cirrhosis and hepatocellular carcinoma, necessitates the urgent development of noninvasive and precise diagnostic methodologies. Denatured collagen emerges as a critical biomarker in the pathogenesis of hepatic fibrosis. Herein, we have for the first time developed 3D-printed collagen capture chips for highly specific surface-enhanced Raman scattering (SERS) detection of denatured type I and type IV collagen in blood, facilitating the early diagnosis of hepatic fibrosis. Employing a novel blend of denatured collagen-targeting peptide-modified silver nanoparticle probes (Ag@DCTP) and polyethylene glycol diacrylate (PEGDA), we engineered a robust ink for the 3D fabrication of these collagen capture chips. The chips are further equipped with specialized SERS peptide probes, Ag@ICTP@R1 (S-I) and Ag@IVCTP@R2 (S-IV), tailored for the targeted detection of type I and IV collagen, respectively. The SERS chip platform demonstrated exceptional specificity and sensitivity in capturing and detecting denatured type I and IV collagen, achieving detection limits of 3.5 ng/mL for type I and 3.2 ng/mL for type IV collagen within a 10-400 ng/mL range. When tested on serum samples from hepatic fibrosis mouse models across a spectrum of fibrosis stages (S0-S4), the chips consistently measured denatured type I collagen and detected a progressive increase in type IV collagen concentration, which correlated with the severity of fibrosis. This novel strategy establishes a benchmark for the multiplexed detection of collagen biomarkers, enhancing our capacity to assess the stages of hepatic fibrosis.

Differences between long- and short-wavelength light-induced retinal damage and the role of PARP-1 in retinal injury induced by blue light.

Photobiomodulation (PBM) therapy uses light of different wavelengths to treat various retinal degeneration diseases, but the potential damage to the retina caused by long-term light irradiation is still unclear. This study were designed to detect the difference between long- and short-wavelength light (650-nm red light and 450-nm blue light, 2.55 mW/cm2, reference intensity in PBM)-induced injury. In addition, a comparative study was conducted to investigate the differences in retinal light damage induced by different irradiation protocols (short periods of repeated irradiation and a long period of constant irradiation). Furthermore, the protective role of PARP-1 inhibition on the molecular mechanism of blue light-induced injury was confirmed by a gene knockdown technique or a specific inhibitor through in vitro and in vivo experiments. The results showed that the susceptibility to retinal damage caused by irradiation with long- and short-wavelength light is different. Shorter wavelength lights, such as blue light, induce more severe retinal damage, while the retina exhibits better resistance to longer wavelength lights, such as red light. In addition, repeated irradiation for short periods induces less retinal damage than constant exposure over a long period. PARP-1 plays a critical role in the molecular mechanism of blue light-induced damage in photoreceptors and retina, and inhibiting PARP-1 can significantly protect the retina against blue light damage. This study lays an experimental foundation for assessing the safety of phototherapy products and for developing target drugs to protect the retina from light damage.

The TLR4/NF-κB/NLRP3 and Nrf2/HO-1 pathways mediate the neuroprotective effects of alkaloids extracted from Uncaria rhynchophylla in Parkinson's disease.

ETHNOPHARMACOLOGICAL RELEVANCE: Parkinson's disease (PD) is the second most common neurodegenerative disorder with limited therapeutic options available. Neuroinflammation plays an important role in the occurrence and development of PD. Alkaloids extracted from Uncaria rhynchophylla (URA), have emerged as a potential neuroprotective agent because of its anti-inflammatory and anti-oxidant properties. Nevertheless, the underlying mechanism by which URA exerts neuroprotective effects in PD remains obscure. AIM OF THE STUDY: The main aim of this study was to investigate the neuroprotective effects and underlying mechanism of URA in the treatment of PD through in vivo and in vitro models, focusing on the neuroinflammation and oxidative stress pathways. MATERIALS AND METHODS: The protective effects of URA against PD were evaluated by neurobehavioral tests, immunohistochemistry, serum biochemical assays, and real-time quantitative polymerase chain reaction in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mice. The role of the TLR4/NF-κB/NLRP3 pathway and the Nrf2/HO-1 pathway in URA-mediated effects was examined in lipopolysaccharide (LPS)-stimulated BV-2 microglial cells and a microglia-neuron coculture system. RESULTS: URA significantly alleviated motor deficits and dopaminergic neurotoxicity, and reversed the abnormal secretion of inflammatory and oxidative stress factors in the serum of MPTP-induced mice. URA suppressed the gene expression of Toll-like receptor 4 (TLR4), NOD-like receptor protein 3, and cyclooxygenase 2 (COX2) in the striatum of PD mice. Further studies indicated that URA inhibited activation of the TLR4/NF-κB/NLRP3 pathway and enhanced activation of the Nrf2/HO-1 pathway, reduced reactive oxygen species (ROS) production, and reversed the secretion of inflammatory mediators in LPS-stimulated BV-2 microglial cells, thereby alleviating neuroinflammatory damage to SH-SY5Y neuronal cells. CONCLUSION: URA exerted neuroprotective effects against PD mainly by the inhibition of the TLR4/NF-κB/NLRP3 pathway and activation of the Nrf2/HO-1 antioxidant pathway, highlighting URA as a promising candidate for PD treatment.

Expression of long non-coding RNA GAS5 by first trimester screening predicts the occurrence of gestational hypertension and pre-eclampsia.

AIMS: Hypertensive disorders of pregnancy (HDP) is a unique disease during gestational period, which is detrimental to pregnancy outcome. This study examined the clinical significance of long non-coding RNA GAS5 in gestational hypertension (GH) and preeclampsia (PE), aiming to explore potential biomarkers for the disease detection. METHODS: 180 pregnant women with HPD including 90 cases with GH and 90 cases with PE, and another 100 healthy pregnant women were enrolled. Serum GAS5 levels were measured by RT-qPCR method. The diagnostic performance of GAS5 was assessed in GH and PE through plotting receiver operating characteristic (ROC) curve. Logistic regression was applied for the identification of independent factors. RESULTS: Elevated serum GAS5 was identified in GH patients, and its diagnostic performance in discriminating GH cases from healthy people was determined by ROC curve. Serum GAS5 was positively associated with SBP, DBP, LDL-C and CRP values. Cases with PE had an increased serum GAS5 level relative to those with GH. Serum GAS5 was identified to be an independent predictor for PE, and can differentiate PE cases from GH ones. with a good diagnositc performance. Cases with high levels of serum GAS5 had a high risk of poor pregnancy outcomes. CONCLUSION: Elevated serum GAS5 could serve as an effective diagnostic biomarker in discriminating GH patients from healthy people by first trimester screening. Detection of serum GAS5 level has a certain predictive value for PE.

Simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues by multicolor quantum dots-based peptide probes.

The accurate detection of multiplex collagen biomarkers is vital for diagnosing and treating various critical diseases such as tumors and fibrosis. Despite the attractive optical properties of quantum dots (QDs), it remains technically challenging to create stable and specific QDs-based probes for multiplex biological imaging. We report for the first time the construction of multi-color QDs-based peptide probes for the simultaneous fingerprinting of multiplex collagen biomarkers in connective tissues. A bipeptide system composed of a glutathione (GSH) host peptide and a collagen-targeting guest peptide (CTP) has been developed, yielding CTP-QDs probes that exhibit exceptional luminescence stability when exposed to ultraviolet irradiation and mildly acidic conditions. The versatile bipeptide system allows for facile one-pot synthesis of high-quality multicolor CTP-QDs probes, exhibiting superior selectivity in targeting critical collagen biomarkers including denatured collagen, type I collagen, type II collagen, and type IV collagen. The multicolor CTP-QDs probes have demonstrated remarkable efficacy in simultaneously fingerprinting multiple collagen types in diverse connective tissues, irrespective of their status, whether affected by injury, diseases, or undergoing remodeling processes. The innovative multicolor CTP-QDs probes offer a robust toolkit for the multiplex fingerprinting of the collagen suprafamily, demonstrating significant potential in the diagnosis and treatment of collagen-related diseases.

Spatial multi-attention conditional neural processes.

Spatial prediction tasks are challenging when observed samples are sparse and prediction samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction tasks and have the advantage of measuring the uncertainty of the interpolation result. However, as the sample size increases, GPs suffer from significant overhead. Standard neural networks (NNs) provide a powerful and scalable solution for modeling spatial data, but they often overfit small sample data. Based on conditional neural processes (CNPs), which combine the advantages of GPs and NNs, we propose a new framework called Spatial Multi-Attention Conditional Neural Processes (SMACNPs) for spatial small sample prediction tasks. SMACNPs are a modular model that can predict targets by employing different attention mechanisms to extract relevant information from different forms of sample data. The task representation is inferred by measuring the spatial correlation contained in different sample points and the relationship contained in attribute variables, respectively. The distribution of the target variable is predicted by GPs parameterized by NNs. SMACNPs allow us to obtain accurate predictions of the target value while quantifying the prediction uncertainty. Experiments on spatial prediction tasks on simulated and real-world datasets demonstrate that this framework flexibly incorporates spatial context and correlation into the model, achieving state-of-the-art results in spatial small sample prediction tasks in terms of both predictive performance and reliability. For example, on the California housing dataset, our method reduces MAE by 8% and MSE by 7% compared to the second-best method. In addition, a spatiotemporal prediction task to forecast traffic speed further confirms the effectiveness and generality of our method.

Electroacupuncture attenuates middle cerebral artery occlusion-induced learning and memory impairment by regulating microglial polarization in hippocampus.

BACKGROUND: As a traditional medical therapy, electroacupuncture (EA) has been demonstrated to have beneficial effects on ischemic stroke-induced cognitive impairment. However, the underlying mechanism is largely unclear. METHODS: Adult rats received occlusion of the middle cerebral artery and reperfusion (MCAO/R) to establish the ischemic stroke model. Morris water maze test was performed following EA stimulation at the GV20, PC6, and KI1 acupoints in rats to test the learning and memory ability. Western blot, immunofluorescent staining, and enzyme-linked immunosorbent assay were conducted to assess the cellular and molecular mechanisms. RESULTS: EA stimulation attenuated neurological deficits. In the Morris water maze test, EA treatment ameliorated the MCAO/R-induced learning and memory impairment. Moreover, we observed that MCAO/R induced microglial activation and polarization in the ischemic hippocampus, whereas, EA treatment dampened microglial activation and inhibited M1 microglial polarization but enhanced M2 microglial polarization. EA treatment inhibited the increased expression of proinflammatory cytokines and enhanced the increased expression of anti-inflammatory cytokines. Finally, we found that EA treatment dampened microglial p38 mitogen-activated protein kinase (MAPK) phosphorylation. CONCLUSION: Collectively, our data suggested that EA treatment ameliorated cognitive impairment induced by MCAO/R and the underlying mechanism may be p38-mediated microglia polarization and neuroinflammation.

Case report: Successful treatment of advanced pulmonary sarcomatoid carcinoma with BUBIB-ALK rearrangement and KRAS G12C mutation by sintilimab combined with anlotinib.

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC) that is characterized by poor differentiation and invasiveness. According to the World Health Organization, PSC exhibits sarcoma or sarcomatoid differentiation and typically presents with an insidious onset, lacking specific symptoms and signs. It is associated with high malignancy, early metastasis, short survival time, and a poor prognosis. Treatment for PSC follows a similar approach to NSCLC; however, it presents significant challenges due to its high resistance to chemotherapy. Previous research has demonstrated the coexistence of two or more target mutations in PSC, and the presence of multiple mutations is correlated with higher mortality rates compared to single mutations. This is supported by our case study of a male patient with advanced BUBIB-ALK rearrangement and KRAS G12C missense mutation. There is currently no standard treatment protocol available for patients with this condition. The patient showed rapid progression after 1 month of alectinib treatment and was intolerant to paclitaxel + cisplatin chemotherapy. Following this, successful disease control was achieved with a combination therapy of sintilimab and anlotinib. The patient achieved a progression-free survival (PFS) of over 20 months, and long-term follow-up is still ongoing for the patient. Based on our clinical experience, the combination of anlotinib and programmed death-1 (PD-1) inhibitors may be a promising strategy for PSC patients, particularly those with multi-target mutations who do not respond to ALK-TKI and are resistant to chemotherapy.

Crossing the River by Feeling the Stones: Experiences with PD-1 Inhibitors in Geriatric Oncology People, a Case Report and Literature Review.

Currently, lung cancer remains one of the deadliest cancers, with a very high mortality rate, accounting for approximately 18% of all cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for 80% of all lung cancer deaths. In particular, elderly patients generally have poor tolerance to chemotherapy or cannot tolerate chemotherapy. This case analysis focuses on an elderly patient with non-small cell lung cancer stage IV. The patient was an 86-year-old female with poor nutritional status and low body weight (27 kg) and could not tolerate platinum-based dual-drug first-line chemotherapy. This patient had tumour cells in alveolar lavage fluid without conditions examined for pd-l1 expression. However, the efficacy of previous first-line immunotherapy was positive, and the patient and his family members agreed to apply it, so there was no contraindication to apply anlotinib + pembrolizumab. Results were reviewed after two cycles, and CR was used to evaluate the efficacy. After four cycles, the efficacy was evaluated as complete remission (CR), the patient developed immune-related side effects, immunotherapy was suspended, and maintenance therapy with anlotinib was used. The most recent review was in 2023-6-9, and PET/CT indicated that the patient had sustained CR. In general, this case provides support for the successful possibility of a treatment strategy for elderly patients with poor physical fitness who cannot tolerate platinum-based doublet chemotherapy and who have driver gene-negative squamous cell lung cancer (PS>0-1).

Glycemic load, but not glycemic index, is associated with an increased risk of ovarian cancer: A systematic review and meta-analysis.

The association between glycemic index (GI),glycemic load (GL) and ovarian cancer risk remains unclear. Carbohydrate intake promotes insulin secretion, leading to cell proliferation and invasion. We hypothesized that high GI and GL intake may increase ovarian cancer risk. Therefore, we conducted a meta-analysis after systematically searching PubMed, Embase, Web of Science, and Cochrane Library from inception to December 2022. Fixed- or random-effect models calculated the pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs). Subgroup, sensitivity, publication bias analysis, and dose-response analysis were performed. Nine original studies were included, involving 4716 cases and 119,960 controls. No significant association was observed between GI or GL and ovarian cancer risk (GI: RR = 1.02 [95% CI, 0.83-1.26]; GL: RR = 1.11 [95% CI, 0.84-1.47]). Subgroup analysis suggested the results were not significantly modified by any group. Sensitivity analysis identified the sources of heterogeneity. No publication bias was observed. A linear positive dose-response relationship was observed between dietary GL and ovarian cancer risk after removing heterogeneous sources (RR = 1.11 [95% CI, 1.05-1.17], I2 = 32.9%, P = .23 at 50 U/d; RR = 1.04 [95% CI, 1.02-1.07], I2 = 19.1%, P = .29 at 20 U/d). These outcomes suggest that high dietary GL, but not GI, is associated with significantly increased ovarian cancer risk. Thus, sufficient intake of a low dietary GL is important for reducing ovarian cancer risk.

[Retracted] MicroRNA­320 targets mitogen­activated protein kinase 1 to inhibit cell proliferation and invasion in epithelial ovarian cancer.

Following the publication of this paper, it was drawn to the Editor's attention by a concerned reader that certain of the Transwell cell invasion assay data shown in Fig. 5C on p. 8534 were strikingly similar to data that had already been published in different form in different articles written by different authors at different research institutes, or were submitted for publication at around the same time (several of which have now been retracted). Owing to the fact that some of the data in the above article had already been published prior to its submission to Molecular Medicine Reports, the Editor has decided that this paper should be retracted from the Journal. The authors were asked for an explanation to account for these concerns, but the Editorial Office did not receive a reply. The Editor apologizes to the readership for any inconvenience caused. [Molecular Medicine Reports 16: 8530­8536, 2017; DOI: 10.3892/mmr.2017.7664].

Shenling Baizhu Decoction (SLBZD) may play a synergistic role of tirelizumab in the treatment of colorectal cancer by influencing the imbalance of colon flora and Tumor microenvironment.

Objective: To explore whether SLBZD can play a synergistic role in promoting the efficacy of PD-1 inhibitors in the treatment of colorectal cancer by influencing the intestinal microenvironment and Tumor microenvironment. Method: Shenling Baizhu Decoction (SLBZD) and tirelizumab (TLzmab) treated the colorectal mouse model. The tumor growth rate, tumor weight, and tumor growth inhibition rate were evaluated. Fecal microbiota was detected by 16S rDNA sequencing and immune cell was detected by the flow cytometry analysis. Result: Compared to tumor weight, there exist significant differences between each group among the three groups. Compared to tumor volume, there was no statistically significant difference in tumor size between the control group and the TLzmab group at 7 days. However, there was a statistical difference in tumor size among the three groups at 18 days. By analyzing the diversity of the Gut microbiota, the diversity decreased after TLzmab treatment with a statistically significant difference. Compared with the control group, the diversity of the TLzmab+SLBZD group increased. The proportion of lymphocytes in the blood was analyzed by flow cytometry. Compared with the control group, Myeloid-derived suppressor cells (MDSCs) decreased and T regulatory cells (Treg) increased significantly in the TLzmab group. Compared with the control group and TLzmab group, the TLzmab+SLBZD group showed a significant increase in M1 type macrophages, while the M2 type macrophages, MDSCs, and Treg showed a significant decrease. Conclusion: An imbalance of Gut microbiota and imbalance of tumor immune microenvironment will occur during TLzmab treatment, which will lead to poor therapeutic effect of TLzmab or drug resistance. SLBZD will increase the abundance of Gut microbiota, which will lead to the increase of M1 macrophages in the tumor immune microenvironment and the decrease of M2 macrophages and Treg cells, thus exerting the synergistic effect of TLzmab. This study provides a new way to explore the improvement of ICIs through traditional Chinese medicine.

Bibliometric analysis of mucosal immunity in IgA nephropathy from 1990 to 2022.

OBJECTIVE: The study aimed to conduct a bibliometric analysis of mucosal immunity in IgA nephropathy (IgAN) and indicate its current status, hot sopts, and direction of future studies. METHODS: The literature data was collected from the Web of Science Core Collection. CiteSpace 6.1.R3 was employed to conduct a visualization bibliometric analysis of mucosal immunity in IgA nephropathy, including authors, countries, journals, keywords, organizations, references, the bursts of keywords and references, and the timeline of keyword clusters and reference clusters. RESULTS: A total of 315 publications from 1990 to 2022 were included. The number of articles in this field has increased in recent years. Suzuki H, Coppo R, and Feehally J took the first place parallelly with 18 articles. Japan contributes the most articles, accounting for 27.3% of all the publications. The institutions with the most publications were Juntendo University and University of Alabama Birmingham. 453 keywords were concluded in the analysis, which mainly focus on the mucosal pathogenesis and therapy of the IgAN. The top five co-cited reference cluster are "aberrantly glycosylated IgA," "corticosteroids," "animal models," "o-glycosylationm" and "microRNA-630." The most recently burst of keyword is "tonsillectomy" and "gut." CONCLUSION: This was the first bibliometric analysis to systematically analyze the mucosal immunity in IgAN, which obtained the current status and indicated the future research hotspots and development trends. The gut microbiota and the related therapy-targeted mucosal immunity might be the future research hotspot.

Association between normalized lactate load and in-hospital mortality in patients with acute myocardial infarction.

BACKGROUND: Lactate was a prognostic indicator for acute myocardial infarction (AMI) patients. However, the association between normalized lactate load, representing hypoxic burden over time, and in-hospital mortality remained uncertain. METHODS: The data for this study was obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.1) database. The normalized lactate load, describing the average intensity of hyperlactatemia, was calculated as the area under the curve (AUC) of lactate divided by time. 5882 AMI patients enrolled in this study were divided into survivor (n = 5015), and non-survivor group (n = 867). The primary endpoint was in-hospital mortality. Receiver operating characteristic (ROC) curves were generated to assess the predictive efficacy of normalized lactate load for in-hospital mortality, and areas under the curves of different parameters were compared using DeLong test. Multivariate binary logistic regression analysis was employed to explore the association between normalized lactate load and in-hospital mortality. The adjusting variables included age, gender, ethnicity, heart rate, systolic blood pressure, congestive heart failure, shock, dyslipidemia, cardiac arrest, cerebrovascular disease, neutrophil, lymphocyte, creatinine, blood nitrogen urea, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB), statins, dialysis, extracorporeal membrane oxygenation (ECMO), the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPS II). Restricted cubic spline (RCS) was conducted to evaluate nonlinear associations of normalized lactate load with in-hospital mortality. RESULTS: The overall in-hospital mortality rate was 14.7%. After adjusting for confounding variables, normalized lactate load was independently associated with increased risk of in-hospital mortality (Normalized lactate load≥2.6 vs Normalized lactate load<2.6: OR, 95% CI: 1.56, 1.27-1.93). The RCS demonstrated a positive linear relationship between normalized lactate load and in-hospital mortality (non-linear p = 0.725). ROC curves showed that normalized lactate load was better than first lactate, maximum lactate, and mean lactate in predicting in-hospital mortality, but lower than SOFA and SAPS II. Among participants with at least nine lactate measures, normalized lactate load showed predictive performance comparable to SOFA and SAPS II. CONCLUSION: Normalized lactate load can be used to predict the prognosis of in-hospital mortality in AMI patients, and its prediction performance increases with the increase of lactate measurement.

Prognostic nutritional index and serum lactate dehydrogenase predict the prognosis of nasopharyngeal carcinoma patients who received intensity-modulated radiation therapy.

PURPOSE: This research aimed to evaluate the prognostic significance of baseline prognostic nutritional index (PNI) and lactate dehydrogenase (LDH) for the outcome of individuals diagnosed with non-metastatic nasopharyngeal carcinoma (NPC). METHODS: A retrospective analysis was conducted on data from 810 patients with non-metastatic NPC who underwent intensity-modulated radiation therapy (IMRT) with or without chemotherapy. The best cut-offs for PNI and LDH were identified by X-tile software to be 48.5 and 150, respectively. To find the independent prognostic factors for survival outcomes, univariate and multivariate regression analyses were conducted, and AUCs were used to compare their prognostic values. RESULTS: Multivariate analysis revealed that patients with PNI > 48.5 had better overall survival (OS) (HR: 0.502, P < 0.001), progression-free survival (PFS) (HR: 0.618, P < 0.001), and distant metastasis-free survival (DMFS) (HR: 0.637, P = 0.005). Higher LDH was associated with poorer OS (HR: 1.798, P < 0.001), PFS (HR: 1.671, P < 0.001), and DMFS (HR: 1.756, P < 0.001). The combination of low PNI and high LDH in non-metastatic NPC patients was correlated with poor OS (P < 0.001), PFS (P < 0.001), and DMFS (P < 0.001). The combination of PNI and LDH had the highest AUCs for predicting OS, PFS, and DMFS. CONCLUSIONS: PNI and LDH might become valuable predictors of the prognosis of non-metastatic NPC patients undergoing IMRT with or without chemotherapy. Prognostic accuracy can be enhanced by combining PNI and LDH.

CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition.

Recently, view-based approaches, which recognize a 3D object through its projected 2-D images, have been extensively studied and have achieved considerable success in 3D object recognition. Nevertheless, most of them use a pooling operation to aggregate viewwise features, which usually leads to the visual information loss. To tackle this problem, we propose a novel layer called capsule attention layer (CAL) by using attention mechanism to fuse the features expressed by capsules. In detail, instead of dynamic routing algorithm, we use an attention module to transmit information from the lower level capsules to higher level capsules, which obviously improves the speed of capsule networks. In particular, the view pooling layer of multiview convolutional neural network (MVCNN) becomes a special case of our CAL when the trainable weights are chosen on some certain values. Furthermore, based on CAL, we propose a capsule attention convolutional neural network (CACNN) for 3D object recognition. Extensive experimental results on three benchmark datasets demonstrate the efficiency of our CACNN and show that it outperforms many state-of-the-art methods.

Ca2+ addition facilitates the shell repair with eggs production of Pomacea canaliculata through biomineralization and food intaking regulation.

Pomacea canaliculata was by far one of the most harmful invasive organisms in the world, causing serious harm to aquatic crops and ecosystem. Calcium carbonate is a common component of aquatic environment, which is important for the growth of Pomacea canaliculata. Therefore, the objective of this study was to investigate the response characteristics of P. canaliculata suffered shell breakage to the addition of calcium carbonate in water environment. In this experiment, we explored the effects of calcium carbonate addition on the P. canaliculata shell repair rate, food intake, egg production, shell strength, and calcium content through breaking the snails shell and the addition of calcium carbonate treatment. The results showed that snail broken-shell repaired mostly within 21 days. The snails experienced a significant increase in shell repair rates during earlier days of the treatment, especially for female snails. Food intake of snails exhibited different patterns when their shells were broken and calcium carbonate was added. Shell breakage treatment combined with calcium carbonate addition significantly increased the diameter of snail eggs compared with the control and the calcium carbonate addition treatment without shell-broken snail group. There was no significant difference in shell strength or calcium content of male snails between the treatments. The study suggests that P. canaliculata exhibits a sex-dependent response pattern when subjected to shell damage and calcium carbonate addition. The findings can provide some references to better understand the invasion mechanism and survival strategy of the P. canaliculata.