Pulmonary Langerhans cell histiocytosis presenting as an uncommon mass in the lung: A rare case report and literature review.

A comprehensive diagnostic approach, including immunohistochemistry, is crucial for confirming pulmonary Langerhans cell histiocytosis in adults. Individualized treatment with dynamically adjusted chemotherapy based on therapeutic response leads to significant absorption of lesions and symptom alleviation. Regular follow-up and timely treatment adjustments according to the patient's condition are essential in managing this rare disease.

Gastrodin Alleviates DSS-Induced Colitis in Mice through Strengthening Intestinal Barrier and Modulating Gut Microbiota.

Inflammatory bowel diseases (IBDs) are commonly associated with dysfunctional intestinal barriers and disturbed gut microbiota. Gastrodin, a major bioactive ingredient of Gastrodia elata Blume, has been shown to exhibit anti-oxidation and anti-inflammation properties and could mitigate non-alcoholic fatty liver disease, but its role in modulating IBD remains elusive. The aim of this study was to investigate the impact of gastrodin on DSS-induced colitis in mice and explore its potential mechanisms. Gastrodin supplementation alleviated clinical symptoms such as weight loss, a shortened colon, and a high disease activity index. Meanwhile, gastrodin strengthened the intestinal barrier by increasing the 0expression of tight junction proteins and mucin. Furthermore, Gastrodin significantly reduced pro-inflammatory cytokine secretion in mice by downregulating the NF-κB and MAPK pathways. Gut microbiota analysis showed that gastrodin improved the DSS-disrupted microbiota of mice. These findings demonstrate that gastrodin could attenuate DSS-induced colitis by enhancing the intestinal barrier and modulating the gut microbiota, providing support for the development of a gastrodin-based strategy to prevent or combat IBD.

Alginate oligosaccharide improves 5-fluorouracil-induced intestinal mucositis by enhancing intestinal barrier and modulating intestinal levels of butyrate and isovalerate.

Chemotherapy-induced mucositis (CIM) is the typical side effect of chemotherapy. This study investigates the potential of alginate oligosaccharide (AOS) in ameliorating CIM induced by 5-fluorouracil (5-FU) in a murine model and its underlying mechanisms. AOS effectively mitigated body weight loss and histopathological damage, modulated inflammatory cytokines and attenuated the oxidative stress. AOS restored intestinal barrier integrity through enhancing expression of tight junction proteins via MLCK signaling pathway. AOS alleviated intestinal mucosal damage by inhibiting TLR4/MyD88/NF-κB signaling pathway, downregulating the pro-apoptotic protein Bax and upregulating the anti-apoptotic protein Bcl-2. Moreover, AOS significantly enriched intestinal Akkermansiaceae and increased the production of short-chain fatty acids (SCFAs), most notably butyrate and isovalerate. Pre-treatment with butyrate and isovalerate also alleviated 5-FU-induced CIM. In conclusion, AOS effectively mitigated CIM through strenghthening intestinal barrier, attenuating inflammation, and modulating gut microbiota and intestianl levels of butyrate and isovalerate. These finding indicate that AOS could be potentially utilized as a supplemental strategy for prevention or mitigation of CIM.

DSCR1-1 attenuates osteoarthritis-associated chondrocyte injury by regulating the CREB1/ALDH2/Wnt/ß-catenin axis: An in vitro and in vivo study.

The progression of osteoarthritis (OA) includes the initial inflammation, subsequent degradation of the extracellular matrix (ECM), and chondrocyte apoptosis. Down syndrome candidate region 1 (DSCR1) is a stress-responsive gene and expresses in varied types of cells, including chondrocytes. Bioinformatics analysis of GSE103416 and GSE104739 datasets showed higher DSCR1 expression in the inflamed cartilage tissues and chondrocytes of OA. DSCR1 had two major isoforms, isoform 1 (DSCR1-1) and isoform 4 (DSCR1-4). We found that DSCR1-1 had a faster (in vitro) and higher expression (in vivo) response to OA compared to DSCR1-4. IL-1ß-induced apoptosis, inflammation, and ECM degradation in chondrocytes were attenuated by DSCR1-1 overexpression. DSCR1-1 triggered the phosphorylation of cAMP response element-binding 1 (CREB1) at 133 serine sites by decreasing calcineurin activity. Moreover, activated CREB1 moved into the cell nucleus and combined in the promoter regions of aldehyde dehydrogenase 2 (ALDH2), thus enhancing its gene transcription. ALDH2 could recover Wnt/ß-catenin signaling transduction by enhancing phosphorylation of ß-catenin at 33/37 serine sites and inhibiting the migration of ß-catenin protein from the cellular matrix to the nucleus. In vivo, adenoviruses (1 × 108 PFU) overexpressing DSCR1-1 were injected into the articular cavity of C57BL/6 mice with medial meniscus surgery-induced OA, and it showed that DSCR1-1 overexpression ameliorated cartilage injury. Collectively, our study demonstrates that DSCR1-1 may be a potential therapeutic target of OA.

Enhancing antimicrobial resistance detection with MetaGeneMiner: Targeted gene extraction from metagenomes.

BACKGROUND: Accurately and efficiently extracting microbial genomic sequences from complex metagenomic data is crucial for advancing our understanding in fields such as clinical diagnostics, environmental microbiology, and biodiversity. As sequencing technologies evolve, this task becomes increasingly challenging due to the intricate nature of microbial communities and the vast amount of data generated. Especially in intensive care units (ICUs), infections caused by antibiotic-resistant bacteria are increasingly prevalent among critically ill patients, significantly impacting the effectiveness of treatments and patient prognoses. Therefore, obtaining timely and accurate information about infectious pathogens is of paramount importance for the treatment of patients with severe infections, which enables precisely targeted anti-infection therapies, and a tool that can extract microbial genomic sequences from metagenomic dataset would be of help. METHODS: We developed MetaGeneMiner to help with retrieving specific microbial genomic sequences from metagenomes using a k-mer-based approach. It facilitates the rapid and accurate identification and analysis of pathogens. The tool is designed to be user-friendly and efficient on standard personal computers, allowing its use across a wide variety of settings. We validated MetaGeneMiner using eight metagenomic samples from ICU patients, which demonstrated its efficiency and accuracy. RESULTS: The software extensively retrieved coding sequences of pathogens Acinetobacter baumannii and herpes simplex virus type 1 and detected a variety of resistance genes. All documentation and source codes for MetaGeneMiner are freely available at https://gitee.com/sculab/MetaGeneMiner. CONCLUSIONS: It is foreseeable that MetaGeneMiner possesses the potential for applications across multiple domains, including clinical diagnostics, environmental microbiology, gut microbiome research, as well as biodiversity and conservation biology. Particularly in ICU settings, MetaGeneMiner introduces a novel, rapid, and precise method for diagnosing and treating infections in critically ill patients. This tool is capable of efficiently identifying infectious pathogens, guiding personalized and precise treatment strategies, and monitoring the development of antibiotic resistance, significantly impacting the diagnosis and treatment of severe infections.

Surface nanocoating-based universal platform for programmed delivery of microorganisms in complicated digestive tract.

Microbial therapies have promising applications in the treatment of a broad range of diseases. However, effective colonization of the target region by therapeutic microorganisms remains a significant challenge owing to the complexity of the intestinal system. Here, we developed surface nanocoating-based universal platform (SNUP), which enabled the manipulation of controlled release and targeted colonization of therapeutic microbes in the digestive tract without the utilization of any targeting molecules. The system controlled the decomposition time of SNUP in the gut by regulating different modification layers and modification sequences on the microorganism's surface, so that the microorganism was released at a predetermined time and space. With the SNUP nanomodification technology, we could effectively deliver therapeutic microorganisms to specific complex intestinal regions such as the small intestine and colon, and protect the bioactivity of therapeutic microorganisms from destruction by both strong acids and digestive enzymes. In this study, we found that two layers SNUP-encapsulated Liiliilactobacillus salivarius (LS@CCMC) could efficiently colonize the small intestine and significantly improve the symptoms of a mouse model of Parkinson's disease through sustained secretion of γ-aminobutyric acid (GABA). This surface nanocoating-based universal platform system does not require the design of specific targeting molecules, providing a simple and universal method for colonized microbial therapy, target theranostics, precision medicine, and personalized medicine.

Effect of SlSAHH2 on metabolites in over-expressed and wild-type tomato fruit.

Background: Tomato (Solanum lycopersicum) is an annual or perennial herb that occupies an important position in daily agricultural production. It is an essential food crop for humans and its ripening process is regulated by a number of genes. S-adenosyl-l-homocysteine hydrolase (AdoHcyase, EC 3.3.1.1) is widespread in organisms and plays an important role in regulating biological methylation reactions. Previous studies have revealed that transgenic tomato that over-express SlSAHH2 ripen earlier than the wild-type (WT). However, the differences in metabolites and the mechanisms driving how these differences affect the ripening cycle are unclear. Objective: To investigate the effects of SlSAHH2 on metabolites in over-expressed tomato and WT tomato. Methods: SlSAHH2 over-expressed tomato fruit (OE-5# and OE-6#) and WT tomato fruit at the breaker stage (Br) were selected for non-targeted metabolome analysis. Results: A total of 733 metabolites were identified by mass spectrometry using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Human Metabolome database (HMDB). The metabolites were divided into 12 categories based on the superclass results and a comparison with the HMDB. The differences between the two databases were analyzed by PLS-DA. Based on a variable important in projection value >1 and P < 0.05, 103 differential metabolites were found between tomato variety OE-5# and WT and 63 differential metabolites were found between OE-6# and WT. These included dehydrotomatine, L-serine, and gallic acid amongst others. Many metabolites are associated with fruit ripening and eight common metabolites were found between the OE-5# vs. WT and OE-6# vs. WT comparison groups. The low L-tryptophan expression in OE-5# and OE-6# is consistent with previous reports that its content decreases with fruit ripening. A KEGG pathway enrichment analysis of the significantly different metabolites revealed that in the OE-5# and WT groups, up-regulated metabolites were enriched in 23 metabolic pathways and down-regulated metabolites were enriched in 11 metabolic pathways. In the OE-6# and WT groups, up-regulated metabolites were enriched in 29 pathways and down-regulated metabolites were enriched in six metabolic pathways. In addition, the differential metabolite changes in the L-serine to flavonoid transformation metabolic pathway also provide evidence that there is a phenotypic explanation for the changes in transgenic tomato. Discussion: The metabolomic mechanism controlling SlSAHH2 promotion of tomato fruit ripening has been further elucidated.

Gut microbiota and D-ribose mediate the anti-colitic effect of punicalagin in DSS-treated mice.

Background: Inflammatory bowel disease (IBD) is an increasing health burden worldwide. Punicalagin, a bioactive component rich in pomegranate rind, has been shown to attenuate chemical or bacteria-induced experimental colitis in mice, but whether punicalagin exerts its function through modulating gut microbiota and metabolites remains unexplored. Results: Punicalagin (100 mg per kg per day) administered orally to mice alleviated dextran-sodium sulfate (DSS)-induced colitis. Gut microbiota analyzed by 16S rRNA sequencing showed that punicalagin altered gut microbiota by increasing the Lachnospiraceae_NK4A136_group and Bifidobacterium abundance. To evaluate the effect of punicalagin-modulated microbiota and its metabolites in colitis mice, we transplanted fecal microbiota and sterile fecal filtrate (SFF) to mice treated with oral antibiotics. The results of fecal microbiota transplantation (FMT) demonstrated that punicalagin's anti-colitic effect is transferable by transplanting punicalagin-modulated gut microbiota and its metabolites. Additionally, we discovered that punicalagin-modulated sterile fecal filtrate also exhibits anti-colitis effects, as evidenced by improved intestinal barrier integrity and decreased inflammation. Subsequently, fecal metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). The analysis revealed that punicalagin significantly increased the level of D-ribose. In vitro experiments showed that D-ribose has both anti-inflammatory and antioxidant properties. Furthermore, D-ribose significantly mitigated DSS-induced colitis symptoms in mice. Conclusions: Overall, this study demonstrated that gut microbiota and its metabolites partly mediate the protective effect of punicalagin against DSS-induced colitis in mice. D-ribose is a key metabolite that contributes to the anti-colitic effect of punicalagin in mice.

PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B-cell lymphoma patients.

Objectives: This study aims to develop 7×7 machine-learning cross-combinatorial methods for selecting and classifying radiomic features used to construct Radiomics Score (RadScore) of predicting the mid-term efficacy and prognosis in high-risk patients with diffuse large B-cell lymphoma (DLBCL). Methods: Retrospectively, we recruited 177 high-risk DLBCL patients from two medical centers between October 2012 and September 2022 and randomly divided them into a training cohort (n=123) and a validation cohort (n=54). We finally extracted 110 radiomic features along with SUVmax, MTV, and TLG from the baseline PET. The 49 features selection-classification pairs were used to obtain the optimal LASSO-LASSO model with 11 key radiomic features for RadScore. Logistic regression was employed to identify independent RadScore, clinical and PET factors. These models were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was conducted to assess the predictive power of the models. The prognostic power of RadScore was assessed using cox regression (COX) and Kaplan-Meier plots (KM). Results: 177 patients (mean age, 63 ± 13 years,129 men) were evaluated. Multivariate analyses showed that gender (OR,2.760; 95%CI:1.196,6.368); p=0.017), B symptoms (OR,4.065; 95%CI:1.837,8.955; p=0.001), SUVmax (OR,2.619; 95%CI:1.107,6.194; p=0.028), and RadScore (OR,7.167; 95%CI:2.815,18.248; p<0.001) independently contributed to the risk factors for predicting mid-term outcome. The AUC values of the combined models in the training and validation groups were 0.846 and 0.724 respectively, outperformed the clinical model (0.714;0.556), PET based model (0.664; 0.589), NCCN-IPI model (0.523;0.406) and IPI model (0.510;0.412) in predicting mid-term treatment outcome. DCA showed that the combined model incorporating RadScore, clinical risk factors, and PET metabolic metrics has optimal net clinical benefit. COX indicated that the high RadScore group had worse prognosis and survival in progression-free survival (PFS) (HR, 2.1737,95%CI: 1.2983, 3.6392) and overall survival (OS) (HR,2.1356,95%CI: 1.2561, 3.6309) compared to the low RadScore group. KM survival analysis also showed the same prognosis prediction as Cox results. Conclusion: The combined model incorporating RadScore, sex, B symptoms and SUVmax demonstrates a significant enhancement in predicting medium-term efficacy and prognosis in high-risk DLBCL patients. RadScore using 7×7 machine learning cross-combinatorial methods for selection and classification holds promise as a potential method for evaluating medium-term treatment outcome and prognosis in high-risk DLBCL patients.

Intestinal linoleic acid contributes to the protective effects of Akkermansia muciniphila against Listeria monocytogenes infection in mice.

Akkermansia muciniphila pretreatment mitigated Listeria monocytogenes infection in mice. A. muciniphila improved gut microbiota disturbed by L. monocytogenes infection and significantly increased the level of intestinal linoleic acid in mice. Linoleic acid strengthened the intestinal epithelial barrier and reduced pathogen translocation partly by regulating NF-κB/MLCK pathway in a GPR40-dependent manner.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

BACKGROUND: Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. METHODS: This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. RESULTS: A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. CONCLUSIONS: In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

SemiSynBio: A new era for neuromorphic computing.

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Mapping the neural mechanism that distinguishes between holistic thinking and analytic thinking.

Holistic and analytic thinking are two distinct modes of thinking used to interpret the world with relative preferences varying across cultures. While most research on these thinking styles has focused on behavioral and cognitive aspects, a few studies have utilized functional magnetic resonance imaging (fMRI) to explore the correlations between brain metrics and self-reported scale scores. Other fMRI studies used single holistic and analytic thinking tasks. As a single task may involve processing in spurious low-level regions, we used two different holistic and analytic thinking tasks, namely the frame-line task and the triad task, to seek convergent brain regions to distinguish holistic and analytic thinking using multivariate pattern analysis (MVPA). Results showed that brain regions fundamental to distinguish holistic and analytic thinking include the bilateral frontal lobes, bilateral parietal lobes, bilateral precentral and postcentral gyrus, bilateral supplementary motor areas, bilateral fusiform, bilateral insula, bilateral angular gyrus, left cuneus, and precuneus, left olfactory cortex, cingulate gyrus, right caudate and putamen. Our study maps brain regions that distinguish between holistic and analytic thinking and provides a new approach to explore the neural representation of cultural constructs. We provide initial evidence connecting culture-related brain regions with language function to explain the origins of cultural differences in cognitive styles.

Efficacy of stem cell therapy in patients with chronic liver disease: an umbrella review of systematic reviews.

BACKGROUND: Stem cell therapy offers promising benefits like modulating immune responses, reducing inflammation, and aiding liver regeneration. This umbrella review seeks to compile evidence from systematic reviews to assess the efficacy of stem cell therapy for improving liver function and survival rates in chronic liver disease patients. METHODS: We searched electronic databases up to February 15, 2024. The selection process focused on systematic reviews comparing stem cell therapy with standard care or a placebo. The primary outcomes evaluated were changes in liver enzymes, the MELD score, and survival rates. Nested Knowledge software was utilized for screening and data extraction. All statistical analyses were performed using R software, version 4.3. RESULTS: Our umbrella review included 28 systematic reviews. The meta-analysis showcased a notable improvement in survival rates with a pooled RR of 1.487 (95% CI: 1.281 to 1.727). In non-randomized studies, albumin levels exhibited an SMD of 0.786 (95% CI: 0.368 to 1.204), indicating positive therapeutic effects. For ALT, the meta-analysis revealed a decrease in levels with an SMD of -0.499 (95% CI: -0.834 to -0.164), and for AST, an overall SMD of -0.362 (95% CI: -0.659 to -0.066) was observed, suggesting hepatoprotective effects. No significant changes were observed in total bilirubin levels and MELD scores in RCTs. CONCLUSION: Stem cell therapy exhibits potential as a novel treatment for chronic liver diseases, as it has demonstrated improvements in survival rates and certain liver function markers. More high-quality RCTs are needed in the future to fully ascertain the efficacy of stem cell therapy in this patient population.

Diffusion-Based Generative Network for de Novo Synthetic Promoter Design.

Computer-aided promoter design is a major development trend in synthetic promoter engineering. Various deep learning models have been used to evaluate or screen synthetic promoters, but there have been few works on de novo promoter design. To explore the potential ability of generative models in promoter design, we established a diffusion-based generative model for promoter design in Escherichia coli. The model was completely driven by sequence data and could study the essential characteristics of natural promoters, thus generating synthetic promoters similar to natural promoters in structure and component. We also improved the calculation method of FID indicator, using a convolution layer to extract the feature matrix of the promoter sequence instead. As a result, we got an FID equal to 1.37, which meant synthetic promoters have a distribution similar to that of natural ones. Our work provides a fresh approach to de novo promoter design, indicating that a completely data-driven generative model is feasible for promoter design.

Prognosis Prediction of Diffuse Large B-Cell Lymphoma in 18F-FDG PET Images Based on Multi-Deep-Learning Models.

Diffuse large B-cell lymphoma (DLBCL), a cancer of B cells, has been one of the most challenging and complicated diseases because of its considerable variation in clinical behavior, response to therapy, and prognosis. Radiomic features from medical images, such as PET images, have become one of the most valuable features for disease classification or prognosis prediction using learning-based methods. In this paper, a new flexible ensemble deep learning model is proposed for the prognosis prediction of the DLBCL in 18F-FDG PET images. This study proposes the multi-R-signature construction through selected pre-trained deep learning models for predicting progression-free survival (PFS) and overall survival (OS). The proposed method is trained and validated on two datasets from different imaging centers. Through analyzing and comparing the results, the prediction models, including Age, Ann abor stage, Bulky disease, SUVmax, TMTV, and multi-R-signature, achieve the almost best PFS prediction performance (C-index: 0.770, 95% CI: 0.705-0.834, with feature adding fusion method and C-index: 0.764, 95% CI: 0.695-0.832, with feature concatenate fusion method) and OS prediction (C-index: 0.770 (0.692-0.848) and 0.771 (0.694-0.849)) on the validation dataset. The developed multiparametric model could achieve accurate survival risk stratification of DLBCL patients. The outcomes of this study will be helpful for the early identification of high-risk DLBCL patients with refractory relapses and for guiding individualized treatment strategies.

Clinical and ultrasound features of 46 children with suppurative osteoarthritis: experience from two centers.

OBJECTIVE: Diagnosing musculoskeletal infections in children is challenging. In recent years, with the advancement of ultrasound technology, high-resolution ultrasound has unique advantages for musculoskeletal children. The aim of this work is to summarize the ultrasonographic and clinical characteristics of children with pyogenic arthritis and osteomyelitis. This study provides a simpler and more effective diagnostic basis for clinical treatment. METHODS: Fifty children with osteomyelitis or arthritis were diagnosed via ultrasound, and the results of the ultrasound diagnosis were compared with those of magnetic resonance imaging and surgery. Clinical and ultrasound characteristics were also analyzed. RESULTS: Out of 50 patients, 46 were confirmed to have suppurative infection by surgical and microbiological examination. Among these 46 patients, 26 were diagnosed with osteomyelitis and 20 had arthritis. The manifestations of osteomyelitis were subperiosteal abscess (15 patients), bone destruction (17 patients), bone marrow abscess (9 patients), and adjacent joint abscess (13 patients). Osteomyelitis mostly affects the long bones of the limbs, femur and humerus (10 and 9 patients, respectively), followed by the ulna, radius, tibia and fibula (one patient each). The manifestations of arthritis were joint pus (20 patients) and joint capsule thickening (20 patients), and hip dislocation (8 patients). All the patients had arthritis involving the hip joint. CONCLUSION: Subperiosteal abscess, bone destruction, and joint abscess with dislocation are ultrasonographic features of pyogenic osteoarthritis. The findings of this work can improve the early diagnosis and differentiation of pyogenic osteoarthritis and provide a reliable basis for treatment.

Polygonatum sibiricum Saponin Prevents Immune Dysfunction and Strengthens Intestinal Mucosal Barrier Function in Cyclophosphamide-Induced Immunosuppressed BALB/c Mice.

The aim of this study was to explore the immunomodulatory effect of Polygonatum sibiricum saponin (PS) in a cyclophosphamide-induced (Cy) immunosuppression mice model. Oral administration of PS by gavage effectively alleviated weight loss caused by Cy and increased the index of immune organs. PS promoted the proliferation of splenic lymphocytes and T cell subsets (CD3+, CD355+, CD4+/CD8+) and relieved the xylene-induced inflammatory response and Cy-induced increase of serum hemolysin. Moreover, PS increased serum levels of lactate dehydrogenase and acid phosphatase. PS elevated serum level of cytokines and immunoglobulins (TNF-α, IFN-γ, IL-4, IL-6, IL-ß, SIgA, and IgG) and the expression of mRNA of IL-10, TNF-α, and IL-6 in the spleen. Increased mRNA expression of tight junction protein (ZO-1, Mucin2, Occludin) expression and protein expression of IL-6/MyD88/TLR4 in the small intestine showed that PS exhibited a restorative effect on intestinal mucosal injury caused by cyclophosphamide. Oral PS prevented Cy-induced decline in leukocytes, red blood cells, lymphocytes, hemoglobin concentrations, and neutrophils, providing evidence for alleviating hematopoietic disorders. In addition, PS increased SOD and NO levels, reduced MDA levels, and improved oxidative damage in the liver. These findings demonstrate that PS has the potential to be developed as a supplemental agent for alleviating immunosuppression caused by chemotherapeutic agents.

Management of pulmonary arterial hypertension：before, during and after pregnancy.

Pregnancy complicated with pulmonary arterial hypertension (PAH) is a severe and dangerous condition for both the mother and the fetus. Pregnancy-specific alterations in the maternal cardiovascular system suggest that PAH in pregnancy may manifest more severe symptoms compared with those in non-pregnant patients. Although most societal guidelines recommend early termination in the case of PAH, some recent data suggests that maternal mortality among patients with PAH is lower than previously observed and suggests if a woman decides to proceed with the pregnancy, she should be counseled about the potential risks of continuing with the pregnancy. This review paper starts with a real clinical case of PAH complicating with pregnancy, then summarizes the clinical features, diagnosis, and risk stratification. Effective treatments were also clarified, including pre-conception counseling and monitoring, general and supportive care, medication and immune therapy, delivery and postpartum care, counseling on contraception and breastfeeding, maternal and fetal outcomes, and cardiac surgery. The article summarizes points of uncertainty in both laboratory and clinical practices, as well as current guidelines and clinical recommendations.

Underwater image enhancement using Divide-and-Conquer network.

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net.