Potential shared pathogenic mechanisms between endometriosis and inflammatory bowel disease indicate a strong initial effect of immune factors.

Introduction: Over the past decades, immune dysregulation has been consistently demonstrated being common charactoristics of endometriosis (EM) and Inflammatory Bowel Disease (IBD) in numerous studies. However, the underlying pathological mechanisms remain unknown. In this study, bioinformatics techniques were used to screen large-scale gene expression data for plausible correlations at the molecular level in order to identify common pathogenic pathways between EM and IBD. Methods: Based on the EM transcriptomic datasets GSE7305 and GSE23339, as well as the IBD transcriptomic datasets GSE87466 and GSE126124, differential gene analysis was performed using the limma package in the R environment. Co-expressed differentially expressed genes were identified, and a protein-protein interaction (PPI) network for the differentially expressed genes was constructed using the 11.5 version of the STRING database. The MCODE tool in Cytoscape facilitated filtering out protein interaction subnetworks. Key genes in the PPI network were identified through two topological analysis algorithms (MCC and Degree) from the CytoHubba plugin. Upset was used for visualization of these key genes. The diagnostic value of gene expression levels for these key genes was assessed using the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) The CIBERSORT algorithm determined the infiltration status of 22 immune cell subtypes, exploring differences between EM and IBD patients in both control and disease groups. Finally, different gene expression trends shared by EM and IBD were input into CMap to identify small molecule compounds with potential therapeutic effects. Results: 113 differentially expressed genes (DEGs) that were co-expressed in EM and IBD have been identified, comprising 28 down-regulated genes and 86 up-regulated genes. The co-expression differential gene of EM and IBD in the functional enrichment analyses focused on immune response activation, circulating immunoglobulin-mediated humoral immune response and humoral immune response. Five hub genes (SERPING1、VCAM1、CLU、C3、CD55) were identified through the Protein-protein Interaction network and MCODE.High Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves for 5hub genes indicate the predictive ability for disease occurrence.These hub genes could be used as potential biomarkers for the development of EM and IBD. Furthermore, the CMap database identified a total of 9 small molecule compounds (TTNPB、CAY-10577、PD-0325901 etc.) targeting therapeutic genes for EM and IBD. Discussion: Our research revealed common pathogenic mechanisms between EM and IBD, particularly emphasizing immune regulation and cell signalling, indicating the significance of immune factors in the occurence and progression of both diseases. By elucidating shared mechanisms, our study provides novel avenues for the prevention and treatment of EM and IBD.

Analysis of clinical evidence on traditional Chinese medicine for the treatment of diabetic nephropathy: a comprehensive review with evidence mapping.

Objective: This study aims to map evidence from Randomized Controlled Trials (RCTs) and systematic reviews/Meta-analyses concerning the treatment of Diabetic Nephropathy (DN) with Traditional Chinese Medicine (TCM), understand the distribution of evidence in this field, and summarize the efficacy and existing problems of TCM in treating DN. The intention is to provide evidence-based data for TCM in preventing and treating DN and to offer a reference for defining future research directions. Methods: Comprehensive searches of major databases were performed, spanning from January 2016 to May 2023, to include clinical RCTs and systematic reviews/Meta-analyses of TCM in treating DN. The analysis encompasses the publishing trend of clinical studies, the staging of research subjects, TCM syndrome differentiation, study scale, intervention plans, and outcome indicators. Methodological quality of systematic reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist, and evidence distribution characteristics were analyzed using a combination of text and charts. Results: A total of 1926 RCTs and 110 systematic reviews/Meta-analyses were included. The majority of studies focused on stage III DN, with Qi-Yin deficiency being the predominant syndrome type, and sample sizes most commonly ranging from 60 to 100. The TCM intervention durations were primarily between 12-24 weeks. Therapeutic measures mainly consisted of Chinese herbal decoctions and patented Chinese medicines, with a substantial focus on clinical efficacy rate, TCM symptomatology, and renal function indicators, while attention to quality of life, dosage of Western medicine, and disease progression was inadequate. Systematic reviews mostly scored between 5 and 8 on the AMSTAR scale, and evidence from 94 studies indicated potential positive effects. Conclusion: DN represents a significant health challenge, particularly for the elderly, with TCM showing promise in symptom alleviation and renal protection. Yet, the field is marred by research inconsistencies and methodological shortcomings. Future investigations should prioritize the development of standardized outcome sets tailored to DN, carefully select evaluation indicators that reflect TCM's unique intervention strategies, and aim to improve the robustness of clinical evidence. Emphasizing TCM's foundational theories while incorporating advanced scientific technologies will be essential for innovating research methodologies and uncovering the mechanisms underlying TCM's efficacy in DN management.

DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

Rapid generation of high-quality structure figures for publication with PyMOL-PUB.

MOTIVATION: The advancement of structural biology has increased the requirements for researchers to quickly and efficiently visualize molecular structures in silico. Meanwhile, it is also time-consuming for structural biologists to create publication-standard figures, as no useful tools can directly generate figures from structure data. Although manual editing can ensure that figures meet the standards required for publication, it requires a deep understanding of software operations and/or program call commands. Therefore, providing interfaces based on established software instead of manual editing becomes a significant necessity. RESULTS: We developed PyMOL-PUB, based on the original design of PyMOL, to effectively create publication-quality figures from molecular structure data. It provides functions including structural alignment methods, functional coloring schemes, conformation adjustments, and layout plotting strategies. These functions allow users to easily generate high-quality figures, demonstrate structural differences, illustrate inter-molecular interactions, and predict performances of biomacromolecules. AVAILABILITY AND IMPLEMENTATION: Our tool is publicly available at https://github.com/BGI-SynBio/PyMOL-PUB.

Derivation and validation of a quantitative risk prediction model for weaning and extubation in neurocritical patients.

Background: Patients with severe neurological conditions are at high risk during withdrawal and extubation, so it is important to establish a model that can quantitatively predict the risk of this procedure. Methods: By analyzing the data of patients with traumatic brain injury and tracheal intubation in the ICU of the affiliated hospital of Hangzhou Normal University, a total of 200 patients were included, of which 140 were in the modeling group and 60 were in the validation group. Through binary logistic regression analysis, 8 independent risk factors closely related to the success of extubation were screened out, including age ≥ 65 years old, APACHE II score ≥ 15 points, combined chronic pulmonary disease, GCS score < 8 points, oxygenation index <300, cough reflex, sputum suction frequency, and swallowing function. Results: Based on these factors, a risk prediction scoring model for extubation was constructed with a critical value of 18 points. The AUC of the model was 0.832, the overall prediction accuracy was 81.5%, the specificity was 81.6%, and the sensitivity was 84.1%. The data of the validation group showed that the AUC of the model was 0.763, the overall prediction accuracy was 79.8%, the specificity was 84.8%, and the sensitivity was 64.0%. Conclusion: These results suggest that the extubation risk prediction model constructed through quantitative scoring has good predictive accuracy and can provide a scientific basis for clinical practice, helping to assess and predict extubation risk, thereby improving the success rate of extubation and improving patient prognosis.

Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model.

BACKGROUND: Breast cancer is a multifaceted and formidable disease with profound public health implications. Cell demise mechanisms play a pivotal role in breast cancer pathogenesis, with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence. AIM: To investigate the impact of ATP-induced cell death (AICD) on breast cancer, enhancing our understanding of its mechanism. METHODS: The foundational genes orchestrating AICD mechanisms were extracted from the literature, underpinning the establishment of a prognostic model. Simultaneously, a microRNA (miRNA) prognostic model was constructed that mirrored the gene-based prognostic model. Distinctions between high- and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized, with the aim of delineating common influence mechanisms, substantiated through enrichment analysis and immune infiltration assessment. RESULTS: The mRNA prognostic model in this study encompassed four specific mRNAs: P2X purinoceptor 4, pannexin 1, caspase 7, and cyclin 2. The miRNA prognostic model integrated four pivotal miRNAs: hsa-miR-615-3p, hsa-miR-519b-3p, hsa-miR-342-3p, and hsa-miR-324-3p. B cells, CD4+ T cells, CD8+ T cells, endothelial cells, and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways, while miRNA risk scores significantly enriched 29 signaling pathways, with 16 pathways being jointly enriched. CONCLUSION: Of paramount significance, distinct mRNA and miRNA signature models were devised tailored to AICD, both potentially autonomous prognostic factors. This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools, offering an unparalleled window for innovative interventions. Essentially, this paper reveals the hitherto enigmatic link between AICD and breast cancer, potentially leading to revolutionary progress in personalized oncology.

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application. Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103. Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells. Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.

Enhancing open clinical trials through blinded evaluations: an exploration with diabetic foot infections.

BACKGROUND: Blinding drugs through simulation techniques is an important means to control the subjective bias of investigators and subjects. However, clinical trials face significant challenges in the placebo production of drugs, and many trials cannot be double-blinded. OBJECTIVE: This study was conducted to ascertain the consistency between non-blind and blind evaluation results in clinical trials and to pioneer strategies to control information bias, particularly in trials where double-blinding is not feasible. METHODS: In this investigation, a randomized controlled trial (RCT) studying diabetic foot infections (DFIs) was utilized as a representative case. In this trial, the grading of DFIs, as per guidelines by the Infectious Disease Society of America (IDSA) and International Working Group on Diabetic Foot (IWGDF), was used as the primary efficacy indicator. A sample of sixteen patients was randomly chosen from the RCT, and DFI grading was assessed jointly by both non-blinded investigators and blinded center-reading investigators. A consistency test was then deployed to compare the evaluation results, forming the basis for our proposed strategies for effective blinded evaluation. In addition, other perspectives were collected at the end of this study, including with those involved in designing and conducting the recent blinded evaluation trial. RESULTS: Five subjects were excluded due to the quality of photos or the lack of post-treatment visits. The post-treatment IDSA/IWGDF grading results were compared in 11 subjects (experimental group=6, control group=5), and the consistency test showed inconsistent results between the non-blinded and center reading blinded evaluations (Kappa=0.248, p=0.384). In the experimental group, three cases were judged as grade 1 in the non-blinded evaluation and grade 2 in the central reading blinded evaluation; in the control group, three cases were judged as grade 2 in the non-blinded evaluation and grade 1 in the central reading blinded evaluation. The sum of these two cases in 22 post-treatment determinations was 27% (6/22). Furthermore, researchers propose several strategies for implementing blinded evaluations in clinical trials after this trial, which encompass aspects such as staff allocation, training, participant management, trial drug administration, efficacy indicator collection, and safety event management. CONCLUSIONS: The study highlighted that evaluations from non-blinded site investigators may potentially exaggerate the efficacy of the experimental group and that deep wounds can present challenges for observation via center-reading photos. These findings underline the vital necessity for objective assessment in open clinical trials, especially those where wound observation serves as the primary efficacy indicator. The study suggests the adoption of independent blinded investigators at each site, complemented by a comprehensive set of standard operating procedures for blinding evaluation. These measures could serve as an effective counterbalance to subjective bias, thereby augmenting the credibility and consistency of results in open clinical trials. The implications of these findings and recommendations could be of great significance for the design and execution of future open clinical trials, potentially bolstering the quality of clinical research in this area. TRIAL REGISTRATION: ChiCTR2000041443. Registered on December 2020.

A robust yeast biocontainment system with two-layered regulation switch dependent on unnatural amino acid.

Synthetic auxotrophy in which cell viability depends on the presence of an unnatural amino acid (unAA) provides a powerful strategy to restrict unwanted propagation of genetically modified organisms (GMOs) in open environments and potentially prevent industrial espionage. Here, we describe a generic approach for robust biocontainment of budding yeast dependent on unAA. By understanding escape mechanisms, we specifically optimize our strategies by introducing designed "immunity" to the generation of amber-suppressor tRNAs and developing the transcriptional- and translational-based biocontainment switch. We further develop a fitness-oriented screening method to easily obtain multiplex safeguard strains that exhibit robust growth and undetectable escape frequency (<~10-9) on solid media for 14 days. Finally, we show that employing our multiplex safeguard system could restrict the proliferation of strains of interest in a real fermentation scenario, highlighting the great potential of our yeast biocontainment strategy to protect the industrial proprietary strains.

Targeting polyketide synthase 13 for the treatment of tuberculosis.

Tuberculosis (TB) is one of the most threatening diseases for humans, however, the drug treatment strategy for TB has been stagnant and inadequate, which could not meet current treatment needs. TB is caused by Mycobacterial tuberculosis, which has a unique cell wall that plays a crucial role in its growth, virulence, and drug resistance. Polyketide synthase 13 (Pks13) is an essential enzyme that catalyzes the biosynthesis of the cell wall and its critical role is only found in Mycobacteria. Therefore, Pks13 is a promising target for developing novel anti-TB drugs. In this review, we first introduced the mechanism of targeting Pks13 for TB treatment. Subsequently, we focused on summarizing the recent advance of Pks13 inhibitors, including the challenges encountered during their discovery and the rational design strategies employed to overcome these obstacles, which could be helpful for the development of novel Pks13 inhibitors in the future.

Adenosine triphosphate induced cell death: Mechanisms and implications in cancer biology and therapy.

Adenosine triphosphate (ATP) induced cell death (AICD) is a critical cellular process that has garnered substantial scientific interest for its profound relevance to cancer biology and to therapeutic interventions. This comprehensive review unveils the intricate web of AICD mechanisms and their intricate connections with cancer biology. This review offers a comprehensive framework for comprehending the multifaceted role of AICD in the context of cancer. This is achieved by elucidating the dynamic interplay between systemic and cellular ATP homeostasis, deciphering the intricate mechanisms governing AICD, elucidating its intricate involvement in cancer signaling pathways, and scrutinizing validated key genes. Moreover, the exploration of AICD as a potential avenue for cancer treatment underscores its essential role in shaping the future landscape of cancer therapeutics.

ATP-induced cell death: a novel hypothesis for osteoporosis.

ATP-induced cell death has emerged as a captivating realm of inquiry with profound ramifications in the context of osteoporosis. This study unveils a paradigm-shifting hypothesis that illuminates the prospective involvement of ATP-induced cellular demise in the etiology of osteoporosis. Initially, we explicate the morphological attributes of ATP-induced cell death and delve into the intricacies of the molecular machinery and regulatory networks governing ATP homeostasis and ATP-induced cell death. Subsequently, our focus pivots towards the multifaceted interplay between ATP-induced cellular demise and pivotal cellular protagonists, such as bone marrow-derived mesenchymal stem cells, osteoblasts, and osteoclasts, accentuating their potential contributions to secondary osteoporosis phenotypes, encompassing diabetic osteoporosis, glucocorticoid-induced osteoporosis, and postmenopausal osteoporosis. Furthermore, we probe the captivating interplay between ATP-induced cellular demise and alternative modalities of cellular demise, encompassing apoptosis, autophagy, and necroptosis. Through an all-encompassing inquiry into the intricate nexus connecting ATP-induced cellular demise and osteoporosis, our primary goal is to deepen our comprehension of the underlying mechanisms propelling this malady and establish a theoretical bedrock to underpin the development of pioneering therapeutic strategies.

Sensitivity and specificity of magnetic resonance imaging in routine diagnosis of pulmonary lesions: a comparison with computed tomography.

Background: State-of-the-art thoracic magnetic resonance imaging (MRI) plays a complementary role in the assessment of pulmonary nodules/masses which potentially indicate to cancer. We aimed to evaluate the sensitivity and specificity of MRI in diagnosis of pulmonary nodules/masses. Methods: Sixty-eight patients with computed tomography (CT)-detected pulmonary nodules/masses underwent 3T MRI (T1-VIBE, T1-starVIBE, T2-fBLADE turbo spin-echo, and T2-SPACE). The detection rate was calculated for each of the different subgroups of pulmonary nodules according to lung imaging reporting and data system (Lung-RADS). The four MRI sequences were compared in terms of detection rate and image quality-signal to noise ratio (SNR), contrast to noise ratio (CNR) and 5-point scoring scale. Agreement of lesion size measurement between CT and MRI was assessed by intraclass correlation coefficient (ICC). The picture-SNR, lesion-SNR and CNR of each sequence were analyzed by Mann-Whitney U test. Results: In total, 232 pulmonary lesions were detected by CT. The CT showed 86 solid nodules (SNs) <6 mm, 15 SNs between 6-8 mm, 35 SNs between 8-15 mm, and 52 SNs between 15-30 mm. The T1-VIBE, T1-starVIBE, T2-fBLADE TSE and T2-SPACE sequences accurately detected 141 SNs (141/188, 75%/83.3%), 150 SNs (150/188, 79.8%/100%), 166 SNs (166/188, 88.3%/66.7%) and 169 SNs (169/188, 89.9%/53.3%), respectively. Four ground glass nodules (GGNs) (4/6) were detected by T2-fBLADE TSE. Twelve part-solid nodules (PSNs) (12/22) were detected by T1-VIBE and 20 PSNs (20/22) by T2-SPACE. A total of 100 lesions (2.2±1.4 cm, 0.8-7.3 cm) were accurately detected and measured by the four MRI sequences with ICC >0.96. The picture-SNR, lesion-SNR and CNR by T1-starVIBE were higher than those by T1-VIBE (P<0.001). The lesion-SNR and CNR by T2-fBLADE TSE were higher than those by T2-SPACE (P=0.006, 0.038). 86% of images by T1-starVIBE, 92% by T2-fBLADE TSE, 90% by T2-SPACE and 93% by T1-VIBE were scored 3 or more. Conclusions: MRI achieves high sensitivity and specificity for different type of pulmonary nodules detection and is an effective alternative to CT as a diagnostic tool for pulmonary nodules.

Study on complications of osteoporosis based on network pharmacology.

Osteoporosis is a serious threat to human life. Guben Zenggu Granule is an empirical prescription for clinical treatment of osteoporosis. MC3T3-E1 cells are mouse osteogenic precursor cells with osteogenic differentiation, and are classic cells for studying bone metabolism and osteogenic mechanism, as well as mechanical stimulation sensitive cells. Therefore, it can be inferred that Guben Zenggu granule can repair MC3T3-E1 cells under continuous static pressure overload. This study aims to through the network of pharmacology and gene sequencing method, reveal thrift increase bone particles under the condition of continuous static pressure overload on osteogenesis mechanism of MC3T3-E1 cells. In the process of analysis, from a variety of 98 compounds was predicted in the database, a collection of 474 goals, a total of 29,164 difference between two groups of genes. Then, construction of composite targets between cells and predict targets and protein - protein interaction networks, and through the cluster analysis to further explore the relationship between the target. In addition, linkages between target proteins and cells were further identified using Gene Ontology (GO) and Pathways (KEGG Pathway). Finally, the repair effect of Guben Zenggu granule on MC3T3-E1 cells under continuous static pressure overload was verified through experiments, so as to accurately explain the pharmacodynamic mechanism of Traditional Chinese medicine.

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation.

OBJECTIVES: To develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules. METHODS: 56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA). RESULTS: The nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73-0.91) in the training set and 0.71 (95% CI, 0.51-0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73-0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50-0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51-0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25-0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better. CONCLUSION: We constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Towards practical and robust DNA-based data archiving using the yin-yang codec system.

DNA is a promising data storage medium due to its remarkable durability and space-efficient storage. Early bit-to-base transcoding schemes have primarily pursued information density, at the expense of introducing biocompatibility challenges or decoding failure. Here we propose a robust transcoding algorithm named the yin-yang codec, using two rules to encode two binary bits into one nucleotide, to generate DNA sequences that are highly compatible with synthesis and sequencing technologies. We encoded two representative file formats and stored them in vitro as 200 nt oligo pools and in vivo as a ~54 kbps DNA fragment in yeast cells. Sequencing results show that the yin-yang codec exhibits high robustness and reliability for a wide variety of data types, with an average recovery rate of 99.9% above 104 molecule copies and an achieved recovery rate of 87.53% at ≤102 copies. Additionally, the in vivo storage demonstration achieved an experimentally measured physical density close to the theoretical maximum.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

Association of pet-keeping in home with self-reported asthma and asthma-related symptoms in 11611 school children from China.

OBJECTIVE: We conducted a cross-sectional study to investigate the associations between domestic pets and respiratory health in children. METHODS: We randomly recruited 11,611 school children from Zhongshan, a southern city in China. Information about the respiratory symptoms and disease history of the recruited children, the status of domestic pets, and other related risk factors were collected from March to July 2016. RESULTS: We identified cat-keeping at home increases the risk of persistent cough (OR, 1.77; 95%CI, 1.03-3.05); poultry-keeping at home increases the risk of current asthma (OR, 3.87; 95%CI, 1.08-13.92) and allergic rhinitis (OR, 1.84; 95%CI, 1.01-3.37); sleeping with pets increases the risk of persistent phlegm (OR, 5.04; 95%CI, 1.05-24.28), doctor-diagnosed asthma (OR, 3.35; 95%CI, 1.31-8.57) and current asthma (OR, 4.94; 95%CI, 1.05-23.31) in children. CONCLUSIONS: Cat-keeping and molds on the wall of the house had the multiplicative and additive interaction in doctor-diagnosed asthma. In conclusion, pet-keeping increased the risk of respiratory symptoms in children.

Pneumomediastinum and pneumoperitoneum on computed tomography after peroral endoscopic myotomy (POEM): postoperative changes or complications?

BACKGROUND: Many of the acute alterations after peroral endoscopic myotomy (POEM) may be of little clinical significance, while others may herald major clinical problems. The question whether pneumomediastinum/pneumoperitoneum is a normal postoperative finding after POEM, or should be regarded as a sign of a complication needs to be evaluated. Familiarity with these findings in computed tomography (CT) is essential for radiologists. PURPOSE: To evaluate whether or not pneumomediastinum/pneumoperitoneum detected by chest CT is a sign of a complication after POEM using CO2 insufflation for esophageal achalasia. MATERIAL AND METHODS: One hundred and eight patients with esophageal achalasia who underwent chest CT within 30 hours after POEM were included. CT findings were retrospectively reviewed by two radiologists in consensus. The correlation between pneumomediastinum and/or pneumoperitoneum shown on CT and the development of complications was analyzed. RESULTS: Abnormal findings were identified on post-treatment CT, including pneumomediastinum and/or pneumoperitoneum (53.7%, 58/108), pneumothorax (0.9%, 1/108), subcutaneous emphysema (29.6%, 32/108), pleural effusion (69.4%, 75/108), segmental atelectasis of lung tissue (29.6%, 32/108), minor inflammation of lungs (69.4%, 75/108), and ascites (0.9%, 1/108). Pneumomediastinum and pneumoperitoneum were observed simultaneously in 29 cases. The incidence rate of mild complications was high (79.6%, 86/108), while the rate of severe complications was low (2.8%, 3/108). There was no significant correlation between the occurrence of pneumomediastinum and/or pneumoperitoneum on CT and the development of complications (P = 0.542), or the development of severe complications including delayed hemorrhage, esophageal perforation, and retroperitoneal abscess. CONCLUSION: Pneumomediastinum and pneumoperitoneum detected by CT occur frequently after POEM and may be regarded as normal postoperative changes.

The application of anti-ESAT-6 monoclonal antibody fluorescent probe in ex vivo near-infrared fluorescence imaging in mice with pulmonary tuberculosis.

Here, we aimed to assess the feasibility of anti-ESAT-6 monoclonal antibody (mAb) coupling with IR783 and rhodamine fluorescent probe in the detection of ESAT-6 expression in tuberculosis tissue of mice using near-infrared fluorescence imaging. IR783 and rhodamine were conjugated to the anti-ESAT-6 mAb or IgG. Mice in the experimental group were injected with fluorescence-labeled mAb probe, and mice in the control group were injected with fluorescence-labeled non-specific IgG antibody. Twenty-four hours later, the lung tissue of mice was examined using ex vivo near-infrared fluorescence imaging. In addition, the contrast-to-noise ratio (CNR) was calculated by measuring the signal intensities of the pulmonary lesions, normal lung tissue and background noise. The frozen lung tissue section was examined under fluorescence microscopy and compared with hemoxylin and eosin (HE) staining. The ex vivo near-infrared fluorescence imaging showed that the fluorescence signal in the lung tuberculosis lesions in the experimental group was significantly enhanced, whereas there was only a weak fluorescence signal or even no fluorescence signal in the control group. CNR values were 64.40 ± 7.02 (n = 6) and 8.75 ± 3.87 (n = 6), respectively (t = 17.01, p < 0.001). The fluorescence accumulation distribution detected under fluorescence microscopy was consistent with HE staining of the tuberculosis region. In conclusion, anti-ESAT-6 mAb fluorescent probe could target and be applied in specific ex vivo imaging of mice tuberculosis, and may be of further use in tuberculosis in living mice.