Identification of macrophage differentiation related genes and subtypes linking atherosclerosis plaque processing and metabolic syndrome via integrated bulk and single-cell sequence analysis.

Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.

Molecular characterisation of Histoplasma capsulatum sensu lato from Ethiopian horses reveals two distinct phylogenetic clades.

Epizootic lymphangitis (EL) is a highly prevalent and contagious infectious disease affecting horses in many parts of Ethiopia caused by Histoplasma capsulatum sensu lato ('var. farciminosum'). In this study, 12 suspected isolates of H. capsulatum sensu lato or yeasts unidentified by conventional biochemical tests isolated from Ethiopian horses with EL were characterised by ITS sequencing. Six of the 12 isolates were identified to be members of H. capsulatum sensu lato and the other six were Pichia kudriavzevii (synonym: Candida krusei) (n = 3), Trichosporon asahii (n = 1), Geotrichum silvicola (n = 1) and Moesziomyces aphidis (n = 1), respectively. The six H. capsulatum sensu lato isolates were further characterised by multilocus sequence analysis. Four distinct gene loci [arf (462 bases), H-anti (410 bases), ole1 (338 bases) and tub1 (272 bases)] of these six isolates as well as those of two H. capsulatum sensu lato ('var. farciminosum') reference strains (ATCC 58332 and ATCC 28798) were PCR-amplified and sequenced. Phylogenetic analyses of their concatenated nucleotide sequences showed that three of the isolates and the reference strain ATCC 58332 were identical and belonged to the Eurasia clade within Latin American (LAm) A (H. suramericanum), and those of the other three isolates and the reference strain ATCC 28798 were identical and belonged to the Africa clade. At least two distinct phylogenetic clades of Histoplasma capsulatum sensu lato were circulating in Ethiopian horses with EL. Advanced molecular technologies and bioinformatics tools are crucial for accurate identification and typing of pathogens as well as discovery of novel microorganisms in veterinary microbiology.

Using multilocus sequence analysis with four concatenated housekeeping gene loci, at least two distinct phylogenetic clades, namely Eurasia clade and Africa clade, of Histoplasma capsulatum sensu lato were confirmed to be circulating in Ethiopian horses with epizootic lymphangitis.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

The genetic diversity and evolution analysis of the Hainan melioidosis outbreak strains.

Melioidosis is a zoonotic disease, with its outbreaks being rare and indicative of an unusual concurrence of extreme climate and natural environmental factors. An outbreak of melioidosis cases emerged in Hainan following Typhoon "Dianmu" from October to December 2021, presenting an opportunity to identify the environmental sources of infection for these cases due to its nature as a well-defined point-source cluster. To investigate the relationship between the occurrence of these melioidosis cases and the environment, we extracted the entire genome of 25 clinical strains and conducted MLST typing, followed by whole genome sequencing and analysis of molecular genetic information for four ST46 genotypes from these strains. Phylogenetic and evolutionary relationships between Hainan sequence types (STs) and those found in other endemic regions were analyzed using IslandPath-DIMO, PHASTER, e-BURST, PHYLOViZ, and the maximum likelihood method. Notably, a total of 25 clinical strains were identified, encompassing 12 STs (ST46, ST1105, ST1991, ST30, ST1992, ST50, ST164, ST55, ST70, ST1993, ST1545, and ST58), with ST1991, ST1992, and ST1993 being newly discovered subtypes. PHYLOViZ clustering analysis divided the strains into two groups (A and B), both closely related to the Asian region. Phylogenetic tree analysis further revealed that most of the strains in this study were closely related to those found in Australia and Thailand. Analysis of patient information and visits to their residences suggested that contaminated water sources might be the primary source of infection during this outbreak. Our findings underscore that extreme weather events, such as typhoons, significantly increase the infection rate of B. pseudomallei, along with its genetic diversity, necessitating additional prevention strategies to control these B. pseudomallei infections.

Maternal Innate Immune Reprogramming After Complicated Pregnancy.

PROBLEM: Preeclampsia (PE) and fetal growth restriction (FGR) are often associated with maternal inflammation and an increased risk of cardiovascular and metabolic disease in the affected mothers. The mechanism responsible for this increased risk of subsequent disease may involve reprogramming of innate immune cells, characterized by epigenetic modifications. METHOD OF STUDY: Circulating monocytes from women with PE, FGR, or uncomplicated pregnancies (control) were isolated before labor. Cytokine release from monocytes following exposure to lipopolysaccharide (LPS) and the presence of lysine 4-trimethylated histone 3 (H3K4me3) within TNF promoter sequences were evaluated. Single-cell transcriptomic profiles of circulating monocytes from women with PE or uncomplicated pregnancies were assessed. RESULTS: Monocytes from women with PE or FGR exhibited increased IL-10 secretion and decreased IL-1ß and GM-CSF secretion in response to LPS. While TNFα secretion was not significantly different in cultures of control monocytes versus those from complicated pregnancies with or without LPS exposure, monocytes from complicated pregnancies had significantly decreased levels of H3K4me3 associated with TNF promoter sequences. Cluster quantification and pathway analysis of differentially expressed genes revealed an increased proportion of anti-inflammatory myeloid cells and a lower proportion of inflammatory non-classical monocytes among the circulating monocyte population in women with PE. CONCLUSIONS: Monocytes from women with PE and FGR exhibit an immune tolerance phenotype before initiation of labor. Further investigation is required to determine whether this tolerogenic phenotype persists after the affected pregnancy and contributes to increased risk of subsequent disease.

Efficient prediction of anticancer peptides through deep learning.

Background: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides. Objective: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods. Methods: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model's effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences. Conclusion: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment. Future Work: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model's predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Francisella sciaenopsi sp. nov. isolated from diseased red drum Sciaenops ocellatus in Florida, USA.

Piscine francisellosis is one of the most important bacterial diseases affecting various fish species worldwide. Francisella orientalis, F. noatunensis, and F. salimarina (F. marina) have been reported as etiological agents of disease in fish. A Francisella sp. was isolated from several diseased red drum Sciaenops ocellatus experiencing morbidity in Florida, USA, in 2008. In this study, molecular and phenotypic characterization of the recovered isolate was conducted. Phenotypically, the isolate showed a biochemical reaction profile distinct from that of F. orientalis and F. salimarina. Although the 16S rRNA sequence of this isolate shared 99.61% identity to the type strain of F. philomiragia O#319LT, whole genome analysis (average nucleotide identity <95%; digital DNA-DNA hybridization <70%) and a multilocus sequence analysis of 8 concatenated housekeeping genes in comparison with other Francisella spp. indicated that this isolate was a novel Francisella species, more closely related to F. orientalis. Immersion, intracoelomic injection, and co-habitation challenges using a Nile tilapia Oreochromis niloticus fingerling model of infection were done to investigate virulence in a piscine model. Variably pigmented granulomas and pigmented macrophage aggregates were observed in the kidneys and spleens of the challenged fish, but no mortality was recorded during the 15 d challenge period, suggesting that this novel Francisella sp. might be an opportunistic pathogen of fish. Based on the phenotypic and genotypic differences from other Francisella spp. observed in this study, we propose the name Francisella sciaenopsi sp. nov. for this novel isolate.

Protocol for analysis of single-cell sequencing data by Seqtometry.

Seqtometry (sequencing-to-measurement) is an analytical platform for single-cell analysis based on direct profiling of gene expression and accessibility achieved by advanced scoring with gene signatures. Here, we present a protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis using Seqtometry. We describe steps for preprocessing, imputation, scoring, and plotting, with extensions to large datasets and integration of multiple datasets. This protocol yields results in the form of biologically interpretable dimensions for direct identification and comprehensive characterization of specific cells. For complete details on the use and execution of this protocol, please refer to Kousnetsov et al.1.

Work-Family Life Course Trajectories and Women's Mental Health: The Moderating Role of Defamilization Policies in 15 European Territories.

This study employs multichannel sequence analysis of data from the Survey of Health, Ageing, and Retirement in Europe to explore variations in the association between work-family life trajectories and women's mental health across European cohorts born between 1924 and 1965 within different policy contexts. It finds that trajectories characterized by prolonged employment and delayed familial commitments are generally associated with increased depressive symptoms. Notably, the strength of this association varies significantly across cohorts and is notably moderated by defamilization policies. These policies, which aim to reduce dependency on family for managing social risks, buffer mental health challenges in traditional family roles but are less effective for women in trajectories with delayed family formation. This investigation highlights the nuanced ways in which historical and cultural contexts alongside policy environments shape mental health outcomes at various life stages, offering valuable insights into our understanding of health disparities across the life course, with an emphasis on exposure to changing institutions.

Molecular Identification and Survey of Cyclospora spp. in Cattle in Shanxi Province, North China.

To date, more than 20 species in the genus Cyclospora have been reported. Among them, Cyclospora cayetanensis has been recognized as the causative agent of human cyclosporiasis, which is characterized by severe intestinal injury and prolonged diarrhea in patients with immune dysfunction. The presence of C. cayetanensis in cattle has been confirmed. To date, however, no surveillance data are available on the occurrence and prevalence of Cyclospora spp. in cattle in Shanxi Province, North China. In the present study, a total of 761 fecal samples collected from cattle in three representative counties (Qi, Jishan, and Shanyin) in this Province were examined for Cyclospora spp. by using a polymerase-chain-reaction-restriction-fragment-length polymorphism (PCR-RFLP) test based on the nuclear small subunit ribosomal RNA (SSU rRNA) gene. The prevalence of Cyclospora spp. in cattle was 2.1%, and region, age, sex, and breed were not identified to be risk factors. Molecular evolutionary analysis based on the SSU rRNA sequences revealed that all 12 of the isolates were relatively distant from the human pathogen C. cayetanensis; seven isolates were grouped with Cyclospora colobi, whereas the others were grouped with cattle Cyclospora spp. reported previously. Though C. cayetanensis was not detected in cattle in the present study, more investigations should be performed in human populations, other animal species, or cattle from other regions of Shanxi Province and other environmental sources from the One Health perspective.

GENet: A Graph-Based Model Leveraging Histone Marks and Transcription Factors for Enhanced Gene Expression Prediction.

Understanding the regulatory mechanisms of gene expression is a crucial objective in genomics. Although the DNA sequence near the transcription start site (TSS) offers valuable insights, recent methods suggest that analyzing only the surrounding DNA may not suffice to accurately predict gene expression levels. We developed GENet (Gene Expression Network from Histone and Transcription Factor Integration), a novel approach that integrates essential regulatory signals from transcription factors and histone modifications into a graph-based model. GENet extends beyond simple DNA sequence analysis by incorporating additional layers of genetic control, which are vital for determining gene expression. Our method markedly enhances the prediction of mRNA levels compared to previous models that depend solely on DNA sequence data. The results underscore the significance of including comprehensive regulatory information in gene expression studies. GENet emerges as a promising tool for researchers, with potential applications extending from fundamental biological research to the development of medical therapies.

Protocol for the construction and functional profiling of metagenome-assembled genomes for microbiome analyses.

Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.

Quantifying periodontitis-associated oral dysbiosis in tongue and saliva microbiomes-An integrated data analysis.

BACKGROUND: Periodontitis is primarily driven by subgingival biofilm dysbiosis. However, the quantification and impact of this periodontal dysbiosis on other oral microbial niches remain unclear. This study seeks to quantify the dysbiotic changes in tongue and salivary microbiomes resulting from periodontitis by applying a clinically relevant dysbiosis index to an integrated data analysis. METHODS: The National Center for Biotechnology Information (NCBI) database was searched to identify BioProjects with published studies on salivary and tongue microbiomes of healthy and periodontitis subjects. Raw sequence datasets were processed using a standardized bioinformatic pipeline and categorized by their ecological niche and periodontal status. The subgingival microbial dysbiosis index (SMDI), a dysbiosis index originally developed using the subgingival microbiome, was computed at species and genus levels and customized for each niche. Its diagnostic accuracy for periodontitis was evaluated using receiver operating characteristic curves. RESULTS: Four studies, contributing 328 microbiome samples, were included. At both species and genus levels, periodontitis samples had a higher SMDI, but the differences were only significant for subgingival biofilm and saliva (p < 0.001). However, SMDI showed good diagnostic accuracy for periodontitis status for all three niches (area under curve ranging from 0.76 to 0.90, p < 0.05). The dysbiosis index of subgingival biofilm was positively correlated with saliva consistently (p < 0.001) and with the tongue at the genus level (p = 0.036). CONCLUSIONS: While the impact on the tongue microbiome requires further investigation, periodontitis-associated dysbiosis affects the salivary microbiome and is quantifiable using the dysbiosis index. The diagnostic potential of salivary microbial dysbiosis as a convenient periodontal biomarker for assessing periodontal status has potential public health and clinical applications. PLAIN LANGUAGE SUMMARY: Periodontitis, a severe inflammation of the gums which causes bone loss, is a disease caused by an imbalance of good and bad bacteria under the gums. However, it is unclear how this bacterial imbalance in the gums affects the bacterial balance of other distinct parts of the mouth, such as the saliva and tongue. This study uses bacteria datasets of four previously published studies, contributing a total of 328 bacterial samples. The data were processed using a uniform data analysis workflow, and a bacterial score, the subgingival microbial dysbiosis index (SMDI), previously shown to capture periodontitis-associated bacteria imbalance, was calculated separately for samples from under the gums, the saliva, and the tongue. The SMDI was able to distinguish between health and periodontitis within each oral location, and in general, the scores were higher for periodontitis samples, though this difference was significant only for bacteria under the gums and in saliva. Saliva scores were also consistently correlated with bacteria under the gums. This study shows that periodontitis-associated bacterial imbalances are observed in oral locations beyond just under the gums, particularly the saliva. Thus, saliva bacteria may be used as a convenient biomarker for assessing gum disease, allowing for potential public health and clinical applications.

TP53 mutations in myeloid neoplasms: implications for accurate laboratory detection, diagnosis, and treatment.

Genetic alterations that affect the function of p53 tumor suppressor have been extensively investigated in myeloid neoplasms, revealing their significant impact on disease progression, treatment response, and patient outcomes. The identification and characterization of TP53 mutations play pivotal roles in subclassifying myeloid neoplasms and guiding treatment decisions. Starting with the presentation of a typical case, this review highlights the complicated nature of genetic alterations involving TP53 and provides a comprehensive analysis of TP53 mutations and other alterations in myeloid neoplasms. Currently available methods used in clinical laboratories to identify TP53 mutations are discussed, focusing on the importance of establishing a robust testing protocol within clinical laboratories to ensure the delivery of accurate and reliable results. The treatment implications of TP53 mutations in myeloid neoplasms and clinical trial options are reviewed. Ultimately, we hope that this review provides valuable insights into the patterns of TP53 alterations in myeloid neoplasms and offers guidance to establish practical laboratory testing protocols to support the best practices of precision oncology.

Pathways from school to work: A sequence analysis of non-engaged youth.

INTRODUCTION: Research on heterogeneous pathways in school-to-work transitions (SWT), particularly longitudinal research, has been limited, as have empirical studies examining effective interventions for facilitating multiple SWT pathways among non-engaged youth (NEY), who are generally at risk of being not in education, employment, or training (NEET). METHODS: To develop a typology of SWT pathways, we conducted sequence analysis with longitudinal data from a sample of 630 NEY aged 14-29 (M = 19.78; 63.65% males) in Hong Kong during a 22-month period beginning in September 2020. We also performed multinomial logistic regressions to assess the impact of career and life development (CLD) interventions on SWT outcomes. RESULTS: Our analysis yielded a fivefold typology of SWT pathways: the Employment/Entrepreneurship cluster (31.27%), the Vocational Education and Training cluster (13.49%), the Generic Education cluster (16.83%), the Serious Leisure Development cluster (15.24%), and the long-term NEET cluster (23.17%). NEY in the intervention group receiving CLD services, inspired by the expanded notion of work (ENOW) and youth development and intervention framework (YDIF), demonstrated significantly higher likelihoods of being in the Employment/Entrepreneurship (OR = 34.5, 95% CI [10.53, 105.08]), Generic Education (OR = 3.74, 95% CI [1.81, 7.74]), Vocational Education and Training (OR = 1.55, 95% CI [1.05, 6.26]), and Serious Leisure Development (OR = 1.77, 95% CI [1.04, 4.46]) clusters than the long-term NEET cluster. CONCLUSIONS: Our findings highlight the dynamic, heterogeneous nature of NEY's CLD journeys, including that CLD interventions based on ENOW-YDIF have had a beneficial effect on NEY's multiple SWT pathways.

Impact of therapeutic inhibition of oncogenic cell signaling tyrosine kinase on cell metabolism: in vivo-detectable metabolic biomarkers of inhibition.

BACKGROUND: Inhibition of kinases is the ever-expanding therapeutic approach to various types of cancer. Typically, assessment of the treatment response is accomplished by standard, volumetric imaging procedures, performed weeks to months after the onset of treatment, given the predominantly cytostatic nature of the kinase inhibitors, at least when used as single agents. Therefore, there is a great clinical need to develop new monitoring approaches to detect the response to kinase inhibition much more promptly. Noninvasive 1H magnetic resonance spectroscopy (MRS) can measure in vitro and in vivo concentration of key metabolites which may potentially serve as biomarkers of response to kinase inhibition. METHODS: We employed mantle cell lymphoma (MCL) cell lines demonstrating markedly diverse sensitivity of inhibition of Bruton's tyrosine kinase (BTK) regarding their growth and studied in-depth effects of the inhibition on various aspects of cell metabolism including metabolite synthesis using metabolomics, glucose and oxidative metabolism by Seahorse XF technology, and concentration of index metabolites lactate, alanine, total choline and taurine by 1H MRS. RESULTS: Effective BTK inhibition profoundly suppressed key cell metabolic pathways, foremost pyrimidine and purine synthesis, the citrate (TCA) cycle, glycolysis, and pyruvate and glutamine/alanine metabolism. It also inhibited glycolysis and amino acid-related oxidative metabolism. Finally, it profoundly and quickly decreased concentration of lactate (a product of mainly glycolysis) and alanine (an indicator of amino acid metabolism) and, less universally total choline both in vitro and in vivo, in the MCL xenotransplant model. The decrease correlated directly with the degree of inhibition of lymphoma cell expansion and tumor growth. CONCLUSIONS: Our results indicate that BTK inhibition exerts a broad and profound suppressive effect on cell metabolism and that the affected index metabolites such as lactate, alanine may serve as early, sensitive, and reliable biomarkers of inhibition in lymphoma patients detectable by noninvasive MRS-based imaging method. This kind of imaging-based detection may also be applicable to other kinase inhibitors, as well as diverse lymphoid and non-lymphoid malignancies.

Efficacy and safety of Lianhua Qingwen granule combined with azithromycin for mycoplasma pneumoniae pneumonia in children: a systematic review with meta-analysis and trial sequential analysis.

Background: Lianhua Qingwen (LHQW) granule, a botanical drug preparation, is frequently utilized as an adjuvant treatment for mycoplasma pneumoniae pneumonia (MPP). Nevertheless, the clinical efficacy and safety of this treatment remain uncertain. Purpose: This study aims to evaluate the efficacy and safety of LHQW granule combined with azithromycin (AZM) in treating MPP in children. Method: To identify all randomized controlled trials (RCTs) of LHQW granule plus AZM, a search was conducted in eight Chinese and English databases (CNKI, Wan Fang, VIP, Sinomed, PubMed, Embase, Web of Science, and Cochrane Library) from their inception until 25 December 2023. Meta-regression and subgroup analysis were employed to investigate heterogeneity. Sensitivity analysis and trial sequential analysis (TSA) were conducted to assess the robustness of the findings. Additionally, the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was utilized to evaluate the quality of evidence. Results: A total of 15 RCTs involving 1909 participants were included in this study. The meta-analysis results indicated combination therapy of LHQW granule and AZM is significant different from AZM alone in both efficacy and safety, which are specifically observed in the following outcomes: response rate (RR = 1.17, 95% CI: 1.12 to 1.22, p < 0.01), antipyretic time (MD = -1.32, 95% CI: -1.66 to -0.98, p < 0.01), cough disappearance time (MD = -1.76, 95% CI: -2.47 to -1.05, p < 0.01), pulmonary rale disappearance time (MD = -1.54, 95% CI: -2.06 to -1.02, p < 0.01), c-reactive protein (CRP) (MD = -5.50, 95% CI: -6.92 to -4.07, p < 0.01), procalcitonin (PCT) (MD = -0.31, 95% CI: -0.38 to -0.24, p < 0.01), interleukin 6 (IL-6) (MD = -5.97, 95% CI: -7.39 to -4.54, p<0.01), tumor necrosis factor α (TNF-α) (MD = -5.74, 95% CI: -7.44 to -4.04, p < 0.01), forced vital capacity (FVC) (SMD = 0.48, 95% CI: 0.34 to 0.62, p < 0.01), forced expiratory volume in the first second (FEV1) (SMD = 0.55, 95% CI: 0.44 to 0.67, p < 0.01), FEV1/FVC (SMD = 0.49, 95% CI: 0.32 to 0.67, p < 0.01), CD4+ T lymphocyte (CD4+) (MD = 4.04, 95% CI: 3.09 to 4.98, p < 0.01), CD8+ T lymphocyte (CD8+) (MD = -3.32, 95% CI: 4.27 to 2.38, p < 0.01) and adverse events (RR = 0.65, 95% CI: 0.43 to 0.96, p < 0.01). Conclusion: The combination therapy of LHQW granule and AZM may be a better strategy to treat MPP in children. However, the clinical efficacy and safety of LHQW granule require further validation. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/.

HIV Subtypes and Drug-resistance-associated Mutations in US Blood Donors, 2015-2020.

Background: Monitoring genotypes of HIV infections in blood donors may provide insights into infection trends in the general population. Methods: HIV RNA was extracted from plasma samples of blood donors confirmed as HIV positive by blood screening nucleic acid and antibody tests. HIV genome target regions were amplified using nested real time-polymerase chain reaction followed by next-generation sequencing. Sequences were compared to those in the Los Alamos National Laboratory (LANL) database. Sequences were also assessed for drug resistance mutations (DRM) using the Stanford HIV DRM Database. Results: From available HIV-positive donations collected between 1 September 2015 and 31 December 2020, 563 of 743 (75.8%) were successfully sequenced; 4 were subtype A, 543 subtype B, 5 subtype C, 1 subtype G, 5 circulating recombinant forms (CRF), and 2 were subtype B and D recombinants. Overall, no significant differences between blood donor and available LANL genotypes were found, and the genotypes of newly acquired versus prevalent HIV infections in donors were similar. The proportion of non-B subtypes and CRF remained a small fraction, with no other subtype or CRF representing more than 1% of the total. DRM were identified in 122 (21.6%) samples with protease inhibitor, nucleoside reverse transcriptase inhibitor and non-nucleoside reverse transcriptase inhibitor DRMs identified in 4.9%, 4.6% and 14.0% of samples, respectively. Conclusions: HIV genetic diversity and DRM in blood donors appear representative of circulating HIV infections in the US general population and may provide more information on infection diversity than sequences reported to LANL, particularly for recently transmitted infections.

SENSE-PPI reconstructs interactomes within, across, and between species at the genome scale.

Ab initio computational reconstructions of protein-protein interaction (PPI) networks will provide invaluable insights into cellular systems, enabling the discovery of novel molecular interactions and elucidating biological mechanisms within and between organisms. Leveraging the latest generation protein language models and recurrent neural networks, we present SENSE-PPI, a sequence-based deep learning model that efficiently reconstructs ab initio PPIs, distinguishing partners among tens of thousands of proteins and identifying specific interactions within functionally similar proteins. SENSE-PPI demonstrates high accuracy, limited training requirements, and versatility in cross-species predictions, even with non-model organisms and human-virus interactions. Its performance decreases for phylogenetically more distant model and non-model organisms, but signal alteration is very slow. In this regard, it demonstrates the important role of parameters in protein language models. SENSE-PPI is very fast and can test 10,000 proteins against themselves in a matter of hours, enabling the reconstruction of genome-wide proteomes.

Protocol for the characterization of the pancreatic tumor microenvironment using organoid-derived mouse models and single-nuclei RNA sequencing.

Single-nuclei RNA sequencing (snRNA-seq) allows for obtaining gene expression profiles from frozen or hard-to-dissociate tissues at the single-nuclei level. Here, we describe a protocol to obtain snRNA-seq data of pancreatic tumors from orthotopically grafted organoid-derived mouse models. We provide details on the establishment of these mouse models, the isolation of single nuclei from pancreatic tumors, and the analysis of the snRNA-seq datasets. For complete details on the use and execution of this protocol, please refer to Mucciolo et al.1.