An enterococcal phage-derived enzyme suppresses graft-versus-host disease.

Changes in the gut microbiome have pivotal roles in the pathogenesis of acute graft-versus-host disease (aGVHD) after allogenic haematopoietic cell transplantation (allo-HCT)1-6. However, effective methods for safely resolving gut dysbiosis have not yet been established. An expansion of the pathogen Enterococcus faecalis in the intestine, associated with dysbiosis, has been shown to be a risk factor for aGVHD7-10. Here we analyse the intestinal microbiome of patients with allo-HCT, and find that E. faecalis escapes elimination and proliferates in the intestine by forming biofilms, rather than by acquiring drug-resistance genes. We isolated cytolysin-positive highly pathogenic E. faecalis from faecal samples and identified an anti-E. faecalis enzyme derived from E. faecalis-specific bacteriophages by analysing bacterial whole-genome sequencing data. The antibacterial enzyme had lytic activity against the biofilm of E. faecalis in vitro and in vivo. Furthermore, in aGVHD-induced gnotobiotic mice that were colonized with E. faecalis or with patient faecal samples characterized by the domination of Enterococcus, levels of intestinal cytolysin-positive E. faecalis were decreased and survival was significantly increased in the group that was treated with the E. faecalis-specific enzyme, compared with controls. Thus, administration of a phage-derived antibacterial enzyme that is specific to biofilm-forming pathogenic E. faecalis-which is difficult to eliminate with existing antibiotics-might provide an approach to protect against aGVHD.

Blocking PD-L1-PD-1 improves senescence surveillance and ageing phenotypes.

The accumulation of senescent cells is a major cause of age-related inflammation and predisposes to a variety of age-related diseases1. However, little is known about the molecular basis underlying this accumulation and its potential as a target to ameliorate the ageing process. Here we show that senescent cells heterogeneously express the immune checkpoint protein programmed death-ligand 1 (PD-L1) and that PD-L1+ senescent cells accumulate with age in vivo. PD-L1- cells are sensitive to T cell surveillance, whereas PD-L1+ cells are resistant, even in the presence of senescence-associated secretory phenotypes (SASP). Single-cell analysis of p16+ cells in vivo revealed that PD-L1 expression correlated with higher levels of SASP. Consistent with this, administration of programmed cell death protein 1 (PD-1) antibody to naturally ageing mice or a mouse model with normal livers or induced nonalcoholic steatohepatitis reduces the total number of p16+ cells in vivo as well as the PD-L1+ population in an activated CD8+ T cell-dependent manner, ameliorating various ageing-related phenotypes. These results suggest that the heterogeneous expression of PD-L1 has an important role in the accumulation of senescent cells and inflammation associated with ageing, and the elimination of PD-L1+ senescent cells by immune checkpoint blockade may be a promising strategy for anti-ageing therapy.

Zero-shot-capable identification of phage-host relationships with whole-genome sequence representation by contrastive learning.

Accurately identifying phage-host relationships from their genome sequences is still challenging, especially for those phages and hosts with less homologous sequences. In this work, focusing on identifying the phage-host relationships at the species and genus level, we propose a contrastive learning based approach to learn whole-genome sequence embeddings that can take account of phage-host interactions (PHIs). Contrastive learning is used to make phages infecting the same hosts close to each other in the new representation space. Specifically, we rephrase whole-genome sequences with frequency chaos game representation (FCGR) and learn latent embeddings that 'encapsulate' phages and host relationships through contrastive learning. The contrastive learning method works well on the imbalanced dataset. Based on the learned embeddings, a proposed pipeline named CL4PHI can predict known hosts and unseen hosts in training. We compare our method with two recently proposed state-of-the-art learning-based methods on their benchmark datasets. The experiment results demonstrate that the proposed method using contrastive learning improves the prediction accuracy on known hosts and demonstrates a zero-shot prediction capability on unseen hosts. In terms of potential applications, the rapid pace of genome sequencing across different species has resulted in a vast amount of whole-genome sequencing data that require efficient computational methods for identifying phage-host interactions. The proposed approach is expected to address this need by efficiently processing whole-genome sequences of phages and prokaryotic hosts and capturing features related to phage-host relationships for genome sequence representation. This approach can be used to accelerate the discovery of phage-host interactions and aid in the development of phage-based therapies for infectious diseases.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities.

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.

biotextgraph: graphical summarization of functional similarities from textual information.

SUMMARY: Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. AVAILABILITY AND IMPLEMENTATION: The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

Regulation of the innate immune response and gut microbiome by p53.

p53 is a key tumor suppressor mutated in half of human cancers. In recent years, p53 was shown to regulate a wide variety of functions. From the transcriptome analysis of 24 tissues of irradiated mice, we identified 553 genes markedly induced by p53. Gene Ontology (GO) enrichment analysis found that the most associated biological process was innate immunity. 16S rRNA-seq analysis revealed that Akkermansia, which has anti-inflammatory properties and is involved in the regulation of intestinal barrier integrity, was decreased in p53-knockout (p53-/- ) mice after radiation. p53-/- mice were susceptible to radiation-induced GI toxicity and had a significantly shorter survival time than p53-wild-type (p53+/+ ) mice following radiation. However, administration of antibiotics resulted in a significant improvement in survival and protection against GI toxicity. Mbl2 and Lcn2, which have antimicrobial activity, were identified to be directly transactivated by p53 and secreted by liver into the circulatory system. We also found the expression of MBL2 and LCN2 was decreased in liver cancer tissues with p53 mutations compared with those without p53 mutations. These results indicate that p53 is involved in shaping the gut microbiome through its downstream targets related to the innate immune system, thus protecting the intestinal barrier.

Investigation of the BERT model on nucleotide sequences with non-standard pre-training and evaluation of different k-mer embeddings.

MOTIVATION: In recent years, pre-training with the transformer architecture has gained significant attention. While this approach has led to notable performance improvements across a variety of downstream tasks, the underlying mechanisms by which pre-training models influence these tasks, particularly in the context of biological data, are not yet fully elucidated. RESULTS: In this study, focusing on the pre-training on nucleotide sequences, we decompose a pre-training model of Bidirectional Encoder Representations from Transformers (BERT) into its embedding and encoding modules to analyze what a pre-trained model learns from nucleotide sequences. Through a comparative study of non-standard pre-training at both the data and model levels, we find that a typical BERT model learns to capture overlapping-consistent k-mer embeddings for its token representation within its embedding module. Interestingly, using the k-mer embeddings pre-trained on random data can yield similar performance in downstream tasks, when compared with those using the k-mer embeddings pre-trained on real biological sequences. We further compare the learned k-mer embeddings with other established k-mer representations in downstream tasks of sequence-based functional prediction. Our experimental results demonstrate that the dense representation of k-mers learned from pre-training can be used as a viable alternative to one-hot encoding for representing nucleotide sequences. Furthermore, integrating the pre-trained k-mer embeddings with simpler models can achieve competitive performance in two typical downstream tasks. AVAILABILITY AND IMPLEMENTATION: The source code and associated data can be accessed at https://github.com/yaozhong/bert_investigation.

ggkegg: analysis and visualization of KEGG data utilizing the grammar of graphics.

SUMMARY: The Kyoto Encyclopedia of Genes and Genomes (KEGG) database serves as a valuable systems biology resource and is widely utilized in diverse research fields. However, existing software does not allow flexible visualization and network analyses of the vast and complex KEGG data. We developed ggkegg, an R package that integrates KEGG information with ggplot2 and ggraph. ggkegg enables enhanced visualization and network analyses of KEGG data. We demonstrate the utility of the package by providing examples of its application in single-cell, bulk transcriptome, and microbiome analyses. ggkegg may empower researchers to analyze complex biological networks and present their results effectively. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at: https://github.com/noriakis/ggkegg.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prognostic significance of chronic kidney disease and impaired renal function in Japanese patients with COVID-19.

BACKGROUND: Renal impairment is a predictor of coronavirus disease (COVID-19) severity. No studies have compared COVID-19 outcomes in patients with chronic kidney disease (CKD) and patients with impaired renal function without a prior diagnosis of CKD. This study aimed to identify the impact of pre-existing impaired renal function without CKD on COVID-19 outcomes. METHODS: This retrospective study included 3,637 patients with COVID-19 classified into three groups by CKD history and estimated glomerular filtration rate (eGFR) on referral: Group 1 (n = 2,460), normal renal function without a CKD history; Group 2 (n = 905), impaired renal function without a CKD history; and Group 3 (n = 272), history of CKD. We compared the clinical characteristics of these groups and assessed the effect of CKD and impaired renal function on critical outcomes (requirement for respiratory support with high-flow oxygen devices, invasive mechanical ventilation, or extracorporeal membrane oxygen, and death during hospitalization) using multivariable logistic regression. RESULTS: The prevalence of comorbidities (hypertension, diabetes, and cardiovascular disease) and incidence of inflammatory responses (white blood counts, and C-reactive protein, procalcitonin, and D-dimer levels) and complications (bacterial infection and heart failure) were higher in Groups 2 and 3 than that in Group 1. The incidence of critical outcomes was 10.8%, 17.7%, and 26.8% in Groups 1, 2, and 3, respectively. The mortality rate and the rate of requiring IMV support was lowest in Group 1 and highest in Group 3. Compared with Group 1, the risk of critical outcomes was higher in Group 2 (adjusted odds ratio [aOR]: 1.32, 95% confidence interval [CI]: 1.03-1.70, P = 0.030) and Group 3 (aOR: 1.94, 95% CI: 1.36-2.78, P < 0.001). Additionally, the eGFR was significantly associated with critical outcomes in Groups 2 (odds ratio [OR]: 2.89, 95% CI: 1.64-4.98, P < 0.001) and 3 (OR: 1.87, 95% CI: 1.08-3.23, P = 0.025) only. CONCLUSIONS: Clinicians should consider pre-existing CKD and impaired renal function at the time of COVID-19 diagnosis for the management of COVID-19.

[Durable remission of T-cell prolymphocytic leukemia with CLEC16A::IL2 after allogeneic hematopoietic stem cell transplantation].

A 64-year-old woman presented with fine motor impairment in both hands. MRI revealed a contrast-enhanced lesion in the medulla oblongata. Lymphoid cells with abnormal blebs were observed and a CD4+/CD8+ double positive (DP) T cell population was detected by flow cytometry (FCM) in the bone marrow (BM) and the peripheral blood (PB). CLEC16A::IL2 fusion gene was identified by whole exome sequencing with DNA prepared from DP T cells. Clonal rearrangement of the T-cell receptor gene and expression of TCL1A protein were detected. This led to a diagnosis of T-cell prolymphocytic leukemia (T-PLL) with central nervous system (CNS) infiltration. Abnormal cells in BM and PB became undetectable on microscopy and FCM, and the CNS lesion disappeared on MRI after second-line therapy with alemtuzumab. Meanwhile, the CLEC16A::IL2 fusion mRNA remained detectable in PB. Allogeneic hematopoietic stem-cell transplantation was performed, and the fusion mRNA has now been undetectable for more than 5 years since transplantation. This is the first report of a T-PLL case with a CLEC16A::IL2 fusion gene.

Clinical and prognostic features of Langerhans cell histiocytosis in adults.

Langerhans cell histiocytosis (LCH) is a rare disease characterized by clonal expansion of CD1a+ CD207+ myeloid dendritic cells. The features of LCH are mainly described in children and remain poorly defined in adults; therefore, we conducted a nationwide survey to collect clinical data from 148 adult patients with LCH. The median age at diagnosis was 46.5 (range: 20-87) years with male predominance (60.8%). Among the 86 patients with detailed treatment information, 40 (46.5%) had single system LCH, whereas 46 (53.5%) had multisystem LCH. Moreover, 19 patients (22.1%) had an additional malignancy. BRAF V600E in plasma cell-free DNA was associated with a low overall survival (OS) rate and the risk of the pituitary gland and central nervous system involvement. At a median follow-up of 55 months from diagnosis, six patients (7.0%) had died, and the four patients with LCH-related death did not respond to initial chemotherapy. The OS probability at 5 years post-diagnosis was 90.6% (95% confidence interval: 79.8-95.8). Multivariate analysis showed that patients aged ≥60 years at diagnosis had a relatively poor prognosis. The probability of event-free survival at 5 years was 52.1% (95% confidence interval: 36.6-65.5), with 57 patients requiring chemotherapy. In this study, we first revealed the high rate of relapse after chemotherapy and mortality of poor responders in adults as well as children. Therefore, prospective therapeutic studies of adults with LCH using targeted therapies are needed to improve outcomes in adults with LCH.

Preemptive Intravenous Human Immunoglobulin G Suppresses BK Polyomavirus Replication and Spread of Infection In Vitro.

BK polyomavirus (BKPyV) infection causes various diseases in immunocompromised patients. Cells from human lung and kidney were infected with BKPyV and treated with commercially available intravenous immunoglobulin G (IVIG). Its effects on BKPyV replication and spread of infection were investigated, focusing on administration timing. IVIG treatment 3 hours after infection suppressed BKPyV replication assessed by real-time PCR and expression of the viral capsid protein 1 and large T-antigen. IVIG effectively reduced the number of BKPyV-infected cells 2 weeks after infection in an antibody titer-dependent manner. Virus release in the culture supernatants was not influenced by IVIG treatment 6-80 hours and 3-9 days after infection. Collectively, IVIG did not affect viral release from infected cells but inhibited the spread of infection by neutralizing the released virus and blocking the new infected cell formation, indicating greater efficacy in early localized infection. BKPyV replication resumed in IVIG-treated cultures at 7 days after IVIG removal. Early prophylactic administration of IVIG is expected to reduce the growth and spread of BKPyV infection, resulting in the reduction of infected cell lesions and prevention of BKPyV-associated diseases.

Comprehensive analysis of indels in whole-genome microsatellite regions and microsatellite instability across 21 cancer types.

Microsatellites are repeats of 1- to 6-bp units, and approximately 10 million microsatellites have been identified across the human genome. Microsatellites are vulnerable to DNA mismatch errors and have thus been used to detect cancers with mismatch repair deficiency. To reveal the mutational landscape of microsatellite repeat regions at the genome level, we analyzed approximately 20.1 billion microsatellites in 2717 whole genomes of pan-cancer samples across 21 tissue types. First, we developed a new insertion and deletion caller (MIMcall) that takes into consideration the error patterns of different types of microsatellites. Among the 2717 pan-cancer samples, our analysis identified 31 samples, including colorectal, uterus, and stomach cancers, with a higher proportion of mutated microsatellite (≥0.03), which we defined as microsatellite instability (MSI) cancers of genome-wide level. Next, we found 20 highly mutated microsatellites that can be used to detect MSI cancers with high sensitivity. Third, we found that replication timing and DNA shape were significantly associated with mutation rates of microsatellites. Last, analysis of mutations in mismatch repair genes showed that somatic SNVs and short indels had larger functional impacts than germline mutations and structural variations. Our analysis provides a comprehensive picture of mutations in the microsatellite regions and reveals possible causes of mutations, as well as provides a useful marker set for MSI detection.

Genetic analysis of low-grade adenosquamous carcinoma of the breast progressing to high-grade metaplastic carcinoma.

PURPOSE: Low-grade adenosquamous carcinoma (LGASC) is a rare type of metaplastic carcinoma of the breast (MBC) with an indolent clinical course. A few LGASC cases with high-grade transformation have been reported; however, the genetics underlying malignant progression of LGASC remain unclear. METHODS: We performed whole-genome sequencing analysis on five MBCs from four patients, including one case with matching primary LGASC and a lymph node metastatic tumor consisting of high-grade MBC with a predominant metaplastic squamous cell carcinoma component (MSC) that progressed from LGASC and three cases of independent de novo MSC. RESULTS: Unlike de novo MSC, LGASC and its associated MSC showed no TP53 mutation and tended to contain fewer structural variants than de novo MSC. Both LGASC and its associated MSC harbored the common GNAS c.C2530T:p.Arg844Cys mutation, which was more frequently detected in the cancer cell fraction of MSC. MSC associated with LGASC showed additional pathogenic deletions of multiple tumor-suppressor genes, such as KMT2D and BTG1. Copy number analysis revealed potential 18q loss of heterozygosity in both LGASC and associated MSC. The frequency of SMAD4::DCC fusion due to deletions increased with progression to MSC; however, chimeric proteins were not detected. SMAD4 protein expression was already decreased at the LGASC stage due to unknown mechanisms. CONCLUSION: Not only LGASC but also its associated high-grade MBC may be genetically different from de novo high-grade MBC. Progression from LGASC to high-grade MBC may involve the concentration of driver mutations caused by clonal selection and inactivation of tumor-suppressor genes.

Role of the Orphan Transporter SLC35E1 in the Nuclear Egress of Herpes Simplex Virus 1.

This study developed a system consisting of two rounds of screening cellular proteins involved in the nuclear egress of herpes simplex virus 1 (HSV-1). Using this system, we first screened cellular proteins that interacted with the HSV-1 nuclear egress complex (NEC) consisting of UL34 and UL31 in HSV-1-infected cells, which are critical for the nuclear egress of HSV-1, by tandem affinity purification coupled with mass spectrometry-based proteomics technology. Next, we performed CRISPR/Cas9-based screening of live HSV-1-infected reporter cells under fluorescence microscopy using single guide RNAs targeting the cellular proteins identified in the first proteomic screening to detect the mislocalization of the lamin-associated protein emerin, which is a phenotype for defects in HSV-1 nuclear egress. This study focused on a cellular orphan transporter SLC35E1, one of the cellular proteins identified by the screening system. Knockout of SLC35E1 reduced HSV-1 replication and induced membranous invaginations containing perinuclear enveloped virions (PEVs) adjacent to the nuclear membrane (NM), aberrant accumulation of PEVs in the perinuclear space between the inner and outer NMs and the invagination structures, and mislocalization of the NEC. These effects were similar to those of previously reported mutation(s) in HSV-1 proteins and depletion of cellular proteins that are important for HSV-1 de-envelopment, one of the steps required for HSV-1 nuclear egress. Our newly established screening system enabled us to identify a novel cellular protein required for efficient HSV-1 de-envelopment. IMPORTANCE The identification of cellular protein(s) that interact with viral effector proteins and function in important viral procedures is necessary for enhancing our understanding of the mechanics of various viral processes. In this study, we established a new system consisting of interactome screening for the herpes simplex virus 1 (HSV-1) nuclear egress complex (NEC), followed by loss-of-function screening to target the identified putative NEC-interacting cellular proteins to detect a defect in HSV-1 nuclear egress. This newly established system identified SLC35E1, an orphan transporter, as a novel cellular protein required for efficient HSV-1 de-envelopment, providing an insight into the mechanisms involved in this viral procedure.

Identification of bacteriophage genome sequences with representation learning.

MOTIVATION: Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. RESULTS: We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AVAILABILITY AND IMPLEMENTATION: The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan.

BACKGROUND: Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions. METHODS: This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions. RESULTS: The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions. CONCLUSIONS: AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Impact of neoadjuvant therapy on gut microbiome in patients with resectable/borderline resectable pancreatic ductal adenocarcinoma.

BACKGROUND: /Objectives: Effects of chemotherapy on gut microbiota have been reported in various carcinomas. The current study aimed to evaluate the changes in the gut microbiota before and after neoadjuvant chemotherapy (NAC) in patients with resectable (R) and borderline resectable (BR) pancreatic ductal adenocarcinoma (PDAC) and understand their clinical implications. METHODS: Twenty patients diagnosed with R/BR-PDAC were included in this study. Stool samples were collected at two points, before and after NAC, for microbiota analysis using 16S ribosomal RNA (16S rRNA) gene sequences. RESULTS: Of the 20 patients, 18 (90%) were treated with gemcitabine plus S-1 as NAC, and the remaining patients received gemcitabine plus nab-paclitaxel and a fluorouracil, leucovorin, irinotecan, and oxaliplatin combination. No significant differences were observed in the α- and ß-diversity before and after NAC. Bacterial diversity was not associated with Evans classification (histological grade of tumor destruction by NAC) or postoperative complications. The relative abundance of Actinobacteria phylum after NAC was significantly lower than that before NAC (P = 0.02). At the genus level, the relative abundance of Bifidobacterium before NAC in patients with Evans grade 2 disease was significantly higher than that in patients with Evans grade 1 disease (P = 0.03). Patients with Evans grade 2 lost significantly more Bifidobacterium than patients with Evans grade 1 (P = 0.01). CONCLUSIONS: The diversity of gut microbiota was neither decreased by NAC for R/BR-PDAC nor associated with postoperative complications. Lower incidence of Bifidobacterium genus before NAC may be associated with a lower pathological response to NAC.