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Abstract Background Patients with Crohn’s disease (CD) experience intestinal inflammation. Ontamalimab (SHP647), a fully human immunoglobulin G2 monoclonal antibody against mucosal addressin cell adhesion molecule-1, is a potential novel CD treatment. OPERA II, a multicenter, open-label, phase 2 extension study, assessed the long-term safety and efficacy of ontamalimab in patients with moderate-to-severe CD. Methods Patients had completed 12 weeks of blinded treatment (placebo or ontamalimab at 22.5, 75, or 225 mg subcutaneously) in OPERA (NCT01276509) or had a clinical response to ontamalimab 225 mg in TOSCA (NCT01387594). Participants received ontamalimab at 75 mg every 4 weeks (weeks 0–72), then were followed up every 4 weeks for 24 weeks. One-time dose reduction to 22.5 mg or escalation to 225 mg was permitted at the investigator’s discretion. The primary end points were safety and tolerability outcomes. Secondary end points included changes in serum drug and biomarker concentrations. Efficacy end points were exploratory, and used non-responder imputation methods. Results Overall, 149/268 patients completed the study. The most common adverse event leading to study discontinuation was CD flare (19.8%). Two patients died; neither death was considered to be drug related. No dose reductions occurred; 157 patients had their dose escalated. Inflammatory biomarker concentrations decreased. Serum ontamalimab levels were consistent with known pharmacokinetics. Remission rates (Harvey-Bradshaw Index [HBI] ≤ 5; baseline, 48.1%; week 72, 37.3%) and response rates (baseline [decrease in Crohn’s Disease Activity Index ≥ 70 points], 63.1%; week 72 [decrease in HBI ≥ 3], 42.5%) decreased gradually. Conclusions Ontamalimab was well tolerated; treatment responses appeared to be sustained over 72 weeks. ClinicalTrials.gov ID: NCT01298492.

Patients with COVID-19 have been reported to experience gastrointestinal symptoms as well as respiratory symptoms, but the effects of COVID-19 on the gut microbiota are poorly understood. We explored gut microbiome profiles associated with the respiratory infection of SARS-CoV-2 during the recovery phase in patients with asymptomatic or mild COVID-19. A longitudinal analysis was performed using the same patients to determine whether the gut microbiota changed after recovery from COVID-19. We applied 16S rRNA amplicon sequencing to analyze two paired fecal samples from 12 patients with asymptomatic or mild COVID-19. Fecal samples were selected at two time points: during SARS-CoV-2 infection (infected state) and after negative conversion of the viral RNA (recovered state). We also compared the microbiome data with those from 36 healthy controls. Microbial evenness of the recovered state was significantly increased compared with the infected state. SARS-CoV-2 infection induced the depletion of Bacteroidetes, while an abundance was observed with a tendency to rapidly reverse in the recovered state. The Firmicutes/Bacteroidetes ratio in the infected state was markedly higher than that in the recovered state. Gut dysbiosis was observed after infection even in patients with asymptomatic or mild COVID-19, while the composition of the gut microbiota was recovered after negative conversion of SARS-CoV-2 RNA. Modifying intestinal microbes in response to COVID-19 might be a useful therapeutic alternative.

Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

Although it is known that ambient temperature can affect the diagnostic performance of the fecal immunochemical test (FIT), the impact of other weather parameters, including humidity, on the sensitivity of FIT remains to be further investigated. We aimed to evaluate the impact of ambient temperature and humidity on the performance of FIT for screening for advanced colorectal neoplasia (ACRN). We included asymptomatic individuals who had undergone both screening colonoscopy and FIT. The diagnostic performance of FIT, including its sensitivity, was analyzed according to the ambient temperature and humidity on the day that FIT was performed. Temperature and humidity were divided into five levels. Among 35,461 participants, 589 (1.7%) had ACRN. The positivity rate of FIT was lower at ≥24 °C (3.1%) than at <0 °C (3.9%), 0–8 °C (4.3%), and 8–16 °C (3.9%). It was also lower at 80–90% humidity (3.1%) than at < 60% humidity (3.9%). Multivariable analysis showed that high ambient temperature (≥24 °C) with high ambient humidity (≥80%) was associated with a low positivity rate of FIT (odds ratio [OR] 0.62, 95% confidence interval [CI] 0.44–0.86). Sensitivity tended to decrease at high ambient temperature (<24 °C vs. ≥24 °C; 20.8% vs. 14.6%, P  = 0.110) and was significantly lower at high ambient humidity (<80% vs. ≥80%; 21.0% vs. 12.5%, P  = 0.044). The multivariable analysis also showed that high ambient humidity was independently associated with low sensitivity of FIT (OR 0.54, 95% CI 0.28–0.96). In conclusion, high ambient humidity decreased the sensitivity, while high ambient temperature along with high ambient humidity decreased the positivity rate of FIT.

Background Reports concerning the clinical usefulness of ¹⁸F 2-fluoro-2-deoxy-d-glucose integrated positron emission and computed tomography (¹⁸F-FDG PET-CT) for patients with gallbladder cancer and cholangiocarcinoma are relatively scarce. The purpose of this study was to assess the diagnostic value of PET-CT in relation to a conventional imaging modality, multidetector row CT (MDCT), for patients with gallbladder cancer and cholangiocarcinoma. Methods Ninety-nine patients with suspected gallbladder cancer and cholangiocarcinoma who underwent both PET-CT and MDCT for initial staging were included in our study. The results of these two imaging modalities for evaluating primary tumors, regional lymph nodes and distant metastases were compared with the final diagnoses based on pathological or clinical findings. Results A maximum standardized uptake value (SUVmax) of 3.65 was found to be the best cutoff value for detecting a malignant tumor. The overall values for the sensitivities, specificities, positive predictive values (PPVs), negative predictive values (NPVs) and the accuracies of PET-CT and MDCT for the detection of a primary tumor were 90.2, 70.6, 93.7, 60.0, 86.9% and 84.2, 70.6, 93.2, 48.0, 81.8%, respectively. PET-CT demonstrated no significant advantage over MDCT for the diagnosis of a primary tumor. PET-CT showed a significantly higher PPV (94.1 vs. 77.5%, P = 0.04) than that found for MDCT in the diagnosis of regional lymph node metastasis. Additionally, PET-CT showed a significantly higher sensitivity (94.7 vs. 63.2%, P = 0.02) than that found for MDCT in the diagnosis of distant metastasis. Conclusions PET-CT is valuable for detecting regional lymph node involvement and unsuspected distant metastases that are not diagnosed by MDCT.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn’s disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.

Crohn’s disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

We establish a robust algorithm to analyze the influence of system uncertainties on the transmission error of a spur gear pair under 2D simplification. The algorithm provides a way of generating smooth cutter profiles with machining uncertainties and measuring the thermal deformation through the uncertainties in material properties. Then, it produces realizations of gear tooth profiles based on the analytical method for accuracy and computational efficiency. Numerical investigations show the statistical analysis on the tooth contact analysis by comparing steel and plastic gears. It is worthwhile remarking that the plastic gear is susceptible to the geometric error caused by thermal deformation. Moreover, although the impact of thermal deformation on steel gear may seem slim, it can have a noticeable influence when it exists with mechanical uncertainties together.

Inflammatory bowel disease is known to be associated with a genetic predisposition involving multiple genes; however, there is growing evidence that abnormal interactions with environmental factors, particularly epigenetic factors, can also significantly contribute to the development of inflammatory bowel disease (IBD). Although many genome-wide association studies have been performed to identify the genetic changes underlying the pathogenesis of Crohn’s disease, the role of epigenetic alterations based on molecular complications arising from Crohn’s disease (CD) is poorly understood. We employed an unbiased approach to define DNA methylation alterations in colonoscopy samples from patients with CD using the HumanMethylation450K BeadChip platform. Technical and functional validation was performed by methylation-specific PCR (MSP) and bisulfite sequencing of a validation set of 207 patients with CD samples. Immunohistochemistry (IHC) analysis was performed in the representative sample sets. DNA methylation profile in CD revealed that 135 probes (24 hypermethylated and 111 hypomethylated probes) were differentially methylated. We validated the methylation levels of 19 genes that showed hypermethylation in patients with CD compared with normal controls. We uniquely identified that the fragile histidine triad (FHIT) gene was hypermethylated in a disease-specific manner and its protein level was downregulated in patients with CD. Pathway analysis of the hypermethylated candidates further suggested putative molecular interactions relevant to IBD pathology. Our data provide information on the biological and clinical implications of DNA hypermethylated genes in CD, identifying FHIT methylation as a promising new biomarker for CD. Further study of the role of FHIT in IBD pathogenesis may lead to the development of new therapeutic targets.

This study aimed to elucidate common and unique microbiome patterns in saliva, intestinal tissue biopsy, and stool samples from patients with Crohn’s disease (CD). Saliva, tissue, and stool samples from patients with CD were prospectively collected. Quantitative and phylogenetic analyses of 16s rRNA sequencing data were performed with bioinformatical pipelines. A total of 30 patients were enrolled in this study. The composition of major microbial taxa was similar between tissue and stool samples. A total of 11 of the 20 most abundant microbiota were found in both samples. The microbial community in saliva was significantly distinct from that in tissue and stool. The major species of microbiota and their composition also differed significantly from those of tissue and stool samples. However, Streptococcus and Prevotella are common genera in saliva, tissue, and stool microbiome. The abundance of Streptococcus, Pantoea, and Actinomyces from the saliva sample group were significantly different, varying with the location of the inflammation. Saliva has a distinct microbial community compared with tissues and stools in patients with CD. Prevotella and Streptococcus, which are commonly observed in saliva, stool, and tissue, can be considered a potential biomarker related to the diagnosis or prognosis of CD.