Systemic antibiotics vary widely in in vitro activity against Clostridium difficile. Some agents with activity against C. difficile (e.g., piperacillin/tazobactam) inhibit establishment of colonization in mice. We tested the hypothesis that piperacillin/tazobactam and other agents with activity against C. difficile achieve sufficient concentrations in the intestinal tract to inhibit colonization in patients. Point-prevalence culture surveys were conducted to compare the frequency of asymptomatic rectal carriage of toxigenic C. difficile among patients receiving piperacillin/tazobactam or other inhibitory antibiotics (e.g. ampicillin, linezolid, carbapenems) versus antibiotics lacking activity against C. difficile (e.g., cephalosporins, ciprofloxacin). For a subset of patients, in vitro inhibition of C. difficile (defined as a reduction in concentration after inoculation of vegetative C. difficile into fresh stool suspensions) was compared among antibiotic treatment groups. Of 250 patients, 32 (13 %) were asymptomatic carriers of C. difficile. In comparison to patients receiving non-inhibitory antibiotics or prior antibiotics within 90 days, patients currently receiving piperacillin/tazobactam were less likely to be asymptomatic carriers (1/36, 3 versus 7/36, 19 and 15/69, 22 %, respectively; P = 0.024) and more likely to have fecal suspensions with in vitro inhibitory activity against C. difficile (20/28, 71 versus 3/11, 27 and 4/26, 15 %; P = 0.03). Patients receiving other inhibitory antibiotics were not less likely to be asymptomatic carriers than those receiving non-inhibitory antibiotics. Our findings suggest that piperacillin/tazobactam achieves sufficient concentrations in the intestinal tract to inhibit C. difficile colonization during therapy.

In an experimental study, the frequency of contamination of healthcare personnel during removal of contaminated personal protective equipment (PPE) was similar for bacteriophage MS2 and a novel reflective marker visualized using flash photography. The reflective marker could be a useful tool to visualize and document personnel contamination during PPE removal.Infect Control Hosp Epidemiol 2016;37:711–713

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and its management is limited by heterogeneous risk profiles, suboptimal surveillance performance, diagnostic uncertainty in chronically diseased livers, and difficulty individualizing prognosis after treatment. The aim of this narrative review was to critically evaluate artificial intelligence (AI) applications across the HCC care continuum, with emphasis on their intended clinical role, reported performance, evidence maturity, and barriers to implementation. A major strength of this review is that it moves beyond a descriptive catalog of models by structuring the literature around clinically relevant decision points and by explicitly distinguishing emerging proof-of-concept tools from applications with stronger translational potential. Across risk stratification, surveillance, imaging-based diagnosis, pathology, treatment-response prediction, and prognostication, we found that AI consistently demonstrates promise, particularly for identifying patients at higher future HCC risk, improving lesion detection and characterization on ultrasound, CT, MRI, and contrast-enhanced ultrasound, assisting histopathologic classification, and predicting outcomes such as microvascular invasion, recurrence, survival, and response to locoregional therapies. However, we also found that the evidence base remains highly uneven: many diagnostic studies are retrospective and lesion-enriched rather than embedded in true surveillance populations, many prognostic models lack robust external validation and calibration assessment, and reference standards, imaging protocols, and dataset composition vary substantially across studies. These findings are clinically relevant because they highlight both where AI may offer near-term value and why most published systems are not yet ready for routine use. Overall, AI in HCC should be viewed as a rapidly evolving but still transitional field. Its future impact will depend not only on higher-performing algorithms but on clearly defined clinical use cases, multicenter and prospective validation, transparent reporting, workflow-aware evaluation, and implementation strategies that support safe, equitable, and scalable adoption.

Introduction: Colorectal cancer remains a leading cause of cancer-related morbidity and mortality, with adenomatous polyps representing a common precursor. Post-polypectomy polyp recurrence represents a significant risk of colorectal cancer, driving periodic colonoscopy surveillance and polypectomy as needed. In this study, we explore a multimodal machine learning approach that integrates endoscopic imaging with clinical and pathology data to improve recurrence risk prediction and support individualized surveillance planning. Methods: We developed and evaluated a multimodal artificial intelligence (AI) model to predict post-polypectomy colorectal polyp recurrence using the ERCPMP-v5 dataset. The cohort included 217 patients with 796 high-resolution endoscopic RGB images and 21 endoscopic videos; video data were converted to still frames at 2 frames per second. Images and frames were resized to 224 × 224 pixels and normalized. Patient-level demographic, morphological (Paris, Kudo Pit, JNET), anatomical, and pathological variables were encoded using standard scaling for continuous features and one-hot encoding for categorical features. Visual representations were extracted using a pretrained Vision Transformer backbone (ViT-Base-Patch16-224) with frozen weights. Structured metadata (79 variables) was encoded using a multilayer perceptron. A late fusion framework used image and metadata representations to generate a recurrence probability via a sigmoid classifier; probabilities were thresholded at 0.5 for binary prediction. Model performance was evaluated on a held-out test set using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). We additionally compared fusion performance with image-only and metadata-only baselines. Predicted probabilities were translated to surveillance recommendations using risk tiers: low risk (0.00 ≤ p < 0.20), moderate risk (0.20 ≤ p < 0.50), and high risk (p ≥ 0.50). Results: On the test set, the multimodal fusion model achieved 90.4% accuracy, 86.7% precision, 83.1% recall, 84.9% F1-score, and an AUC of 0.920. The image-only model achieved 84.6% accuracy (AUC 0.880), and the metadata-only model achieved 81.9% accuracy (AUC 0.850), indicating improved performance with multimodal fusion. Risk stratification enabled surveillance recommendations of 1–3 years for low risk, 6–12 months for moderate risk, and 3–6 months for high risk. Conclusions: A late-fusion multimodal model integrating endoscopic imaging with structured clinical and pathology variables demonstrated excellent performance for predicting post-polypectomy recurrence and generated actionable risk-based surveillance intervals. This approach may support individualized follow-up planning and more efficient allocation of surveillance resources, while prioritizing timely evaluation for patients at higher predicted risk.

Background: Revisional bariatric surgery is increasingly performed due to weight regain, inadequate weight loss, or complications following primary bariatric procedures. These operations are technically challenging, and while laparoscopic surgery remains the standard approach, robotic platforms have gained adoption. However, comparative evidence on safety and efficiency remains limited and methodologically heterogeneous. Methods: A systematic review and meta-analysis was conducted in accordance with PRISMA guidelines. PubMed/Medline, Embase, Scopus, Web of Science, and Cochrane Central were searched. Comparative studies of robotic versus laparoscopic revisional bariatric surgery were included. To reduce bias from overlapping cohorts, we systematically identified and excluded duplicate datasets from national and institutional registries, prioritising the most contemporary and comprehensive reports, and retained partially overlapping studies only when the non-overlapping period spanned ≥3 years. Study quality was assessed using the Newcastle–Ottawa Scale. Outcomes analysed included length of hospital stay, operative time, postoperative complications, reoperation, anastomotic leakage and pneumonia. Random-effects models were used to generate pooled estimates. Results: Six retrospective studies encompassing 7199 robotic and 34,584 laparoscopic procedures were included. Robotic surgery was associated with a statistically significant reduction in length of hospital stay compared with laparoscopy (mean difference −1.06 days; 95% CI −1.97 to −0.15; I² = 84%). This finding was sensitive to the inclusion of a single large registry-based study. No significant differences were observed in the overall postoperative complications, reoperation rates, anastomotic leakage or pneumonia. Operative time did not differ significantly in the primary analysis. (I² = 93%), but sensitivity analyses suggested shorter procedure durations with laparoscopic surgery after exclusion of influential studies. Conclusions: In this systematic review and meta-analysis, robotic revisional bariatric surgery was associated with a shorter length of hospital stay and similar short-term safety outcomes to laparoscopic surgery. However, the very high statistical heterogeneity, observational design and potential for selection bias mean that these findings should be interpreted as hypothesis-generating rather than definitive evidence of superiority or equivalence. Larger, prospective, multi-centre studies with standardised outcome definitions and robust adjustment for confounding are needed to clarify the comparative clinical and cost-effectiveness of robotic versus laparoscopic approaches in revisional bariatric surgery.