Pancreatic head resections are associated with postoperative morbidity, primarily due to infectious complications. Microbiota in these infections is critical, and selective decontamination of the digestive tract (SDD) aims to mitigate this risk by targeting pathogenic organisms while preserving beneficial flora. This study focuses on the impact of SDD on bacterial shifts and resistance patterns in pancreatic head resections. All patients who underwent pancreatic head resections either between January 2012 to August 2018 (non-SDD group) or between January 2019 and December 2021 (SDD group) were included. Propensity score-matched analysis was performed to compare bacterial presence and resistance patterns in bile duct smear tests and postoperative complications. Positive bile duct-smear tests were observed more often in the non-SDD group (63.5%) than in the SDD group (51%). Moreover, the SDD group exhibited a significant reduction in the median number of bacterial species in bile ducts compared to the non-SDD group (p=0.04). However, a notable increase in gram-negative species was observed in the SDD group. The SDD group experienced higher rates of postoperative complications, including relevant pancreatic fistulas (24.8% vs. 11.6%, p<0.01) and delayed gastric emptying (33.8% vs. 21.9%, p<0.01). No significant differences in antibiotic resistance patterns were observed. SDD in pancreatic head resections reduces bacterial load within the biliary tract but is associated with a shift towards more gram-negative species and higher rates of severe postoperative complications. These findings suggest that SDD may negatively impact postoperative outcomes and should be carefully considered in clinical practice.

Significance: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.Aim: We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.Approach: Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using a MATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leave-one-out method. Validation of the algorithm was accomplished with 13 independent test samples.Results: Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.Conclusion: IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications.

Pancreatic surgery is highly demanding and mainly performed to treat benign and malignant lesions of the pancreas. Due to the complexity of these procedures, the patient’s postoperative morbidity remains an important factor for the performance. Especially in pancreatic surgery for malignant lesions, a radical resection of the tumor in combination with sparing of physiological tissue is the main focus to maintain the organ’s endo- and exocrine function and therefore the patient’s quality of life. However, pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal entities even in localized diseases, with an average overall survival of 28 to 54 months.^{1 – 3} In advanced stage PDAC, lymph node and margin resection statuses are both leading factors for patient outcome.⁴

Background: A lymphoepithelial cyst of the pancreas is a rare benign lesion that is difficult to diagnose preoperatively and challenging in distinguishing from potentially malignant cystic pancreatic neoplasms. A diagnostic step-up approach is recommended to clarify the lesion’s dignity and specify a treatment plan. Case presentation: Here, we describe a case of a 51-year-old male European with a lymphoepithelial cyst of the pancreas mimicking malignant features in a mid-age male patient with abdominal pain and unintended weight loss. Conclusion: Patients with indeterminate cystic pancreatic lesions should be examined by a multidisciplinary diagnostic team in a step-up approach to clarify the lesion’s entity. In the case of incidentally found lymphoepithelial cysts of the pancreas, a watchful waiting strategy might be clinically reasonable if the diagnosis is proven.

Liver cancer, including hepatocellular carcinoma (HCC), cholangiocellular carcinoma (CCC), and metastases, presents diagnostic challenges during surgery due to its infiltrative nature. Accurate intraoperative classification and margin assessment are crucial for improving outcomes. Current methods, like frozen section analysis, are time-consuming and subjective, necessitating rapid, objective alternatives. This study assessed fiber-based attenuated total reflection infrared (ATR IR) spectroscopy combined with supervised machine learning for intraoperative liver tumor classification based on a holistic biochemical signature approach. Fresh liver tissue from 69 surgical patients was analyzed using a probe consisting of Ge ATR crystal and silver halide fibers. Supervised algorithms reliably classified normal tissue and tumor subtypes (HCC, CCC, metastases) using cross-validation and independent test sets. Normal liver tissue was distinguished primarily by differences in glycogen content and structural compactness of tumor tissue. Normal and tumor tissues were differentiated with a sensitivity of 0.89 and a specificity of 0.92. The accuracy of spectroscopic classification is 0.90. The three-group classification of tumor subtypes also yielded an average accuracy of 0.90. HCC is characterized by a higher glycogen content compared to CCC and metastases and can be identified spectroscopically with high reliability. CCC showed distinct protein-associated spectral signatures, while metastases exhibited unique profiles reflecting their different origins. In a minority of cases, misclassifications occurred, indicating potential for further refinement. Fiber-based ATR IR spectroscopy in combination with machine learning provides a rapid, objective, and highly accurate intraoperative tool for liver tumor classification. This label-free biochemical approach may enhance surgical precision and reduce recurrence risks across the full range of solid tumor entities.

Background: Pancreatic ductal adenocarcinoma (PDAC) continues to pose profound challenges within the field of oncology due to its notorious resistance to existing therapies and constant high mortality rates. The recent emergence of three‐dimensional patient‐derived organoid (PDO) models marks a significant advancement, opening new avenues for exploring cancer biology and assessing therapeutic approaches.Aims: The aim of this study focuses on the innovative use of Fourier‐transform infrared (FT‐IR) spectroscopy to analyze PDAC organoids, thus illuminating their biochemical intricacies.Materials and Methods: In this study, PDAC organoids, cultivated from specimens sourced from cancer patients, were subjected to FT‐IR spectroscopic imaging. By examining the spectral data within the critical fingerprint region (950–1800 cm⁻¹), and employing principal component analysis (PCA), biochemical disparities were detected and analyzed.Results: The results revealed distinct spectral profiles corresponding to different sample preparation techniques, which in turn highlighted variations in protein content and structure. PCA revealed a high homogeneity within classes and minimal passage number influence on spectral profiles, with variations in lipid content and protein profiles. Significantly, the biochemical fingerprint of these PDOs closely mirrored that of the original human tissue samples.Conclusion: This investigation underscores the efficacy of molecular spectroscopy as a non‐invasive method for profound characterization of PDAC organoids, enhancing our comprehension of tumor biochemistry. The capacity for swift and precise biochemical profiling of PDOs via molecular spectroscopy heralds a promising future for this technique in the realms of cancer diagnostics and personalized medicine.