The role of the CXCR6/CXCL16 axis in the pathogenesis of fibrotic disease.

CXC chemokine receptor 6 (CXCR6), a seven-transmembrane domain G-protein-coupled receptor, plays a pivotal regulatory role in inflammation and tissue damage through its interaction with CXC chemokine ligand 16 (CXCL16). This axis is implicated in the pathogenesis of various fibrotic diseases and correlates with clinical parameters that indicate disease severity, activity, and prognosis in organ fibrosis, including afflictions of the liver, kidney, lung, cardiovascular system, skin, and intestines. Soluble CXCL16 (sCXCL16) serves as a chemokine, facilitating the migration and recruitment of CXCR6-expressing cells, while membrane-bound CXCL16 (mCXCL16) functions as a transmembrane protein with adhesion properties, facilitating intercellular interactions by binding to CXCR6. The CXCR6/CXCL16 axis is established to regulate the cycle of damage and repair during chronic inflammation, either through modulating immune cell-mediated intercellular communication or by independently influencing fibroblast homing, proliferation, and activation, with each pathway potentially culminating in the onset and progression of fibrotic diseases. However, clinically exploiting the targeting of the CXCR6/CXCL16 axis requires further elucidation of the intricate chemokine interactions within fibrosis pathogenesis. This review explores the biology of CXCR6/CXCL16, its multifaceted effects contributing to fibrosis in various organs, and the prospective clinical implications of these insights.

Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study.

BACKGROUND: Due to the complexity and numerous comorbidities associated with Crohn's disease (CD), the incidence of postoperative complications is high, significantly impacting the recovery and prognosis of patients. Consequently, additional studies are required to precisely predict short-term major complications following intestinal resection (IR), aiding surgical decision-making and optimizing patient care. AIM: To construct novel models based on machine learning (ML) to predict short-term major postoperative complications in patients with CD following IR. METHODS: A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022. The study participants were randomly allocated to either a training cohort or a validation cohort. The logistic regression and random forest (RF) were applied to construct models in the training cohort, with model discrimination evaluated using the area under the curves (AUC). The validation cohort assessed the performance of the constructed models. RESULTS: Out of the 259 patients encompassed in the study, 5.0% encountered major postoperative complications (Clavien-Dindo ≥ III) within 30 d following IR for CD. The AUC for the logistic model was 0.916, significantly lower than the AUC of 0.965 for the RF model. The logistic model incorporated a preoperative CD activity index (CDAI) of ≥ 220, a diminished preoperative serum albumin level, conversion to laparotomy surgery, and an extended operation time. A nomogram for the logistic model was plotted. Except for the surgical approach, the other three variables ranked among the top four important variables in the novel ML model. CONCLUSION: Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complications in patients with CD, with the RF model showing more superiority. A preoperative CDAI of ≥ 220, a diminished preoperative serum albumin level, and an extended operation time might be the most crucial variables. The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

Laparoscopic vs open surgery in ileostomy reversal in Crohn's disease: A retrospective study.

BACKGROUND: Although minimally invasive surgery is becoming more commonly applied for ileostomy reversal (IR), there have been relatively few studies of IR for patients with Crohn's disease (CD). It is therefore important to evaluate the potential benefits and risks of laparoscopy for patients with CD. AIM: To compare the safety, feasibility, and short-term and long-term outcomes of laparoscopic IR (LIR) vs open IR (OIR) for the treatment of CD. METHODS: The baseline characteristics, operative data, and short-term (30-d) and long-term outcomes of patients with CD who underwent LIR and OIR at our institution between January 2017 and January 2020 were retrieved from an electronic database and retrospectively reviewed. RESULTS: Of the 60 patients enrolled in this study, LIR was performed for 48 and OIR for 12. There were no statistically significant differences in baseline characteristics, operation time, intraoperative blood loss, days to flatus and soft diet, postoperative complications, hospitalization time, readmission rate within 30 d, length of hospitalization, hospitalization costs, or reoperation rate after IR between the two groups. However, patients in the LIR group more frequently required lysis of adhesions as compared to those in the OIR group (87.5% vs 41.7%, respectively, P < 0.05). Notably, following exclusion of patients who underwent enterectomy plus IR, OIR was more advantageous in terms of postoperative recovery of gastrointestinal function and hospitalization costs. CONCLUSION: The safety and feasibility of LIR for the treatment of CD are comparable to those of OIR with no increase in intraoperative or postoperative complications.

Ibuprofen-mediated potential inhibition of biofilm development and quorum sensing in Pseudomonas aeruginosa.

AIMS: Pseudomonas aeruginosa is one of the leading causes of opportunistic and hospital-acquired infections worldwide, which is frequently linked with clinical treatment difficulties. Ibuprofen, a widely used non-steroidal anti-inflammatory drug, has been previously reported to exert antimicrobial activity with the specific mechanism. We hypothesized that inhibition of P. aeruginosa with ibuprofen is involved in the quorum sensing (QS) systems. MAIN METHODS: CFU was utilized to assessed the growth condition of P. aeruginosa. Crystal violent staining and acridine orange staining was used to evaluate the biofilm formation and adherence activity. The detection of QS virulence factors such as pyocyanin, elastase, protease, and rhamnolipids were applied to investigation the anti-QS activity of ibuprofen against P. aeruginosa. The production of 3-oxo-C12-HSL and C4-HSL was confirmed by liquid chromatography/mass spectrometry analysis. qRT-PCR was used to identify the QS-related gene expression. Furthermore, we explored the binding effects between ibuprofen and QS-associated proteins with molecular docking. KEY FINDINGS: Ibuprofen inhibits P. aeruginosa biofilm formation and adherence activity. And the inhibitory effects of ibuprofen on C4-HSL levels were concentration-dependent (p < 0.05), while it has no effect on 3-oxo-C12-HSL. Moreover, ibuprofen attenuates the production of virulence factors in P. aeruginosa (p < 0.05). In addition, the genes of QS system were decreased after the ibuprofen treatment (p < 0.05). Of note, ibuprofen was binding with LuxR, LasR, LasI, and RhlR at high binding scores. SIGNIFICANCE: The antibiofilm and anti-QS activity of ibuprofen suggest that it can be a candidate drug for the treatment of clinical infections with P. aeruginosa.

An Extreme Learning Machine Based on Artificial Immune System.

Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training speed by randomly generating the relevant parameters of the input layer and the hidden layer. However, due to the randomly generated parameters, some generated "bad" parameters may be introduced to bring negative effect on the final generalization ability. To overcome such drawback, this paper combines the artificial immune system (AIS) with ELM, namely, AIS-ELM. With the help of AIS's global search and good convergence, the randomly generated parameters of ELM are optimized effectively and efficiently to achieve a better generalization performance. To evaluate the performance of AIS-ELM, this paper compares it with relevant algorithms on several benchmark datasets. The experimental results reveal that our proposed algorithm can always achieve superior performance.