Goodbye Hartmann trial: a prospective, international, multicenter, observational study on the current use of a surgical procedure developed a century ago

Background: Literature suggests colonic resection and primary anastomosis (RPA) instead of Hartmann's procedure (HP) for the treatment of left-sided colonic emergencies. We aim to evaluate the surgical options globally used to treat patients with acute left-sided colonic emergencies and the factors that leading to the choice of treatment, comparing HP and RPA. Methods: This is a prospective, international, multicenter, observational study registered on ClinicalTrials.gov. A total 1215 patients with left-sided colonic emergencies who required surgery were included from 204 centers during the period of March 1, 2020, to May 31, 2020. with a 1-year follow-up. Results: 564 patients (43.1%) were females. The mean age was 65.9 ± 15.6 years. HP was performed in 697 (57.3%) patients and RPA in 384 (31.6%) cases. Complicated acute diverticulitis was the most common cause of left-sided colonic emergencies (40.2%), followed by colorectal malignancy (36.6%). Severe complications (Clavien-Dindo ≥ 3b) were higher in the HP group (P < 0.001). 30-day mortality was higher in HP patients (13.7%), especially in case of bowel perforation and diffused peritonitis. 1-year follow-up showed no differences on ostomy reversal rate between HP and RPA. (P = 0.127). A backward likelihood logistic regression model showed that RPA was preferred in younger patients, having low ASA score (≤ 3), in case of large bowel obstruction, absence of colonic ischemia, longer time from admission to surgery, operating early at the day working hours, by a surgeon who performed more than 50 colorectal resections. Conclusions: After 100 years since the first Hartmann's procedure, HP remains the most common treatment for left-sided colorectal emergencies. Treatment's choice depends on patient characteristics, the time of surgery and the experience of the surgeon. RPA should be considered as the gold standard for surgery, with HP being an exception

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Induction of Autophagic Cell Death by Thymoquinone in Docetaxel Resistant Prostate Cancer Cells

Aim: Acquired docetaxel (DOC) resistance of prostate cancer (PCa) is still a clinical problem. In addition to failure in chemotherapy treatment, it causes tumor recurrence. Therefore, novel and more effective compounds are needed in DOC-resistant PCa treatment. This study aimed to investigate the possible cytotoxic and cell death-inducing activities of thymoquinone (TQ), one of the main active components of Nigella sativa L., on DOC-resistant prostate cancer cells. Material and Methods: DOC-resistant PC3 cells (DOC-R/PC3) were developed by the continuous culture with increment concentrations of DOC (1-10 nM) until they improved their growth and division abilities. The cell viability was determined by MTT assay. The MuseTM Annexin V & Dead Cell kit was performed to detect apoptotic cell death. Autophagic vacuoles were observed by staining autophagic vacuoles. The levels of LC3I, LC3II and Beclin-1 proteins were investigated via western blot analysis. Results: TQ inhibited the viability of DOC-R/PC3 cells in a dose- and time-dependent manner (p=0.014). The IC50 value of TQ for DOC-R/PC3 cells was calculated as 60 µM at 72 h. Treatment of TQ did not induce apoptotic cell death in DOC-resistant prostate cancer cells but induced the formation of autophagic vacuoles. Moreover, Beclin-1 and LC3-II protein levels were increased in TQ-treated DOC-R/PC3 cells, however, LC3-I levels were decreased in DOC-R/PC3 cells. Conclusion: All these results show that TQ may become a new therapeutic target for DOC-resistant prostate cancer in the future

Modeling of Spatio-Temporal Hawkes Processes With Randomized Kernels

We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process. We replace the spatial kernel calculations by randomized Fourier feature-based transformations. The introduced randomization by this representation provides flexibility while modeling the spatial excitation between events. Moreover, the system described by the process is expressed within closed-form in terms of scalable matrix operations. During the optimization, we use maximum likelihood estimation approach and gradient descent while properly handling positivity and orthonormality constraints. The experiment results show the improvements achieved by the introduced method in terms of fitting capability in synthetic and real datasets with respect to the conventional inference methods in the spatio-temporal Hawkes process literature. We also analyze the triggering interactions between event types and how their dynamics change in space and time through the interpretation of learned parameters