Complications of canine tonsillectomy by clamping technique combined with monopolar electrosurgery – a retrospective study of 39 cases

BackgroundCanine tonsillectomy is performed due to acute or chronic tonsillitis, neoplasia, trauma or occasionally brachycephalic obstructive airway syndrome. Several tonsillectomy techniques are used but information about surgical complications is scarce. This retrospective study of patient records at the University Animal Hospital aimed to investigate complications related to canine tonsillectomy performed by 20-min clamping combined with monopolar electrosurgery.Inclusion criteria were bilateral tonsillectomy performed with “20-min clamping technique combined with monopolar electrosurgery without suture or ligation”. Exclusion criteria were unilateral tonsillectomy, tonsillar neoplasia, additional surgical procedures other than tonsillectomy, cases where sutures were used initially, and cases where unspecified or other methods of tonsillectomy were used. The search of the patient records of the University Animal Hospital included a 10-year period. Complications that required additional anaesthesia were defined as major complications. Minor complications were handled during surgery or after surgery without surgical intervention.ResultsOf 39 dogs that fulfilled the inclusion criteria, 11 dogs had complications and out of those 1 dog had two complications. Altogether, of the 12 complications, 2 were classified as major complications and 10 as minor.The most frequent complication was bleeding from the surgical site, in total 11 incidences; 10 dogs had an incidence of bleeding and out of those, 1 dog bled twice, both during and after surgery. Of these 10 dogs that bled, seven incidences of bleeding occurred during surgery and four incidences occurred after surgery. The two dogs with major complications were re-anaesthetized due to bleeding after surgery. No lethal complications occurred and all dogs survived to discharge.ConclusionsBleeding during and after surgery was a common complication in dogs after bilateral tonsillectomy using “20-min clamping technique combined with monopolar electrocautery”. Revision intervention was often needed, sometimes urgently. Although no comparison was made with another technique, the studied technique should be used with caution

Antibacterial efficiency of Finnish spice essential oils against pathogenic and spoilage bacteria

Reprinted with permission from the Journal of Food Protection. Copyright held by the International Association for Food Protection, Des Moines, Iowa, U.S.A

Capturing complex tumour biology in vitro: Histological and molecular characterisation of precision cut slices

Precision-cut slices of in vivo tumours permit interrogation in vitro of heterogeneous cells from solid tumours together with their native microenvironment. They offer a low throughput but high content in vitro experimental platform. Using mouse models as surrogates for three common human solid tumours, we describe a standardised workflow for systematic comparison of tumour slice cultivation methods and a tissue microarray-based method to archive them. Cultivated slices were compared to their in vivo source tissue using immunohistochemical and transcriptional biomarkers, particularly of cellular stress. Mechanical slicing induced minimal stress. Cultivation of tumour slices required organotypic support materials and atmospheric oxygen for maintenance of integrity and was associated with significant temporal and loco-regional changes in protein expression, for example HIF-1α. We recommend adherence to the robust workflow described, with recognition of temporal-spatial changes in protein expression before interrogation of tumour slices by pharmacological or other means

The Subcellular Distribution of Acyltransferases which Catalyze the Synthesis of Phosphoglycerides

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65213/1/j.1432-1033.1969.tb00602.x.pd

Precision systems medicine in urological Tumors – Molecular profiling and functional testing

Background: Most precision cancer medicine efforts are based on the identification of oncogenic driver mutations by genome sequencing. We believe and have emerging evidence that this will miss therapeutic opportunities and additional technologies, such as cell-based functional testing should be included. Pioneering studies in leukemia indicate the value of ex-vivo drug testing to identify novel, clinically actionable therapeutic opportunities. Methods: Using conditional re-programming technology, we established patient-derived cells (PDCs) from castration-resistant prostate cancer (CRPC)3 and renal cell cancer (RCC) in order to pilot precision systems medicine in solid tumours. The PDCs were compared with primary tumour tissue by genomic profiling and then subjected to drug sensitivity profiling with >306 approved and investigational oncology drugs. Results: Here, we generated both benign and malignant PDCs from prostate tissue, including six benign PDCs that were androgen receptor (AR)-negative, basal/transit-amplifying phenotype, but could re-express AR in 3D-culture. The PDCs from a CRPC patient displayed multiple CNAs, some of which were shared with the parental tumor. The cancer-selective drug profile for these PDCs showed sensitivity to taxanes, navitoclax, bexarotene, tretinoin, oxaliplatin and mepacrine3. RCC displays extensive intra-tumour heterogeneity and clonal evolution. There is, however, very little information on how much this impacts drug sensitivities. Therefore, we generated several PDCs from each RCC patient across multiple tumour regions. We verified their clonal relationship with the uncultured tumour tissue by NGS and performed drug sensitivity profiling. The PDCs retained CNAs and driver mutations in e.g. VHL, PBRM1, PIK3C2A, KMD5C, TSC2 genes present in the original tumour tissue. Drug testing with 461 oncology drugs identified shared vulnerability among the multiple PDCs to pazopanib and temsirolimus that inhibit well-established renal cancer pathways EGFR/PDGFR/ FGFR and mTOR. Importantly, however, the individual PDC from different regions in one patient also showed distinct drug response profiles, confirming that genomic heterogeneity leads to variability in drug responses. Conclusions: Our aim is to generate molecular profiles and drug testing data using representative PDCs from each patient to help clinicians in treatment decision and to facilitate the early selection of the best drug candidates for clinical development. We believe this approach will help to personalize treatment, prioritize drugs for clinical testing, provide for intelligent selection of drug combinations and improve treatment outcomes.Non peer reviewe

Breast cancer outcome prediction with tumour tissue images and machine learning

Purpose Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input. Methods Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients. Results In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33–3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20–3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55–0.65), as compared to 0.58 (95% CI 0.53–0.63) for human expert predictions based on the same TMA samples. Conclusions Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer