Acupuncture reduces the time from extubation to 'ready for discharge' from the post anaesthesia care unit: results from the randomised controlled AcuARP trial

Acupuncture may improve peri-operative care as it reduces post-operative symptoms, such as pain, nausea and vomiting, or sedation. This patient-assessor blinded, randomised trial in 75 women undergoing gynaecologic laparoscopy evaluated the effects of acupuncture combined with a standardised anaesthetic regimen (ACU) on post-anaesthetic recovery, when compared to acupressure (APU) or standard anaesthesia alone (CON). Main outcome measure was the time from extubation to 'ready for discharge' from recovery as assessed by validated questionnaires. The main outcome differed significantly between groups (p = 0.013). Median time to ready for discharge in the ACU group (30 (IQR: 24-41) min) was 16 minutes (35%) shorter than in the CON group (46 (36-64) min;p = 0.015) and tended to be shorter than in the APU group (43 (31-58) min;p = 0.08). Compared to CON (p = 0.029), median time to extubation was approximately 7 minutes shorter in both, the ACU and the APU group. No acupuncture or acupressure-related side-effects could be observed. A difference in time to recovery of 16 minutes compared to standard alone can be considered clinically relevant. Thus, results of this study encourage the application of acupuncture in gynaecological laparoscopy as it improves postanaesthetic recovery

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Communication in bacteria: an ecological and evolutionary perspective

Individual bacteria can alter their behaviour through chemical interactions between organisms in microbial communities - this is generally referred to as quorum sensing. Frequently, these interactions are interpreted in terms of communication to mediate coordinated, multicellular behaviour. We show that the nature of interactions through quorum-sensing chemicals does not simply involve cooperative signals, but entails other interactions such as cues and chemical manipulations. These signals might have a role in conflicts within and between species. The nature of the chemical interaction is important to take into account when studying why and how bacteria react to the chemical substances that are produced by other bacteria

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Use of green fluorescent protein as a marker for ecological studies of activated sludge communities

The potential of the green fluorescent protein (GFP) as a marker gene for ecological investigations of an activated sludge community was assessed. By inserting the hybrid transposon mini-Tn5 gfp into the chromosome of Pseudomonas putida KT2442 a strongly fluorescent mutant was obtained. This strain was used for in vivo tracking of individual cells after introduction into a simple sludge microcosm. It is demonstrated that the observed reduction of introduced bacteria from sewage is mainly the result of predation by protozoa. The feasibility of combining detection of GFP fluorescence with whole cell hybridization employing fluorescently labeled, rRNA-targeted oligonucleotides in paraformaldehyde fixed samples is demonstrated

Genome Methylation Accurately Predicts Neuroendocrine Tumor Origin: An Online Tool

PURPOSE: The primary origin of neuroendocrine tumor metastases can be difficult to determine by histopathology alone, but is critical for therapeutic decision making. DNA methylation-based profiling is now routinely used in the diagnostic workup of brain tumors. This has been enabled by the availability of cost-efficient array-based platforms. We have extended these efforts to augment histopathologic diagnosis in neuroendocrine tumors. EXPERIMENTAL DESIGN: Methylation data was compiled for 69 small intestinal, pulmonary, and pancreatic neuroendocrine tumors. These data were used to build a ridge regression calibrated random forest classification algorithm (neuroendocrine neoplasm identifier, NEN-ID). The model was validated during 3 × 3 nested cross-validation and tested in a local and an external cohort (n = 198 cases). RESULTS: NEN-ID predicted the origin of tumor samples with high accuracy (>95%). In addition, the diagnostic approach was determined to be robust across a range of possible confounding experimental parameters, such as tumor purity and array quality. A software infrastructure and online user interface were built to make the model available to the scientific community. CONCLUSIONS: This DNA methylation-based prediction model can be used in the workup for patients with neuroendocrine tumors of unknown primary. To facilitate validation and clinical implementation, we provide a user-friendly, publicly available web-based version of NEN-ID