New insights into how the colon functions and actually expels its contents have been revealed for the first time following decades of study by Flinders University researchers.

It promises new diagnostics tools and treatments for gastrointestinal disorders to address problems with bowel movements leading to constipation, diarrhoea and pain, affecting hundreds of millions of people worldwide.

Propulsion of intestinal contents is controlled by millions of neurons within the wall of the gut, known as the enteric nervous system.  Capable of operating independently of the brain, a functioning enteric nervous system is essential for life – but exactly how it functions has been a mystery.

By unravelling the neural circuits of the enteric nervous system in guinea pigs and humans Professor Marcello Costa and colleagues are able to understand how the enteric nervous system ensures that food is slowly mixed and propelled along the digestive tube, allowing for absorption of nutrients and excretion of waste.

“For the first time we have combined video recording intestinal movements with a pressure-measuring manometric probe, enabling movements, pressures and electrical activities to be recorded all at the same time within the colon.

“This powerful combination of techniques applied to a guinea pig colon identified several distinct neural mechanisms involved in the propulsion of colonic contents.

“This answers the deceptively simple question of how neural mechanisms within the colon manage the propulsion of bowel contents” Professor Costa says.

“The findings also show how studies in human and animals can be complementary, identifying fundamental mechanisms that are shared across species – in this case guinea pigs and humans.
“Currently we treat intestinal disorders by addressing the symptoms, such a stopping-up diarrhoea or softening stools to ease constipation, but as a result of this new understanding of the neural networks of the enteric system, clinicians may be able to develop treatments that treat the cause of the problems” Professor Costa says.

Scimex www.scimex.org/newsfeed/cracking-the-colon-code-new-light-shed-on-gut-function

Japanese scientists report a new method to construct 3D models from 2D images. The approach, which involves non-rigid registration with a blending of rigid transforms, overcomes several of the limitations in current methods. The researchers validate their method by applying it to the Kyoto Collection of Human Embryos and Fetuses, the largest collection of human embryos in the world, with over 45,000 specimens.
MRI and CT scans are standard techniques for acquiring 3D images of the body. These modalities can trace with unprecedented precision the location of an injury or stroke. They can even reveal the microscopic protein deposits seen in brain pathologies like Alzheimer’s disease. However, for the best resolution, scientists still depend on slices of the specimen, which is why cancer and other biopsies are taken. Once the information desired is acquired, scientists use algorithms that can put together the 2D slices to recreate a simulated 3D image. In this way, they can reconstruct an entire organ or even organism.
Stacking slices together to create a 3D image is akin to putting a cake together after it has been cut. Yes, the general shape is there, but the knife will cause certain slices to break so that the reconstructed cake never looks as beautiful as the original. While this might not upset the party of five-year olds who want to indulge, the party of surgeons looking for the precise location of a tumour are harder to appease.
“The sectioning process stretches, bends and tears the tissue. The staining process varies between samples. And the fixation process causes tissue destruction,” explains Nara Institute of Science and Technology (NAIST), Nara, Japan, Associate Professor Takuya Funatomi, who led the project.
Fundamentally, there are three challenges that emerge with the 3D reconstruction. First is non-rigid deformation, in which the position and orientation of various points in the original specimen have changed. Second is tissue discontinuity, where gaps may appear in the reconstruction if the registration fails. Finally, there is a scale change, where portions of the reconstruction are disproportional to their real size due to non-rigid registration.
For each of these problems, Associate Professor Takuya Funatomi and his research team proposed a solution that when combined resulted in a reconstruction that minimizes all three factors using less computational cost than standard methods.
“First, we represent non-rigid deformation using a small number of control points by blending rigid transforms,” says Funatomi. The small number of control points can be estimated robustly against the staining variation.
“Then we select the target images according to the non-rigid registration results and apply scale adjustment,” he continues.
The new method mainly focuses on a number of serial section images of human embryos from the Kyoto Collection of Human Embryos and Fetuses and could reconstruct 3D embryos with extraordinary success.
Notably, there are no MRI or CT scans of the samples, meaning no 3D models could be used as a reference for the 3D reconstruction. Further, wide variability in tissue damage and staining complicated the reconstruction.
Nara Institute of Science and Technology https://tinyurl.com/y4ealgow

by Dr Huub H. van Rossum
Recently, significant improvements have been made in understanding and applying moving average quality control (MA QC) that enable its practical implementation. These include the description of new and laboratory-specific MA QC optimization and validation methods, the online availability thereof, insights into operational requirements, and demonstration of practical implementation.
Introduction
Moving average quality control (MA QC) is the process of algorithmically averaging obtained test results and using that average for (analytical) quality control purposes. MA QC is generally referred to as patient-based real-time quality control (PBRTQC) because it is one of various methods (e.g. limit checks, delta checks, etc) that use patient results for (real-time) quality control. MA QC was first described over half a century ago as ‘average of normals’ [1]. Since then, it has evolved into a more general MA QC concept not necessarily based on using mean calculations of the obtained ‘normal’ test results [2]. Although MA QC has been available for a few decades, its adoption by laboratories has been limited due to the complexity of setting up the necessary procedures, operational challenges and a lack of evidence to justify its application and demonstrate its value. During the past 5|years, however, significant improvements have been made in the field of MA QC, and research studies have addressed all these issues. Consequently, true practical application of validated MA QC procedures to support analytical quality control in medical laboratories is now possible. Furthermore, the recent improvements may well change the way we perform daily analytical quality control in medical laboratories in the near future.
MA QC optimization and validation
The recent significant improvements in the field of MA QC include, first and foremost, the description of new methods to design and optimize laboratory-specific MA QC procedures and to enable validation of their actual error-detection performance [2–5]. These methods use realistic MA QC simulations based on laboratory-specific datasets and thus provide objective insights into MA QC error detection [2]. To enable practical implementation, the requirement that the number of MA QC alarms must be manageable is now acknowledged as essential and has been fulfilled when setting up MA QC [2, 6]. The newly developed methods use a novel metric to determine the error-detection performance: that is, the mean or median number of test results needed for error detection. One of the new methods presents these simulation results in bias-detection curves so that the optimal MA QC procedure can be selected, based on its overall error-detection performance [5]. An example of a bias-detection curve and its application is presented in Figure 1. After selecting the optimal MA QC settings, an MA validation chart can be used to obtain objective insights into the overall error-detection performance and the uncertainty thereof. Therefore, this chart can be seen as a validation of the MA QC procedure. An example of an MA validation chart is presented in Figure 2 and shows that the MA QC procedure will almost always (with 97.5% probability) detect a systematic error of −4% (or larger negative errors) within 20 test results.
Importantly, this method has become available to laboratories via the online MA Generator application, enabling them to design their own optimized and validated MA QC procedures [7]. Laboratories can now upload their own datasets of historical results, study potential MA QC settings using this simulation analysis and obtain their own laboratory-specific MA QC settings and MA validation charts. Several laboratories have demonstrated that this tool has enabled them to obtain relevant MA QC settings and thus implement MA QC [8, 9].
Integration of MA QC with internal QC
Measurement of internal quality control (iQC) samples is still the cornerstone of analytical quality control as performed in medical laboratories. For many tests, iQC alone is sufficient to assure and control the quality of obtained test results. For some tests, however, iQC itself is insufficient. The reasons for this are related to certain fundamental characteristics of iQC that include: lack of available (stable) control materials, its scheduled character, the risk of using non-commutable control samples and tests with a sigma metric score of ≤4. For several reasons, PBRTQC or, more specifically, MA QC is a particularly valuable and powerful way to support quality assurance in such cases.
First, if no (stable) QC materials are available it is impossible, or it becomes complicated, to use iQC. This is, for example, relevant for the erythrocyte sedimentation rate, serum indices or hemocytometry tests including erythrocyte mean corpuscular volume in particular. MA QC is possible as long as patient results are available. Second, the scheduled character of iQC becomes a limitation and a risk when temporary assay failures or rapid onset of critical errors occur between scheduled iQC. Because a new MA QC value can be calculated for each newly obtained test result, MA QC can be designed as a continuous and real-time QC tool. In this context, detection of temporary assay failure by MA QC between scheduled iQC has been demonstrated for a sodium case [10], and several examples of MA QC detection of rapid onset of critical errors have been published for both chemistry and hematological tests [11]. Third, because PBRTQC methods such as MA QC use obtained patient results, by design there is no commutability issue. Fourth, and finally, for some tests iQC is intrinsically limited in its ability to detect relevant clinical errors, due to the low ratio of biological variations to analytical variations, as reflected in low sigma metric values. Such tests require frequent iQC analysis and application of stringent control rules. However, even with such an intensive and strict iQC set-up, the probability of detecting clinically relevant errors remains limited [12]. In contrast, MA QC has the best error-detection performance for tests with a low sigma value [13].
For all these reasons, MA QC is ideal for supplementing analytical quality control by iQC. Recently, an approach was presented that integrated MA QC into the QC plan when iQC was found to be insufficient [9]. This approach was based on first determining whether one of the abovementioned iQC limitations applied to a test. If so, then iQC alone was considered insufficient and MA QC was studied, using the online MA Generator tool (www.huvaros.com) to obtain optimal MA QC settings and MA QC procedures to support the analytical quality control [7, 9]. The MA QC error-detection performance was validated using MA validation charts. These latter insights into MA QC error detection also enabled iQC measurements to be reduced. The MA QC procedures alone provided significant error-detection performance, so running iQC measures multiple times a day would add only limited error-detection performance. Therefore, it was decided to run the iQC only once a day and add the obtained MA QC procedures to the QC plan.
Others have taken this a step further and studied MA QC not only for tests with limited iQC performance but also for a much larger test selection, in order to reduce the number of iQC measures and more efficiently schedule and apply iQC [4]. This approach has been shown to be successful for a large commercial laboratory with high production numbers. Since the MA QC error-detection performance improves with an increasing number of test results and benefits from a small number of pathological test results, this approach may be particularly valuable to the larger commercial laboratories. For such an approach, the key is objective insights into the error-detection performance of MA QC procedures such as obtained using MA validation charts.
Implementation and application of MA QC for real-time QC in medical laboratories
The final aspect in which there have been significant improvements in recent years relates to the practical application of MA QC in medical laboratories. Recently, an International Federation of Clinical Chemistry and Laboratory Medicine working group was founded that summarized medical laboratories’ experiences of practically applying MA QC and formulated several recommendations for both MA QC software suppliers and medical laboratories that are working on, or are interested in, implementation of MA QC [14, 15]. Also, a step-by-step roadmap has recently been published to enable MA QC implementation [9]. The first two steps of this roadmap – i.e. selection of tests and obtaining MA QC settings for them – were discussed in the previous two paragraphs.
The next step would be to set up and configure the software used to implement MA QC in medical laboratories. If you are interested in applying MA QC in your laboratory, it is important to review the available software (e.g. analyser, middleware, LIS, third party) and to decide which will be used to run and apply MA QC. Your decision depends not only on the availability of suitable software in or for the laboratory, but also on the actual MA QC functionality present in the software packages.
The minimum software features that are necessary to enable practical implementation have been formulated [2, 15]. In my view, key elements would be that the software supports: exclusion of specified samples (non-patient materials, QC results, extreme results, etc), calculation of relevant MA QC algorithms, applying SD-based as well as non-statistical control limits (plain lower and upper control limits), proper real-time alarming and – depending on the MA QC optimization method – presentation of MA QC in a Levey–Jennings or accuracy graph. Figure 3 presents an example of MA QC in an accuracy graph as operated for real-time QC in my laboratory. To enable effective implementation of MA QC, all of these software features should be configured.
The final implementation step I wish to address here is the design of laboratory protocols for working up MA QC alarms, which determines the extent to which an error detected by an MA QC alarm is acknowledged. An important requirement is that all MA QC alarms should be worked up by means of this protocol.
As previously indicated, because MA QC can generate many more QC results and alarms than iQC, a critical requirement of every MA QC procedure is a manageable number of alarms. As a result, when an MA QC alarm occurs there is a reasonable chance of detecting error.
A first common action as part of the MA QC alarm protocol would be to run iQC. This provides a quick insight into the size of the error and enables rapid confirmation of large errors. As a second step, re-running of recently analysed samples (in addition to running iQC) enables temporary assay failures to be detected and can confirm or exclude errors not necessarily detectable by iQC. Also, finally, a review of recently analysed test results to identify a pre-analytical cause or a single patient with extreme but valid test results is often very useful as part of the MA QC alarm protocol. All these aspects have recently been discussed in greater detail [10, 14].
Conclusions
Altogether, the recent developments in the field of PBRTQC and, more specifically, MA QC now – finally – enable true practical implementation of MA QC in medical laboratories and allow more effective and efficient QC plans to be designed.
The authors
Huub H. van Rossum1,2 PhD
1 Department of Laboratory Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
2 Huvaros, Amsterdam, The Netherlands

E-mail: h.v.rossum@nki.nl

Working with model mice, post-mortem human brains, and people with schizophrenia, researchers at the RIKEN Center for Brain Science in Japan have discovered that a subtype of schizophrenia is related to abnormally high levels hydrogen sulfide in the brain.
Experiments showed that this abnormality likely results from a DNA-modifying reaction during development that lasts throughout life. In addition to providing a new direction for research into drug therapies, higher than normal levels of the hydrogen sulfide-producing enzyme can act as biomarker for this type of schizophrenia.

Diagnosing disorders of thought is easier when a reliable and objective marker can be found. In the case of schizophrenia, we have known for more than 30 years that it is associated with an abnormal startle response. Normally, we are not startled as much by a burst of noise if a smaller burst – called a prepulse – comes a little bit earlier. This phenomenon is called prepulse inhibition (PPI) because the early pulse inhibits the startle response. In people with schizophrenia, PPI is lowed, meaning that their startle response is not dampened as much as it should be after the prepulse.

The PPI test is a good behavioural marker, and although it cannot directly help us understand the biology behind schizophrenia, it was the starting point that led to current discoveries.

The researchers at RIKEN CBS began first looked for differences in protein expression between strains of mice that exhibit extremely low or extremely high PPI.
Ultimately, they found that the enzyme Mpst was expressed much more in the brains of the mouse strain with low PPI than in the strain with high PPI. Knowing that this enzyme helps produce hydrogen sulfide, the team then measured hydrogen sulfide levels and found that they were higher in the low-PPI mice.

"Nobody has ever thought about a causal link between hydrogen sulfide and schizophrenia," says team leader Takeo Toshikawa. "Once we discovered this, we had to figure out how it happens and if these findings in mice would hold true for people with schizophrenia."

First, to be sure that Mpst was the culprit, the researchers created an Mpst knockout version of the low-PPI mice and showed that their PPI was higher than that in regular low-PPI mice. Thus, reducing the amount of Mpst helped the mice become more normal. Next, they found that MPST gene expression was indeed higher in postmortem brains from people with schizophrenia than in those from unaffected people. MPST protein levels in these brains also correlated well with the severity of premortem symptoms.
Now the team had enough information to look at MPST expression as a biomarker for schizophrenia. They examined hair follicles from more than 150 people with schizophrenia and found that expression of MPST mRNA was much higher than people without schizophrenia. Even though the results were not perfect-indicating that sulfide stress does not account for all cases of schizophrenia-MPST levels in hair could be a good biomarker for schizophrenia before other symptoms appear.

Whether a person develops schizophrenia is related to both their genetics and the environment. Testing in mice and postmortem brains indicated that high MPST levels were associated with changes in DNA that lead to permanently altered gene expression. So, the next step was for the team to search for environmental factors that could result in permanently increased MPST production.

Because hydrogen sulfide can actually protect against inflammatory stress, the group hypothesized that inflammatory stress during early development might be the root cause. "We found that anti-oxidative markers – including the production of hydrogen sulfide – that compensate against oxidative stress and neuroinflammation during brain development were correlated with MPST levels in the brains of people with schizophrenia," says Yoshikawa.

He proposes that once excess hydrogen sulfide production is primed, it persists throughout life due to permanent epigenetic changes to DNA, leading to "sulfide stress" induced schizophrenia.

EurekAlert

Researchers have demonstrated that an optical technique known as Raman spectroscopy can be used to differentiate between benign and cancerous thyroid cells. The new study shows Raman spectroscopy’s potential as a tool to improve the diagnosis of thyroid cancer, which is the ninth most common cancer with more than 50,000 new cases diagnosed in the United States each year.
“Our encouraging results show that Raman spectroscopy could be developed into a new optical modality that can help avoid invasive procedures used to diagnose thyroid cancer by providing biochemical information that isn’t currently accessible,” said James W. Chan from the University of California, Davis, U.S.A. “This could have a major impact in the field of pathology and could lead to new ways to diagnose other diseases.”
A lump — or nodule — in the neck is a common symptom of thyroid cancer. However, most thyroid nodules aren’t cancerous. Ultrasound-guided fine needle aspiration biopsies are typically used to check for cancer by inserting a thin needle into the nodule to obtain cells that are prepared on a microscope slide, stained and analysed by a pathologist. For about 15 to 30 percent of cases, the pathologist cannot determine whether cells acquired from the biopsy are benign or malignant. For these cases, a surgical procedure known as a thyroidectomy is required to remove tissue, which provides more information for a more accurate diagnosis. The researchers turned to Raman spectroscopy as a possible solution because it is a non-invasive technique that requires no sample preparation or staining to determine subtle differences in the molecular composition of complex samples such as cells.
“We would like to use Raman spectroscopy to improve the pathologist’s analysis of the cells obtained with fine needle aspiration to reduce the number of thyroidectomies necessary,” said Chan. “This would both minimize surgical complications and reduce healthcare costs.”
For the new study, the researchers used a line-scan Raman microscope that allowed them to rapidly acquire Raman signals from an entire cell volume. This allowed them to more accurately capture the chemical composition of entire cells compared to other approaches that acquire a Raman spectrum from only part of a cell’s volume. Multivariate statistical methods and classification methods were then used to analyse the Raman data and classify the cells in an objective, unbiased manner.
The researchers applied this Raman spectroscopy approach to individual cells isolated from 10 patient thyroid nodules diagnosed as benign or cancerous. The data analysis identified unique spectral differences that could distinguish cancerous cells from benign with 97 percent diagnostic accuracy. They also showed that other subtypes could be identified by their spectral differences.
“These preliminary results are exciting because they involve single cells from human clinical samples, but more work will need to be done to take this from a research project to final clinical use,” said Chan.
The Optical Society (OSA) https://tinyurl.com/y6hw35z8