Influence of pre-anaesthetic thoracic radiographs on ASA physical status classification and anaesthetic protocols in traumatized dogs and cats

The purpose of this study was to evaluate if pre-anaesthetic thoracic radiographs contribute to the anaesthetic management of trauma patients by comparing American Society of Anesthesiologists Physical Status Classification (ASA grade) with and without information from thoracic radiography findings. Case records of 157 dogs and cats being anaesthetized with or without post-traumatic, pre-anaesthetic chest radiographs were retrospectively evaluated for clinical parameters, radiographic abnormalities and anaesthetic protocol. Animals were retrospectively assigned an ASA grade. ASA grades, clinical signs of respiratory abnormalities and anaesthesia protocols were compared between animals with and without chest radiographs. The group of animals without pre-anaesthetic radiographs was anaesthetized earlier after trauma and showed less respiratory abnormalities at presentation. The retrospectively evaluated ASA grade significantly increased with the information from thoracic radiography. Animals with a higher ASA grade were less frequently mechanically ventilated. Pre-anaesthetic radiographs may provide important information to assess the ASA grade in traumatized patients and may therefore influence the anesthesia protocol.

Towards the introduction of the 'Immunoscore' in the classification of malignant tumours.

The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named 'Immunoscore' has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Clinical herniation – how to improve differentiation between WFNS grade IV and V

Objective: Current data show a favorable outcome after poor grade subarachnoid hemorrhage (SAH) in up to 50% of patients. This limits the use of the WFNS scale for drawing treatment decisions. We therefore analyzed how clinical signs of herniation might improve the existing WFNS grading. Therefore we compared the current WFNS grading and a modified WFNS grading with respect to outcome. Method: We performed a retrospective study including 182 poor grade SAH patients. Patients were graded according to the original WFNS scale and additionally into a modified classification the “WFNS herniation” (WFNSh grade IV: no herniation; grade V clinical signs of herniation). Outcome was compared between these two grading systems with respect to the dichotomized modified Rankin scale after 6 months. Results: The WFNS and WFNSh showed a positive predictive value (PPV) for poor outcome of 74.3% (OR 3.79, 95% confidence interval [CI]=1.94, 7.54) and 85.7% (OR 8.27, 95% CI=3.78, 19.47), respectively. With respect to mortality the PPV was 68.3% (OR 3.9, 95% CI=2.01, 7.69) for the WFNS grade V and 77.9% (OR 6.22, 95% CI=3.07, 13.14) for the WFNSh grade V. Conclusions: Using positive clinical signs of herniation instead of “no response to pain stimuli” (motor Glasgow Coma Scale Score) can improve WFNS V grading. Using this modification, prediction of poor outcome or death improves.

DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

BACKGROUND Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients. METHODS A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation. RESULTS Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis. CONCLUSIONS Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups

Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.

Pathology: Classification and Immunoprofile

The classification of neuroendocrine neoplasms (NENs) has been evolving steadily over the last decades. Important prognostic factors of NENs are their proliferative activity and presence/absence of necrosis. These factors are reported in NENs of all body sites; however, the terminology as well as the exact rules of classification differ according to the location of the primary tumor. Only in gastroenteropancreatic (GEP) NENs a formal grading is performed. This grading is based on proliferation assessed by the mitotic count and/or Ki-67 proliferation index. In the lung, NEN grading is an intrinsic part of the tumor designation with typical carcinoids corresponding to neuroendocrine tumor (NET) G1 and atypical carcinoids to NET G2; however, the presence or absence of necrotic foci is as important as proliferation for the differentiation between typical and atypical carcinoids. Immunohistochemical markers can be used to demonstrate neuroendocrine differentiation. Synaptophysin and chromogranin A are, to date, the most reliable and most commonly used for this purpose. Beyond this, other markers can be helpful, for example in the situation of a NET metastasis of unknown primary, where a hormonal profile or a panel of transcription factors can give hints to the primary site. Many immunohistochemical markers have been shown to correlate with prognosis but are not used in clinical practice, for example cytokeratin 19 and KIT expression in pancreatic NETs. There is no predictive biomarker in use, with the exception of somatostatin receptor (SSTR) 2 expression for predicting the amenability of a tumor to in vivo SSTR targeting for imaging or therapy.

Operative Treatment of Acute Fractures of the Tarsal Navicular Body: Midterm Results With a New Classification.

BACKGROUND Treatment of displaced tarsal navicular body fractures usually consists of open reduction and internal fixation. However, there is little literature reporting results of this treatment and correlation to fracture severity. METHODS We report the results of 24 patients treated in our institution over a 12-year period. Primary outcome measurements were Visual-Analogue-Scale Foot and Ankle score (VAS-FA), AOFAS midfoot score, and talonavicular osteoarthritis at final follow-up. According to a new classification system reflecting talonavicular joint damage, 2-part fractures were classified as type I, multifragmentary fractures as type II, and fractures with talonavicular joint dislocation and/or concomitant talar head fractures as type III. Spearman's coefficients tested this classification's correlation with the primary outcome measurements. Mean patient age was 33 (range 16-61) years and mean follow-up duration 73 (range 24-159) months. RESULTS Average VAS-FA score was 74.7 (standard deviation [SD] 16.9), and average AOFAS midfoot score was 83.8 (SD = 12.8). Final radiographs showed no talonavicular arthritis in 5 patients, grade 1 in 7, grade 2 in 3, grade 3 in 6, and grade 4 in 1 patient. Two patients had secondary or spontaneous talonavicular fusion. Spearman coefficients showed strong correlation of the classification system with VAS-FA score (r = -0.663, P < .005) and talonavicular arthritis (r = 0.600, P = .003), and moderate correlation with AOFAS score (r = -.509, P = .011). CONCLUSION At midterm follow-up, open reduction and internal fixation of navicular body fractures led to good clinical outcome but was closely related to fracture severity. A new classification based on the degree of talonavicular joint damage showed close correlation to clinical and radiologic outcome. LEVEL OF EVIDENCE Level IV, retrospective case series.

The comprehensiveness of the ESHRE/ESGE classification of female genital tract congenital anomalies: a systematic review of cases not classified by the AFS system.

STUDY QUESTION How comprehensive is the recently published European Society of Human Reproduction and Embryology (ESHRE)/European Society for Gynaecological Endoscopy (ESGE) classification system of female genital anomalies? SUMMARY ANSWER The ESHRE/ESGE classification provides a comprehensive description and categorization of almost all of the currently known anomalies that could not be classified properly with the American Fertility Society (AFS) system. WHAT IS KNOWN ALREADY Until now, the more accepted classification system, namely that of the AFS, is associated with serious limitations in effective categorization of female genital anomalies. Many cases published in the literature could not be properly classified using the AFS system, yet a clear and accurate classification is a prerequisite for treatment. STUDY DESIGN, SIZE AND DURATION The CONUTA (CONgenital UTerine Anomalies) ESHRE/ESGE group conducted a systematic review of the literature to examine if those types of anomalies that could not be properly classified with the AFS system could be effectively classified with the use of the new ESHRE/ESGE system. An electronic literature search through Medline, Embase and Cochrane library was carried out from January 1988 to January 2014. Three participants independently screened, selected articles of potential interest and finally extracted data from all the included studies. Any disagreement was discussed and resolved after consultation with a fourth reviewer and the results were assessed independently and approved by all members of the CONUTA group. PARTICIPANTS/MATERIALS, SETTING, METHODS Among the 143 articles assessed in detail, 120 were finally selected reporting 140 cases that could not properly fit into a specific class of the AFS system. Those 140 cases were clustered in 39 different types of anomalies. MAIN RESULTS AND THE ROLE OF CHANCE The congenital anomaly involved a single organ in 12 (30.8%) out of the 39 types of anomalies, while multiple organs and/or segments of Müllerian ducts (complex anomaly) were involved in 27 (69.2%) types. Uterus was the organ most frequently involved (30/39: 76.9%), followed by cervix (26/39: 66.7%) and vagina (23/39: 59%). In all 39 types, the ESHRE/ESGE classification system provided a comprehensive description of each single or complex anomaly. A precise categorization was reached in 38 out of 39 types studied. Only one case of a bizarre uterine anomaly, with no clear embryological defect, could not be categorized and thus was placed in Class 6 (un-classified) of the ESHRE/ESGE system. LIMITATIONS, REASONS FOR CAUTION The review of the literature was thorough but we cannot rule out the possibility that other defects exist which will also require testing in the new ESHRE/ESGE system. These anomalies, however, must be rare. WIDER IMPLICATIONS OF THE FINDINGS The comprehensiveness of the ESHRE/ESGE classification adds objective scientific validity to its use. This may, therefore, promote its further dissemination and acceptance, which will have a positive outcome in clinical care and research. STUDY FUNDING/COMPETING INTERESTS None.

Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of epidemiological and clinical factors in survival of patients with de novo myelodysplastic syndrome at the University of Texas MD Anderson cancer center

Little is known about epidemiological markers that are associated with survival of patients with myelodysplastic syndromes (MDS). We conducted a secondary case-based analysis of 465 de novo MDS patients from the University of Texas MD Anderson Cancer Center (UTMDACC). We investigated the association between demographic as well as occupational exposure markers and survival while incorporating known clinical markers of prognosis. In our patient population, 60.6% were men and the majority were white (93.1%). The distribution of MDS subtypes by the French–American–British (FAB) classification was 81 (19%) refractory anemia (RA), 46 (9.9%) refractory anemia with ringed sideroblasts (RARS), 57 (12.3%) chronic myelomonocytic leukemia (CMML), 173 (37.2%) RA with excess blasts (RAEB), and 86 (18.5%) RAEB in transformation (RAEBT). We found that those older at diagnosis (> 60 years of age) (HR = 1.68, CI = 1.26-2.25) were at a higher risk of dying compared to younger patients. Similarly, high pack years of smoking (>= 30 pack years of smoking) (HR = 1.34, CI = 1.02-1.74), and agricultural chemical exposure (HR = 1.61, CI = 1.05-2.46) were significantly associated with overall lower survival when compared to patients with none or medium exposures. Among clinical markers, greater than 5% bone marrow blasts (HR = 1.81 CI = 1.27-2.56), poor cytogenetics (HR = 3.20, CI = 2.37-4.33)), and platelet cytopenias (<100000/ul) (HR = 1.46, CI = 1.11-1.92) were also significantly associated with overall MDS survival.^ The identification of epidemiological markers could help physicians stratify patients and customize treatment strategies to improve the outcome of MDS based on patient lifestyle information such as smoking exposure and agrochemical exposure. We hope that this study highlights the impact of these exposures in MDS prognosis.^

Genetic variations in the PI3K-AKT-mTOR pathway and bladder cancer susceptibility and clinical outcomes

Bladder cancer is the fourth most common cancer in men in the United States. There is compelling evidence supporting that genetic variations contribute to the risk and outcomes of bladder cancer. The PI3K-AKT-mTOR pathway is a major cellular pathway involved in proliferation, invasion, inflammation, tumorigenesis, and drug response. Somatic aberrations of PI3K-AKT-mTOR pathway are frequent events in several cancers including bladder cancer; however, no studies have investigated the role of germline genetic variations in this pathway in bladder cancer. In this project, we used a large case control study to evaluate the associations of a comprehensive catalogue of SNPs in this pathway with bladder cancer risk and outcomes. Three SNPs in RAPTOR were significantly associated with susceptibility: rs11653499 (OR: 1.79, 95%CI: 1.24–2.60), rs7211818 (OR: 2.13, 95%CI: 1.35–3.36), and rs7212142 (OR: 1.57, 95%CI: 1.19–2.07). Two haplotypes constructed from these 3 SNPs were also associated with bladder cancer risk. In combined analysis, a significant trend was observed for increased risk with an increase in the number of unfavorable genotypes (P for trend<0.001). Classification and regression tree analysis identified potential gene-environment interactions between RPS6KA5 rs11653499 and smoking. In superficial bladder cancer, we found that PTEN rs1234219 and rs11202600, TSC1 rs7040593, RAPTOR rs901065, and PIK3R1 rs251404 were significantly associated with recurrence in patients receiving BCG. In muscle invasive and metastatic bladder cancer, AKT2 rs3730050, PIK3R1 rs10515074, and RAPTOR rs9906827 were associated with survival. Survival tree analysis revealed potential gene-gene interactions: patients carrying the unfavorable genotypes of PTEN rs1234219 and TSC1 rs704059 exhibited a 5.24-fold (95% CI: 2.44–11.24) increased risk of recurrence. In combined analysis, with the increasing number of unfavorable genotypes, there was a significant trend of higher risk of recurrence and death (P for trend<0.001) in Cox proportional hazard regression analysis, and shorter event (recurrence and death) free survival in Kaplan-Meier estimates (P log rank<0.001). This study strongly suggests that genetic variations in PI3K-AKT-mTOR pathway play an important role in bladder cancer development. The identified SNPs, if validated in further studies, may become valuable biomarkers in assessing an individual's cancer risk, predicting prognosis and treatment response, and facilitating physicians to make individualized treatment decisions. ^

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Clinical relevance of the Helicobacter pylori gene for blood-group antigen-binding adhesin

Infection with Helicobacter pylori is associated with different human gastric diseases. Biochemical studies, in vitro adherence assays, and in vivo animal models revealed that epithelial attachment of H. pylori can be mediated by the blood-group antigen-binding adhesin (BabA) targeting human Lewisb surface epitopes. Studies with transgenic mice expressing the Lewisb epitope have shown that such attachment can alter disease outcome. In the current study, the presence of the babA2 gene encoding the adhesin was investigated in clinical isolates from a German population by using PCR and reverse transcription–PCR. A positive genotype was correlated to allelic variations in the genes encoding VacA and CagA and also to the prevalence of duodenal ulcer, distal gastric adenocarcinoma, mucosa-associated lymphoid tissue lymphoma, and antral gastritis. The presence of babA2 was significantly associated with duodenal ulcer (P = 0.0002) and adenocarcinoma (P = 0.033). In contrast, type 1 strains (vacAs1- and cagA-positive) were associated with only duodenal ulcer (P = 0.004) but not adenocarcinoma (P = 0.235). Genotype presence of babA2, vacAs1, and cagA (“triple-positive” strains) showed a highly significant correlation to the prevalence of ulcer (P = 0.000002) and adenocarcinoma (P = 0.014) and discriminated significantly better between disease outcome than did the current type 1 classification. These results indicate that the babA2 gene is of high clinical relevance and would be a useful marker to identify patients who are at higher risk for specific H. pylori-related diseases.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.