The most common initial treatment received by patients with a brain tumour is surgical removal of the growth. Precise histopathological diagnosis of brain tumours is to some extent subjective. Furthermore, currently available diagnostic imaging techniques to delineate the excision border during cytoreductive surgery lack the required spatial precision to aid surgeons. We set out to determine whether infrared (IR) and/or Raman spectroscopy combined with multivariate analysis could be applied to discriminate between normal brain tissue and different tumour types (meningioma, glioma and brain metastasis) based on the unique spectral "fingerprints" of their biochemical composition. Formalin-fixed paraffin-embedded tissue blocks of normal brain and different brain tumours were de-waxed, mounted on low-E slides and desiccated before being analyzed using attenuated total reflection Fourier-transform IR (ATR-FTIR) and Raman spectroscopy. ATR-FTIR spectroscopy showed a clear segregation between normal and different tumour subtypes. Discrimination of tumour classes was also apparent with Raman spectroscopy. Further analysis of spectral data revealed changes in brain biochemical structure associated with different tumours. Decreased tentatively-assigned lipid-to-protein ratio was associated with increased tumour progression. Alteration in cholesterol esters-to-phenylalanine ratio was evident in grade IV glioma and metastatic tumours. The current study indicates that IR and/or Raman spectroscopy have the potential to provide a novel diagnostic approach in the accurate diagnosis of brain tumours and have potential for application in intra-operative diagnosis.

This study seeks to examine and analyze the spatial and temporal patterns of 2019 novel coronavirus disease outbreaks and identify the spatiotemporal distribution characteristics and changing trends of cases. Hence, local outlier analysis and emerging spatiotemporal hot spot analysis were performed to analyze the spatiotemporal clustering pattern and cold/hot spot trends of COVID-19 cases based on space-time cube during the period from 23 January 2020 to 24 February 2020. The main findings are as follows: (1) The outbreak had spread rapidly throughout the country within a short time and the current totality incidence rate has decreased. (2) The spatiotemporal distribution of cases was uneven. In terms of the spatiotemporal clustering pattern, Wuhan and Shiyan city were the center as both cities had high-high clustering pattern with a surrounding unstable multiple-type pattern in partial areas of Henan, Anhui, Jiangxi, and Hunan provinces, and Chongqing city. Those regions are continuously in the hot spot on the spatiotemporal tendency. (3) The spatiotemporal analysis technology based on the space-time cube can analyze comprehensively the spatiotemporal pattern of epidemiological data and produce a visual output of the consequences, which can reflect intuitively the distribution and trend of data in space-time. Therefore, the Chinese government should strengthen the prevention and control efforts in a targeted manner to cope with a highly changeable situation. K E Y W O R D S COVID-19, emerging spatiotemporal hot spots analysis, local outlier analysis, space-time cube

Background Female breast cancer (FBC) is a malignancy involving multiple risk factors and has imposed heavy disease burden on women. We aim to analyze the secular trends of mortality rate of FBC according to its major risk factors. Methods Death data of FBC at the global, regional, and national levels were retrieved from the online database of Global Burden of Disease study 2017. Deaths of FBC attributable to alcohol use, high body-mass index (BMI), high fasting plasma glucose (FPG), low physical activity, and tobacco were collected. Estimated average percentage change (EAPC) was used to quantify the temporal trends of age-standardized mortality rate (ASMR) of FBC in 1990–2017. Results Worldwide, the number of deaths from FBC increased from 344.9 thousand in 1990 to 600.7 thousand in 2017. The ASMR of FBC decreased by 0.59% (95% CI, 0.52, 0.66%) per year during the study period. This decrease was largely driven by the reduction in alcohol use- and tobacco-related FBC, of which the ASMR was decreased by 1.73 and 1.77% per year, respectively. In contrast, the ASMR of FBC attributable to high BMI and high FPG was increased by 1.26% (95% CI, 1.22, 1.30%) and 0.26% (95% CI, 0.23, 0.30%) per year between 1990 and 2017, respectively. Conclusions The mortality rate of FBC experienced a reduction over the last three decades, which was partly owing to the effective control for alcohol and tobacco use. However, more potent and tailored prevention strategies for obesity and diabetes are urgently warranted.

Breast cancer (BC) is one of the most prevalent forms of cancer globally. However, the practical relevance of the RNA expression-based prediction of BC is not clearly understood and requires further study. Using gene expression data downloaded from The Cancer Genome Atlas (TCGA), a risk score staging classification was created using Cox's multiple regression and was used to predict the clinical outcomes of patients with BC. In total, 7 genes, including AC123595.1, leukocyte immunoglobulin-like receptor B5, CD209 molecule, AL049749.1, lymphatic vessel endothelial hyaluronan receptor 1, transmembrane protein 190 and tubulin α 3D chain were identified in association with patient survival. The patients with lower risk scores had considerably improved survival rates than those with higher risk scores. Compared with other clinical factors, the risk score more accurately predicted the clinical outcome of patients with BC. In summary, 7 genes were identified using the Cox regression model, and subsequently used to develop a risk staging model for BC, which may be of use for the medical management of patients.

Environmental contaminants accumulate in many organisms and induce a number of adverse effects. As contaminants mostly occur in the environment as mixtures, it remains to be fully understood which chemical interactions induce the most important toxic responses. In this study, we set out to determine the effects of chemical contaminants extracted from Northern Gannet (Morus bassanus) eggs (collected from the UK coast from three sampling years (1987, 1990, and 1992) on cell cultures using infrared (IR) spectroscopy with computational data handling approaches. Gannet extracts were chemically analyzed for different contaminants, and MCF-7 cell lines were treated for 24 h in a dose-related manner with individual-year extracts varying in their polybrominated diphenyl ether (PBDE) to polychlorinated biphenyl (PCB) ratios. Treated cellular material was then fixed and interrogated using attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy; resultant IR spectra were computationally analyzed to derive dose-response relationships and to identify biomarkers associated with each contaminant mixture treatment. The results show distinct biomarkers of effect are related to each contamination scenario, with an inverse relationship with dose observed. This study suggests that specific contaminant mixtures induce cellular alterations in the DNA/RNA spectral region that are most pronounced at low doses. It also suggests alterations in the "biochemical-cell fingerprint" of IR spectra can be indicative of mixture exposures.