Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47, BATF, FASLG, RGS16, SYNPO, SELE, PTPN7, WARS, PRC1, EXO1, RRM2, PBK, RAD54L, KIFC1, SPC25, PKMYT, HISTH1A, TPX2, DLGAP5, TPX2, CH25H, and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies.

High relative amount of nodular calcification in femoral plaques is associated with milder lower extremity arterial disease.

BACKGROUND: Clinical implications of different types of vascular calcification are poorly understood. The two most abundant forms of calcification, nodular and sheet calcification, have not been quantitatively analyzed in relation to the clinical presentation of lower extremity arterial disease (LEAD). METHODS: The study analyzed 51 femoral artery plaques collected during femoral endarterectomy, characterized by the presence of > 90% stenosis. Comprehensive clinical data was obtained from patient records, including magnetic resonance angiography (MRA) images, toe pressure and ankle brachial index measurements and laboratory values. The plaques were longitudinally sectioned, stained with Hematoxylin and Eosin and digitized in a deep learning platform for quantification of the relative area of nodular and sheet calcification to the plaque section area. A deep learning artificial intelligence algorithm was designed and independently validated to reliably quantify nodular calcification and sheet calcification. Vessel measurements and quantity of each calcification category was compared to the risk factors and clinical presentation. RESULTS: On average, > 90% stenosed vessels contained 22.4 ± 12.3% of nodular and 14.5 ± 11.8% of sheet calcification. Nodular calcification area proportion in lesions with > 90% stenosis is associated with reduced risk of critically low toe pressure (< 30 mmHg) (OR = 0.910, 95% CI = 0.835-0.992, p < 0.05), severely lowered ankle brachial index (< 0.4) (OR = 0.912, 95% CI = 0.84-0.986, p < 0.05), and semi-urgent operation (OR = 0.882, 95% CI = 0.797-0.976, p < 0.05). Sheet calcification did not show any significant association. CONCLUSIONS: Large amount of nodular calcification is associated with less severe LEAD. Patients with nodular calcification may have better flow reserves despite local obstruction.

Using 360-degree videos for virtual reality exposure in CBT for panic disorder with agoraphobia: a feasibility study.

BACKGROUND: Cognitive behavioural therapy (CBT) is an effective treatment for panic disorder with agoraphobia (PDA). However, implementation of some of the procedures involved, particularly in vivo exposure, can be time consuming and taxing for routine health care services. CBT with exposure taking place in virtual reality (VR-CBT) is a more time-efficient option and has shown promising results in the treatment of PDA. However, VR-CBT requires expensive equipment and appropriate virtual environments, which historically has been costly and cumbersome to produce. Thus, access to VR-CBT has been sparse in regular care environments. AIMS: The aim of this study was to investigate whether VR-CBT using filmed virtual environments produced with a low-cost 360-degree film camera can be a feasible and acceptable treatment for PDA when implemented in a primary care context. METHOD: This was an open feasibility trial with a within-group design, with assessments conducted at pre-test, post-test, and 6-month follow-up. Participants (n = 12) received a 10-12 week treatment programme of VR-CBT and PDA-related symptoms were assessed by the primary outcome measure The Mobility Inventory for Agoraphobia (MIA) and the Panic-Disorder Severity Scale-Self Rated (PDSS-SR). RESULTS: The results showed that treatment satisfaction was high and participants were significantly improved on PDA-related measures at post-treatment and at 6-month follow-up with large effect sizes (Cohen's d range = 1.46-2.82). All 12 participants completed the treatment. CONCLUSIONS: These findings suggest that VR-CBT with 360-degree video virtual environments delivered to primary care patients with PDA is feasible, acceptable, and potentially efficacious.

Clonal heterogeneity influences drug responsiveness in renal cancer assessed by ex vivo drug testing of multiple patient-derived cancer cells.

Renal cell cancer (RCC) has become a prototype example of the extensive intratumor heterogeneity and clonal evolution of human cancers. However, there is little direct evidence on how the genetic heterogeneity impacts on drug response profiles of the cancer cells. Our goal was to determine how genomic clonal evolution impacts drug responses. Finding from our study could help to define the challenge that clonal evolution poses on cancer therapy. We established multiple patient-derived cells (PDCs) from different tumor regions of four RCC patients, verified their clonal relationship to each other and to the uncultured tumor tissue by genome sequencing. Furthermore, comprehensive drug-sensitivity testing with 460 oncological drugs was performed on all PDC clones. The PDCs retained many cancer-specific copy number alterations and mutations in driver genes such as VHL, PBRM1, PIK3C2A, KMD5C and TSC2 genes. The drug testing highlighted vulnerability in the PDCs toward approved RCC drugs, such as the mTOR-inhibitor temsirolimus, but also novel sensitivities were uncovered. The individual PDC clones from different tumor regions in a patient showed distinct drug-response profiles, suggesting that genomic heterogeneity contributes to the variability in drug responses. Studies of multiple PDCs from a patient with cancer are informative for elucidating cancer heterogeneity and for the determination on how the genomic evolution is manifested in cancer drug responsiveness. This approach could facilitate tailoring of drugs and drug combinations to individual patients.

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.

Spatial aspects of oncogenic signalling determine the response to combination therapy in slice explants from Kras-driven lung tumours.

A key question in precision medicine is how functional heterogeneity in solid tumours informs therapeutic sensitivity. We demonstrate that spatial characteristics of oncogenic signalling and therapy response can be modelled in precision-cut slices from Kras-driven non-small-cell lung cancer with varying histopathologies. Unexpectedly, profiling of in situ tumours demonstrated that signalling stratifies mostly according to histopathology, showing enhanced AKT and SRC activity in adenosquamous carcinoma, and mitogen-activated protein kinase (MAPK) activity in adenocarcinoma. In addition, high intertumour and intratumour variability was detected, particularly of MAPK and mammalian target of rapamycin (mTOR) complex 1 activity. Using short-term treatment of slice explants, we showed that cytotoxic responses to combination MAPK and phosphoinositide 3-kinase-mTOR inhibition correlate with the spatially defined activities of both pathways. Thus, whereas genetic drivers determine histopathology spectra, histopathology-associated and spatially variable signalling activities determine drug sensitivity. Our study is in support of spatial aspects of signalling heterogeneity being considered in clinical diagnostic settings, particularly to guide the selection of drug combinations. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

The prognostic significance of tall cells in papillary thyroid carcinoma: A case-control study.

The subtype of the papillary thyroid carcinoma tall-cell variant has a worse prognosis than does the conventional papillary type (papillary thyroid carcinoma). The new World Health Organization 2017 classification defines a tall-cell variant as a tumour consisting of over 30% of cells that are two or three times as tall as they are wide. However, thresholds have differed. Our aim was to study how tall cells affect the prognosis of papillary thyroid carcinoma patients and to determine, for such cells, a cut-off percentage. Our cohort included 65 papillary thyroid carcinoma patients who underwent surgery at Helsinki University Hospital between 1973 and 1996: originally, 36 otherwise-matched patient pairs, eventually comprising 34 patients with an adverse outcome plus 31 who had recovered. All samples were digitally scanned and scored by two investigators based on tall cell composition. The cohort was analysed with four tall cell thresholds: 10%, 30%, 50% and 70% with a median follow-up of 22 years. In survival analysis, only the 70% threshold showed a correlation with reduced overall survival, disease-specific survival and relapse-free survival. A correlation also emerged with death from papillary thyroid carcinoma. In multivariate analysis, a 70% cut-off and age at diagnosis significantly affected DSS. Increasing tall cell score correlated with increasing age and extrathyroidal extensions. A tall cell composition of 10%, 30% or 50% showed no correlation with adverse outcome and suggests that the choice of pathologists reporting tall-cell variant should be a 70% threshold.

Chronic Activation of Innate Immunity Correlates With Poor Prognosis in Cancer Patients Treated With Oncolytic Adenovirus.

Despite many clinical trials conducted with oncolytic viruses, the exact tumor-level mechanisms affecting therapeutic efficacy have not been established. Currently there are no biomarkers available that would predict the clinical outcome to any oncolytic virus. To assess the baseline immunological phenotype and find potential prognostic biomarkers, we monitored mRNA expression levels in 31 tumor biopsy or fluid samples from 27 patients treated with oncolytic adenovirus. Additionally, protein expression was studied from 19 biopsies using immunohistochemical staining. We found highly significant changes in several signaling pathways and genes associated with immune responses, such as B-cell receptor signaling (P < 0.001), granulocyte macrophage colony-stimulating factor (GM-CSF) signaling (P < 0.001), and leukocyte extravasation signaling (P < 0.001), in patients surviving a shorter time than their controls. In immunohistochemical analysis, markers CD4 and CD163 were significantly elevated (P = 0.020 and P = 0.016 respectively), in patients with shorter than expected survival. Interestingly, T-cell exhaustion marker TIM-3 was also found to be significantly upregulated (P = 0.006) in patients with poor prognosis. Collectively, these data suggest that activation of several functions of the innate immunity before treatment is associated with inferior survival in patients treated with oncolytic adenovirus. Conversely, lack of chronic innate inflammation at baseline may predict improved treatment outcome, as suggested by good overall prognosis.

Loss of PTEN expression in ERG-negative prostate cancer predicts secondary therapies and leads to shorter disease-specific survival time after radical prostatectomy.

The clinical course of prostate cancer is highly variable. Current prognostic variables, stage, and Gleason score have limitations in assessing treatment regimens for individual patients, especially in the intermediate-risk group of Gleason score 7. ERG:TMPRSS2 fusion and loss of PTEN are some of the most common genetic alterations in prostate cancer. Immunohistochemistry of PTEN and ERG has generated interest as a promising method for more precise outcome prediction but requires further validation in population-based cohorts. We studied the predictive value of ERG and PTEN expression by immunohistochemistry in two large radical prostatectomy cohorts comprising 815 patients with extensive follow-up information. Clinical end points were initiation of secondary therapy, overall survival, and disease-specific survival. Predictions of clinical outcomes were also assessed according to androgen receptor (AR) activity. PTEN loss, especially in ERG-negative cancers, predicted initiation of secondary treatments and shortened disease-specific survival time, as well as stratifying Gleason score 7 patients into different prognostic groups with regard to secondary treatments and disease-specific survival. High AR immunoreactivity in ERG-negative cancers with PTEN loss predicted worse disease-specific survival. We also observed that in Gleason score 7 ERG-negative cases with PTEN loss and high AR expression have significantly shorter disease-specific survival time compared with ERG-positive cases. Our conclusion is that loss of PTEN is a strong determining factor for shorter disease-specific survival time and initiation of secondary therapies after radical prostatectomy. The predictive value of PTEN immunoreactivity is further accentuated in ERG-negative cancers with high AR expression. Negative PTEN expression, accompanied by ERG status, can be used to stratify patients with Gleason score 7 into different survival groups. Assessment of PTEN and ERG status could provide an additional tool for initial diagnostics when determining the prognosis and subsequent follow-up regimen for patients treated by radical prostatectomy.

Elevated levels of StAR-related lipid transfer protein 3 alter cholesterol balance and adhesiveness of breast cancer cells: potential mechanisms contributing to progression of HER2-positive breast cancers.

The STARD3 gene belongs to the minimal amplicon in HER2-positive breast cancers and encodes a cholesterol-binding membrane protein. To study how elevated StAR-related lipid transfer protein 3 (StARD3) expression affects breast cancer cells, we generated MCF-7 cells stably overexpressing StARD3-green fluorescent protein. We found that StARD3-overexpressing cells exhibited nonadherent morphological features, had increased Src levels, and had altered cholesterol balance, as evidenced by elevated mRNA levels of the cholesterol biosynthesis rate-limiting enzyme 3-hydroxy-3-methylglutaryl-coenzyme A reductase, and increased plasma membrane cholesterol content. On removal of serum and insulin from the culture medium, the morphological characteristics of the StARD3-overexpressing cells changed, the cells became adherent, and they developed enlarged focal adhesions. Under these conditions, the StARD3-overexpressing cells maintained elevated Src and plasma membrane cholesterol content and showed increased phosphorylation of focal adhesion kinase. In two Finnish nationwide patient cohorts, approximately 10% (212/2220) breast cancers exhibited high StARD3 protein levels, which was strongly associated with HER2 amplification; several factors related to poor disease outcome and poor breast cancer-specific survival. In addition, high StARD3 levels in breast cancers were associated with elevated 3-hydroxy-3-methylglutaryl-coenzyme A reductase mRNA levels and anti-Src-Tyr416 immunoreactivity. These results provide evidence that StARD3 overexpression results in increased cholesterol biosynthesis and Src kinase activity in breast cancer cells and suggest that elevated StARD3 expression may contribute to breast cancer aggressiveness by increasing membrane cholesterol and enhancing oncogenic signaling.

T-cell subsets in peripheral blood and tumors of patients treated with oncolytic adenoviruses.

The quality of the antitumor immune response is decisive when developing new immunotherapies for cancer. Oncolytic adenoviruses cause a potent immunogenic stimulus and arming them with costimulatory molecules reshapes the immune response further. We evaluated peripheral blood T-cell subsets of 50 patients with refractory solid tumors undergoing treatment with oncolytic adenovirus. These data were compared to changes in antiviral and antitumor T cells, treatment efficacy, overall survival, and T-cell subsets in pre- and post-treatment tumor biopsies. Treatment caused a significant (P < 0.0001) shift in T-cell subsets in blood, characterized by a proportional increase of CD8(+) cells, and decrease of CD4(+) cells. Concomitant treatment with cyclophosphamide and temozolomide resulted in less CD4(+) decrease (P = 0.041) than cyclophosphamide only. Interestingly, we saw a correlation between T-cell changes in peripheral blood and the tumor site. This correlation was positive for CD8(+) and inverse for CD4(+) cells. These findings give insight to the interconnections between peripheral blood and tumor-infiltrating lymphocyte (TIL) populations regarding oncolytic virotherapy. In particular, our data suggest that induction of T-cell response is not sufficient for clinical response in the context of immunosuppressive tumors, and that peripheral blood T cells have a complicated and potentially misleading relationship with TILs.

Androgen receptor-interacting protein HSPBAP1 facilitates growth of prostate cancer cells in androgen-deficient conditions.

Hormonal therapies targeting androgen receptor (AR) are effective in prostate cancer (PCa), but often the cancers progress to fatal castrate-resistant disease. Improved understanding of the cellular events during androgen deprivation would help to identify survival and stress pathways whose inhibition could synergize with androgen deprivation. Toward this aim, we performed an RNAi screen on 2,068 genes, including kinases, phosphatases, epigenetic enzymes and other druggable gene targets. High-content cell spot microarray (CSMA) screen was performed in VCaP cells in the presence and absence of androgens with detection of Ki67 and cleaved ADP-ribose polymerase (cPARP) as assays for cell proliferation and apoptosis. Thirty-nine candidate genes were identified, whose silencing inhibited proliferation or induced apoptosis of VCaP cells exclusively under androgen-deprived conditions. One of the candidates, HSPB (heat shock 27 kDa)-associated protein 1 (HSPBAP1), was confirmed to be highly expressed in tumor samples and its mRNA expression levels increased with the Gleason grade. We found that strong HSPBAP1 immunohistochemical staining (IHC) was associated with shorter disease-specific survival of PCa patients compared with negative to moderate staining. Furthermore, we demonstrate that HSPBAP1 interacts with AR in the nucleus of PCa cells specifically during androgen-deprived conditions, occupies chromatin at PSA/klk3 and TMPRSS2/tmprss2 enhancers and regulates their expression. In conclusion, we suggest that HSPBAP1 aids in sustaining cell viability by maintaining AR signaling during androgen-deprived conditions.

Dual role of FoxA1 in androgen receptor binding to chromatin, androgen signalling and prostate cancer.

High androgen receptor (AR) level in primary tumour predicts increased prostate cancer-specific mortality. However, the mechanisms that regulate AR function in prostate cancer are poorly known. We report here a new paradigm for the forkhead protein FoxA1 action in androgen signalling. Besides pioneering the AR pathway, FoxA1 depletion elicited extensive redistribution of AR-binding sites (ARBs) on LNCaP-1F5 cell chromatin that was commensurate with changes in androgen-dependent gene expression signature. We identified three distinct classes of ARBs and androgen-responsive genes: (i) independent of FoxA1, (ii) pioneered by FoxA1 and (iii) masked by FoxA1 and functional upon FoxA1 depletion. FoxA1 depletion also reprogrammed AR binding in VCaP cells, and glucocorticoid receptor binding and glucocorticoid-dependent signalling in LNCaP-1F5 cells. Importantly, FoxA1 protein level in primary prostate tumour had significant association to disease outcome; high FoxA1 level was associated with poor prognosis, whereas low FoxA1 level, even in the presence of high AR expression, predicted good prognosis. The role of FoxA1 in androgen signalling and prostate cancer is distinctly different from that in oestrogen signalling and breast cancer.

Benefit of adjuvant interferon alfa-2b (IFN-α) therapy in melanoma patients with high serum MMP-8 levels.

Matrix metalloproteinases (MMPs) are important enzymes in tissue turnover and various inflammatory processes. In this study, it was evaluated whether serum MMP-8 can predict the response to adjuvant interferon alfa-2b (IFN-α) therapy in patients with operated high-risk cutaneous melanoma. Pre-treatment sera from 460 patients with stage IIB-IIIC melanoma were analyzed for MMP-8. The patients were randomized after surgery to adjuvant IFN-α for 12 or 24 months (n = 313) or observation only (n = 147). The median serum MMP-8 level was used to classify the patients into a low MMP-8 (n = 232) and a high MMP-8 (n = 228) group. In the high MMP-8 subgroup, IFN-α therapy significantly improved relapse-free survival (RFS). RFS was 36.8 months in patients with high MMP-8 levels receiving IFN-α therapy, whereas RFS for those with high MMP-8 levels with observation only was 10.6 months (P = 0.027). Median overall survival for patients with high MMP-8 and observation only was 36.7 versus 71.7 months in those receiving IFN-α (P = 0.13). In a multivariate model, IFN-α therapy was a significant predictor of favorable RFS (HR 0.74; 95 % CI 0.55-0.99; P = 0.048), after adjustment for pre-treatment MMP-8 (HR 1.17; 95 % CI 0.88-1.55; P = 0.28), gender (HR 1.16; 95 % CI 0.86-1.56; P = 0.32), age (HR 1.00; 95 % CI 1.00-1.02; P = 0.12), ulceration (HR 1.09; 95 % CI 0.81-1.46; P = 0.58), and the presence of node metastases (HR 1.36; 95 % CI 1.17-1.58; P < 0.0001). In conclusion, patients with high serum MMP-8 levels may benefit from adjuvant IFN-α therapy, but this observation should be further investigated.

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting.

BACKGROUND: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples. However, the diagnostic accuracy of AI-based analysis of entire microscope slides, so called whole-slide images (WSI), has previously not been evaluated on a sample-level in primary healthcare settings in STH endemic countries. METHODOLOGY/PRINCIPAL FINDINGS: Stool samples (n = 1,335) were collected during 2020 from children attending primary schools in Kwale County, Kenya, prepared according to the Kato-Katz method at a local primary healthcare laboratory and digitized with a portable whole-slide microscopy scanner and uploaded via mobile networks to a cloud environment. The digital samples of adequate quality (n = 1,180) were split into a training (n = 388) and test set (n = 792) and a deep-learning system (DLS) developed for detection of STHs. The DLS findings were compared with expert manual microscopy and additional visual assessment of the digital samples in slides with discordant results between the methods. Manual microscopy detected 15 (1.9%) Ascaris lumbricoides, 172 (21.7%) Tricuris trichiura and 140 (17.7%) hookworm (Ancylostoma duodenale or Necator americanus) infections in the test set. Importantly, more than 90% of all STH positive cases represented light intensity infections. With manual microscopy as the reference standard, the sensitivity of the DLS as the index test for detection of A. lumbricoides, T. trichiura and hookworm was 80%, 92% and 76%, respectively. The corresponding specificity was 98%, 90% and 95%. Notably, in 79 samples (10%) classified as negative by manual microscopy for a specific species, STH eggs were detected by the DLS and confirmed correct by visual inspection of the digital samples. CONCLUSIONS/SIGNIFICANCE: Analysis of digitally scanned stool samples with the DLS provided high diagnostic accuracy for detection of STHs. Importantly, a substantial number of light intensity infections were missed by manual microscopy but detected by the DLS. Thus, analysis of WSIs with image-based AI may provide a future tool for improved detection of STHs in a primary healthcare setting, which in turn could facilitate monitoring and evaluation of control programs.

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery.

Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.

An extensive tumor array analysis supports tumor suppressive role for nucleophosmin in breast cancer.

Nucleophosmin (NPM) is a multifunctional protein involved in a complex network of interactions. The role of NPM in oncogenesis is controversial. The NPM gene (NPM1) is mutated or rearranged in a number of hematological disorders, but such changes have not been detected in solid cancers. However, experiments with cultured NPM-null cells and with mice carrying a single inactivated NPM allele indicate a tumor suppressor function for NPM. To resolve the role of NPM in solid cancers, we examined its expression and localization in histologically normal breast tissue and a large array of human breast carcinoma samples (n = 1160), and also evaluated its association with clinicopathological variables and patient survival. The intensity and localization (nucleolar, nuclear, cytoplasmic) of NPM varied across clinical samples. No mutations explaining the differences were found, but the present findings indicate that expression levels of NPM affected its localization. Our study also revealed a novel granular staining pattern for NPM, which was an independent prognostic factor of poor prognosis. In addition, reduced levels of NPM protein were associated with poor prognosis. Furthermore, luminal epithelial cells of histologically normal breast displayed high levels of NPM and overexpression of NPM in the invasive MDA-MB-231 cells abrogated their growth in soft agar. These results support a tumor suppressive role for NPM in breast cancer.