Dogs can be affected by a tumour arising from the glands of the anal sacs. These tumours can grow aggressively and spread to other organs, resulting in disruption of bowel movements and/or organ function, ultimately leading to death or euthanasia. The reason why some tumours grow or spread more quickly is not well understood, and veterinarians are not able to distinguish between dogs likely to have better or worse survival times at the time of initial diagnosis. This study evaluates clinical, imaging, and tumour features of dogs with anal sac tumours and examines which features were linked to improved survival times. Smaller tumour size, smaller lymph node size, surgical removal, and treatment with radiotherapy were associated with better survival times. When examining the tumour tissue, the presence of closely packed tumour cells, dead tumour tissue, and invasion of vessels by tumour cells were each associated with worse survival times. Features that impacted survival were used to create two checklists that, when applied to an individual case, may indicate prognosis and therefore help clinicians and owners make more informed clinical decisions.Canine apocrine gland anal sac adenocarcinoma (AGASAC) is a malignant tumour with variable clinical progression. The objective of this study was to use robust multivariate models, based on models employed in human medical oncology, to establish clinical and histopathological risk factors of poor survival. Clinical data and imaging of 81 cases with AGASAC were reviewed. Tissue was available for histological review and immunohistochemistry in 49 cases. Tumour and lymph node size were determined using the response evaluation criteria in the solid tumours system (RECIST). Modelling revealed tumour size over 2 cm, lymph node size grouped in three tiers by the two thresholds 1.6 cm and 5 cm, surgical management, and radiotherapy were independent clinical variables associated with survival, irrespective of tumour stage. Tumour size over 1.3 cm and presence of distant metastasis were independent clinical variables associated with the first progression-free interval. The presence of the histopathological variables of tumour necrosis, a solid histological pattern, and vascular invasion in the primary tumour were independent risk factors of poor survival. Based upon these independent risk factors, scoring algorithms to predict survival in AGASAC patients are presented. Canine apocrine gland adenocarcinoma of the anal sac (AGASAC) is a malignant epithelial tumour arising from the glands of the anal sac ,2. A genSeveral clinical and histological factors have been suggested as indicators of poor survival or reduced progression-free interval (PFI). Primary tumour size has the greatest volume of evidence, with larger tumours associated with shorter survival times ,7,15. LyIn this study, multivariate models optimised for stability were used to establish clinical and histopathological risk factors of decreased survival and PFI in AGASAC patients. Our aim was to provide clinicians treating dogs with AGASAC with replicable algorithms using readily available clinical and pathological data to generate estimated survival times and relative prognosis for the purpose of informing treatment decisions. Guiding that aim were two hypotheses. Firstly, as our case population employed predominant use of CT imaging for patient staging that allowed precise surgical planning for primary and metastatic disease, we hypothesised that surgery would benefit AGASAC patients irrespective of clinically defined disease stage. Secondly, we also hypothesised that histological features associated with decreased survival have additive risk and that the presence of more than one such feature has greater predictive power for decreased survival than a single variable alone.Medical records from a single veterinary referral hospital, the Royal Veterinary College, were searched for dogs diagnosed with AGASAC from 2006 to 2015. Cases were included if they were diagnosed by cytology or histopathology, underwent clinical staging with diagnostic imaging, and had follow-up with at least one hospital visit or documented communication with the referring veterinary practice.https://doi.org/10.5061/dryad.x95x69pgx).Relevant clinical information was retrieved from the Queen Mother Hospital database at the Royal Veterinary College, including patient breed, age, sex, primary clinical sign, location of mass, presence of palpable sublumbar lymphadenopathy, treatment, and complications. Diagnostic imaging data for each case were reviewed by a board-certified radiologist, RD. Diagnostic imaging included thoracoabdominal computed tomography (CT), thoracic radiographs, and abdominal ultrasound, or combination thereof, as determined by clinician discretion and owner consent. Tumour and lymph node measurements were performed using the response evaluation criteria in solid tumours (RECIST) system and were reviewed by a board-certified radiologist, RD . Distant2 consecutive fields in the area of highest mitotic activity, was recorded. Histological pattern was defined as the predominant pattern and was categorised as rosette, tubular, or solid. The presence of histological necrosis was defined as any amount of intra-tumoural necrosis. Biopsy specimens had secondary blinded histological review by board-certified pathologists where tissue was available. HW and SP reviewed the haematoxylin and eosin-stained slides, with the scoring performed by consensus. RR, blinded to the clinical and histological data, scored the immunohistochemistry. Histological parameters of pattern type, presence of necrosis, and presence of vascular invasion within the primary tumour or histological evidence of metastasis in any tissue were recorded. The mitotic count of the primary tumour, across 2.37 mm2, the number of positively immunolabelled Ki67 cells were counted from at least 1000 cells for each sample. Ki67 expression in each sample was defined as the percentage of positive cells relative to the total number of cells counted. Ki67 immunohistochemical labelling was performed on fresh-cut tissue sections using a 1:150 concentration of monoclonal anti-Ki67 antibody  on a BondMax Autostainer Leica, Milton Keynes). Antigen retrieval was at Ph 9  and 90 °C for 10 min. The Bond Polymer Redefine Detection kit (Leica) was used for antibody visualisation. A positive reaction was indicated by the presence of specific brown intranuclear immunoreactivity. The positive control was a canine cutaneous mast cell tumour, and this tissue without the Ki67 antibody was used as a negative control. At ×400 magnification, over an area of 2.37 mmp values were the results of 1000 bootstrap samples. Significant variables were compiled into algorithms. Survival times were calculated using the Kaplan–Meier product-limit method and assessed by Log-rank tests. Hazard ratios were calculated by Cox regression analysis. Statistically significant differences were defined as p < 0.05 or using the Bonferroni correction for multiple comparisons. Statistical calculations were performed using SPSS Statistics  and GraphPad Prism .The zero-time point was the date of initial diagnosis confirmed by cytology or histology. Overall survival was defined as the time to death by any cause relatable to AGASAC disease. Cases were censored if the dog was alive at the date of data analysis, had been lost to follow-up, or died due to a defined condition unrelatable to the AGASAC disease. Progression-free interval was defined as the first period from diagnosis until confirmed disease progression on imaging or clinical examination at the referral centre. Cases without documented disease progression were censored at the most recent examination at the referral centre. Cases that did not revisit the referral centre to evaluate progressive disease were not included in the PFI data set. Model variables were identified from review of the literature, and the evidence of effect was considered, with higher-ranking variables included in the global model . ProportEighty-one cases met the inclusion criteria, and the population signalment is detailed in p = 0.006 **). Distant metastases were confirmed either through histology or cytology in the liver (n = 5), spleen (n = 3), pancreas (n = 1), adrenal glands (n = 2), and urinary bladder (n = 1). Lung metastasis (n = 15) was presumed based on CT imaging, except for 1 case in which necropsy was performed. Other presumed metastatic locations included adrenal glands (n = 4), bone (n = 3), vertebral canal (n = 1), intramuscular (n = 3), rectal wall (n = 1), and retroperitoneal (n = 1).Thoracoabdominal CT was performed in 61 cases . Thoracic radiographs combined with either abdominal ultrasound or abdominal CT was performed in 18 cases . Two cases had abdominal ultrasound only . Primary disease alone was present in 19 cases , 37 cases  had nodal metastasis, and 25 cases  had distant metastasis. Sixty-five cases had lymphadenopathy (65/81), most commonly of the medial iliac node (n = 49), followed by the hypogastric node (n = 30) and sacral/sublumbar nodes (n = 27). Metastatic disease was ruled out as a cause of the lymphadenopathy in three cases through cytological examination, and in five cases with putative metastatic lesions, owners declined any further investigation. Forty-six cases  with putative metastasis that underwent further investigation had metastasis confirmed by cytological or histological examination. Lymph node size was weakly correlated with the presence of hypercalcaemia , and the median time to censor was 346 days (range 9–2442 days). Of the 37 dogs with PFI data, 27 dogs had disease progression, and 10 were in remission. The median PFI was 243 days (range 57–686 days), and the median time to censor was 206 days. The median longest tumour diameter was 2.5 cm (range 0.2–10 cm). The median longest lymph node diameter was 2.2 cm (range 0.1–9.6 cm). Specifically, for the twelve dogs presenting with dysuria or tenesmus, all dogs had either a primary tumour >2 cm  or a lymph node >5 cm . According to the previously suggested clinical grading scheme for AGASAC [Sixty-three dogs underwent surgical management , with 37 receiving surgery alone , 19 receiving surgery and medical therapy , one dog receiving surgery and radiotherapy , and six dogs receiving surgery, medical therapy, and radiotherapy . Seven dogs received medical therapy without surgery , and this included one dog that received radiotherapy alongside medical therapy. Eleven dogs  received no further treatment following diagnosis. Surgical approaches consisted of anal sacculectomy, lymphadenectomy, and/or distant metastasectomy. Surgical complications were reported in 14 dogs  hazard ratio of 3.739 , and vascular invasion were significant independent risk factors of poor survival  Table S4The parameters of tumour necrosis, histological pattern, and vascular invasion were combined to produce a histopathological algorithm, generating estimated survival times for dogs with AGASAC .p < 0.0001 Log-rank; p = 0.001 Cox regression; Cases that scored 0−1  had an MST of 906 days, significantly longer than the MST of 318 days for cases that had a score of 2 or more   [This paper is the first to use a multivariate model stratified by clinical stage to investigate the effect of clinical parameters on survival. It has been postulated that some effect of tumour size and lymph node size upon survival is often, in part, related to a potential association between larger tumour or lymph node size and increased stage of disease ,17. Howe 3.6 cm) . In thisSuccessful management of the primary tumour and metastatic lesions, primarily through surgery, can lead to improved survival ,12,20,21The prognostic relevance of tumour and lymph node size is recognised in the current clinical grading system , but sugIn our study population, the incidence of hypercalcaemia was 23%. This study identified a positive association between hypercalcaemia and lymph node size, suggesting that the presence of hypercalcaemia may be related to metastatic volume. Differential expression of parathyroid-hormone-related polypeptide between primary and metastatic lesions has been documented in human mammary carcinoma ,42 and mOur study identified tumour size and the presence of distant metastasis as independent significant risk factors in the first PFI. Tumours over 1.3 cm had an almost six-fold increase in the relative risk of a decreased PFI, compared to tumours equal to or less than 1.3 cm. These data are supportive of previously published results in which tumour size influenced the first PFI . The effWith regard to histopathological features, we identified necrosis, a solid histological pattern, and vascular invasion as independent risk factors of poor survival in multivariate analysis. A solid histological pattern has previously been found to correlate with poor survival on univariate analysis. ,16,30 OuA limitation of our study is that the retrospective nature resulted in varied patient management, an output of diverse clinical, financial, and ethical considerations. The statistical approach was designed to accommodate this variability, but a subsequent multi-centre study using standardised protocols would be useful to further investigate the findings presented here.In this paper, we retrospectively reviewed clinical and histological data from a population of 81 dogs with AGASAC. Tumour and lymph node size, surgical management, and/or radiotherapy were identified as independent clinical variables associated with survival. Tumour size and presence of distant metastasis were identified as independent clinical variables associated with the first progression-free interval. From the histological data, the presence of necrosis, solid histological pattern, and vascular invasion were independent histological variables associated with decreased survival. The independent variables associated with prognosis were incorporated into prognostic algorithms either for use at the clinical staging of AGASAC patients or at histological diagnosis to predict survival. The use of these algorithms, with case-specific adaptations, will aid clinicians and owners, at the time of clinical staging or histological diagnosis, in therapeutic decision making and to project individual outcomes.


Pseudomonas aeruginosa has become one of the most common pathogens associated with nosocomial infections. A series of susceptibility studies was designed to evaluate the activity of tebipenem against a panel of P. aeruginosa isolates known to have alterations in efflux pumps, outer-membrane porins, and expression of AmpC beta-lactamase enzymes compared to a subset of wild-type organisms.SPR994 is an orally bioavailable pivoxil hydrobromide prodrug of tebipenem, a novel oral carbapenem with activity against Gram-positive and -negative bacteria currently under development for the treatment of urinary tract infections (UTI). P. aeruginosa isolates was subjected to tebipenem and five other challenge compounds  in order to determine minimum inhibitory concentration (MIC) values, in triplicate, using standard agar dilution methodologies. P. aeruginosa ATCC 27853 was used as an internal control and all reported MIC values represented the modal value.A panel of 20 Table 1) and were similar to those found for meropenem and ertapenem with MIC values ranging from ≤0.03 to 16 mg/L and ≤0.25 to 128 mg/L, respectively. The deletion of MexB decreased the carbapenem MIC values by 2 to 4x while deletion of MexCD-OprJ and MexXy did not. Simultaneous deficiencies in MexAB, CD, EF, and XY did not decrease MIC values in comparison with the deletion of MexAB alone. Isolates possessing a derepressed AmpC showed no significant elevation in MIC value compared to wild-type isolates while those known to have lost OprD resulted in carbapenem MIC values increasing by ~4 to 32x. The combined loss of OprD and overexpression of AmpC resulted in the highest carbapenem MIC values (≥16 mg/L). Levofloxacin, tetracycline, and carbenicillin MIC values ranged from ≤0.06 to 16 mg/L, 0.5 to 64 mg/L, and ≤0.125 to >128 mg/L, respectively. All MIC values determined for the internal control were within Clinical Laboratory Standard Institute reported values.Tebipenem MIC values ranged from 0.25 to >64 mg/L for the 20 isolates evaluated Achogen (Grant/Research Support)Amplyx Pharmaceuticals, Inc. (Grant/Research Support)Arixa Pharmaceuticals (Grant/Research Support)Arsanis Inc. (Grant/Research Support)B. Braun Medical Inc. (Grant/Research Support)Basilea Pharmaceutica (Grant/Research Support)BLC USA (Grant/Research Support)Boston Pharmaceuticals (Grant/Research Support)Bravos Biosciences, LLC (Grant/Research Support)Cidara Therapeutics Inc. (Grant/Research Support)Cipla, USA (Grant/Research Support)Corcept Therapeutics (Grant/Research Support)Cumberland Pharmaceuticals (Grant/Research Support)Debiopharm International SA (Grant/Research Support)Discuva Limited (Grant/Research Support)Emerald Lake Technologies (Grant/Research Support)Enhanced Pharmacodynamics (Grant/Research Support)Entasis Therapeutics (Grant/Research Support)E-Scape Bio (Grant/Research Support)Genentech (Grant/Research Support)Geom Therapeutics, Inc. (Grant/Research Support)GlaxoSmithKline (Grant/Research Support)Hoffmann-La Roche (Grant/Research Support)Horizon Orphan LLC (Grant/Research Support)ICPD Biosciences, LLC (Grant/Research Support)Indalo Therapeutics (Grant/Research Support)Insmed Inc. (Grant/Research Support)Institute for Clinical Pharmacodynamics (Employee)Iterum (Grant/Research Support)KBP Biosciences USA (Grant/Research Support)Kyoto Biopharma, Inc. (Grant/Research Support)Matinas (Grant/Research Support)Meiji Seika Pharma Co., Ltd. (Grant/Research Support)Melinta Therapeutics (Grant/Research Support)Menarini Ricerche S.p.A. (Grant/Research Support)Merck & Co., Inc (Grant/Research Support)Mutabilis (Grant/Research Support)Nabriva Therapeutics AG (Grant/Research Support)Naeja-RGM Pharmaceuticals (Grant/Research Support)Nosopharm SAS (Grant/Research Support)Novartis Pharmaceuticals Corp. (Grant/Research Support)NuCana Biomed (Grant/Research Support)Paratek Pharmaceuticals, Inc. (Grant/Research Support)Polyphor, Ltd. (Grant/Research Support)Prothena Corporation (Grant/Research Support)PTC Therapeutics (Grant/Research Support)Rempex Pharmaceuticals (Grant/Research Support)Roche TCRC (Grant/Research Support)Sagimet (Grant/Research Support)scPharmaceuticals Inc. (Grant/Research Support)Scynexis (Grant/Research Support)Spero Therapeutics (Grant/Research Support)TauRx Therapeutics (Grant/Research Support)Tetraphase Pharmaceuticals (Grant/Research Support)Theravance Biopharma Pharmaceutica (Grant/Research Support)USCAST (Grant/Research Support)VenatoRx (Grant/Research Support)Vical Incorporated (Grant/Research Support)Wockhardt Bio AG (Grant/Research Support)Zavante Therapeutics (Grant/Research Support)Zogenix International (Grant/Research Support) Haley Conde, B.S., 3-V Biosciences (Grant/Research Support)Achogen (Grant/Research Support)Amplyx Pharmaceuticals, Inc. (Grant/Research Support)Arixa Pharmaceuticals (Grant/Research Support)Arsanis Inc. (Grant/Research Support)B. Braun Medical Inc. (Grant/Research Support)Basilea Pharmaceutica (Grant/Research Support)BLC USA (Grant/Research Support)Boston Pharmaceuticals (Grant/Research Support)Bravos Biosciences, LLC (Grant/Research Support)Cidara Therapeutics Inc. (Grant/Research Support)Cipla, USA (Grant/Research Support)Corcept Therapeutics (Grant/Research Support)Cumberland Pharmaceuticals (Grant/Research Support)Debiopharm International SA (Grant/Research Support)Discuva Limited (Grant/Research Support)Emerald Lake Technologies (Grant/Research Support)Enhanced Pharmacodynamics (Grant/Research Support)Entasis Therapeutics (Grant/Research Support)E-Scape Bio (Grant/Research Support)Genentech (Grant/Research Support)Geom Therapeutics, Inc. (Grant/Research Support)GlaxoSmithKline (Grant/Research Support)Hoffmann-La Roche (Grant/Research Support)Horizon Orphan LLC (Grant/Research Support)ICPD Biosciences, LLC (Grant/Research Support)Indalo Therapeutics (Grant/Research Support)Insmed Inc. (Grant/Research Support)Institute for Clinical Pharmacodynamics (Employee)Iterum (Grant/Research Support)KBP Biosciences USA (Grant/Research Support)Kyoto Biopharma, Inc. (Grant/Research Support)Matinas (Grant/Research Support)Meiji Seika Pharma Co., Ltd. (Grant/Research Support)Melinta Therapeutics (Grant/Research Support)Menarini Ricerche S.p.A. (Grant/Research Support)Merck & Co., Inc (Grant/Research Support)Mutabilis (Grant/Research Support)Nabriva Therapeutics AG (Grant/Research Support)Naeja-RGM Pharmaceuticals (Grant/Research Support)Nosopharm SAS (Grant/Research Support)Novartis Pharmaceuticals Corp. (Grant/Research Support)NuCana Biomed (Grant/Research Support)Paratek Pharmaceuticals, Inc. (Grant/Research Support)Polyphor, Ltd. (Grant/Research Support)Prothena Corporation (Grant/Research Support)PTC Therapeutics (Grant/Research Support)Rempex Pharmaceuticals (Grant/Research Support)Roche TCRC (Grant/Research Support)Sagimet (Grant/Research Support)scPharmaceuticals Inc. (Grant/Research Support)Scynexis (Grant/Research Support)Spero Therapeutics (Grant/Research Support)TauRx Therapeutics (Grant/Research Support)Tetraphase Pharmaceuticals (Grant/Research Support)Theravance Biopharma Pharmaceutica (Grant/Research Support)USCAST (Grant/Research Support)VenatoRx (Grant/Research Support)Vical Incorporated (Grant/Research Support)Wockhardt Bio AG (Grant/Research Support)Zavante Therapeutics (Grant/Research Support)Zogenix International (Grant/Research Support) Nicole Cotroneo, Spero Therapeutics  Ian A. Critchley, Ph.D., Spero Therapeutics  Thomas R. Parr, Ph.D., Spero Therapeutics  Paul G. Ambrose, Pharm.D., FIDSA, 3-V Biosciences (Grant/Research Support)Achogen (Grant/Research Support)Amplyx Pharmaceuticals, Inc. (Grant/Research Support)Arixa Pharmaceuticals (Grant/Research Support)Arsanis Inc. (Grant/Research Support)B. Braun Medical Inc. (Grant/Research Support)Basilea Pharmaceutica (Grant/Research Support)BLC USA (Grant/Research Support)Boston Pharmaceuticals (Grant/Research Support)Bravos Biosciences, LLC Cidara Therapeutics Inc. (Grant/Research Support)Cipla, USA (Grant/Research Support)Corcept Therapeutics (Grant/Research Support)Cumberland Pharmaceuticals (Grant/Research Support)Debiopharm International SA (Grant/Research Support)Discuva Limited (Research Grant or Support)Emerald Lake Technologies (Grant/Research Support)Enhanced Pharmacodynamics (Grant/Research Support)Entasis Therapeutics (Grant/Research Support)E-Scape Bio (Grant/Research Support)Genentech (Grant/Research Support)Geom Therapeutics, Inc. (Grant/Research Support)GlaxoSmithKline (Grant/Research Support)Hoffmann-La Roche (Grant/Research Support)Horizon Orphan LLC (Grant/Research Support)ICPD Biosciences, LLC Indalo Therapeutics (Grant/Research Support)Insmed Inc. (Grant/Research Support)Institute for Clinical Pharmacodynamics (Employee)Iterum (Grant/Research Support)KBP Biosciences USA (Grant/Research Support)Kyoto Biopharma, Inc. (Grant/Research Support)Matinas (Grant/Research Support)Meiji Seika Pharma Co., Ltd. (Grant/Research Support)Melinta Therapeutics (Grant/Research Support)Menarini Ricerche S.p.A. (Grant/Research Support)Merck & Co., Inc (Grant/Research Support)Mutabilis (Grant/Research Support)Nabriva Therapeutics AG (Grant/Research Support)Naeja-RGM Pharmaceuticals (Grant/Research Support)Nosopharm SAS (Grant/Research Support)Novartis Pharmaceuticals Corp. (Grant/Research Support)NuCana Biomed (Grant/Research Support)Paratek Pharmaceuticals, Inc. (Grant/Research Support)Polyphor, Ltd. (Grant/Research Support)Prothena Corporation (Grant/Research Support)PTC Therapeutics (Grant/Research Support)Rempex Pharmaceuticals (Grant/Research Support)Roche TCRC (Grant/Research Support)Sagimet (Grant/Research Support)scPharmaceuticals Inc. (Grant/Research Support)Scynexis (Grant/Research Support)Spero Therapeutics (Grant/Research Support)TauRx Therapeutics (Grant/Research Support)Tetraphase Pharmaceuticals (Grant/Research Support)Theravance Biopharma Pharmaceutica (Grant/Research Support)USCAST (Grant/Research Support)VenatoRx (Grant/Research Support)Vical Incorporated (Grant/Research Support)Wockhardt Bio AG (Grant/Research Support)Zavante Therapeutics (Grant/Research Support)Zogenix International (Grant/Research Support)


Furthermore, a new statistical model called the exponential T-X exponentiated exponential is introduced. The proposed model is very interesting and possesses heavy-tailed characteristics which are useful in finance and other related sectors. Finally, the applicability of the new model is illustrated by considering the sales data.Marketing refers to the strategies a company undertakes to promote its brands to its potential audience. Advertising provides useful venues for marketing to promote a company's survives/goods to the audience. It has a positive impact on the sale of services or products. In this study, we consider a well-known online medium called Twitter  to check its impact on sales. For this purpose, the simple linear regression modeling approach is implemented to test the significance and usefulness of Twitter advertising on sale. Statistical tests such as  Advertisement is a way of marketing to communicate with the potential consumers of certain goods, products, or services. Advertisements are the information usually paid by a company to influence consumer's decision to buy their products. Numerous mediums are available for advertisements and marketing. The available mediums can be divided into two main groups, such as printed mediums and online mediums. Online media is the most effective medium to reach more consumers , 2.Online advertising is a way of marketing to convey messages to potential users via using the Internet. It is also called online marketing and helps in finding the potential and right audience. Online advertisements appear in a search engine, on social media, in a browser, or even in e-mail. Online advertising can be classified into different groups such as display ads, e-mail ads, native ads, video ads, and social media ads , 4.Twitter is one of the most prominent online/social media platforms that can be used quite effectively for advertising a particular brand. It is a fruitful tool for marketing the business and engaging the audience. It permits its users to create accounts and interact with others through short messages called “tweets.” Twitter is one of the marketer's favorite social media platforms. For instance, 53% of marketers worldwide used it as a channel for their marketing activities in 2020. Social media generally provides brands with personally consumer-related opportunities. Twitter has a huge impact on consumer behavior. According to a report by the Digital Marketing Institute, it turns out that about 40% of Twitter users bought something after seeing it on Twitter. This reflects the steadily increasing impact of social media on consumers' buying habits .Not only is it enough to get the company brand involved on Twitter but also brands need to consider the facilities of social media influencers. The influence of social media like Twitter is also competing with friends in building consumer trust. When trying to find a product recommendation, 56% of consumers asked their friends for instructions, and 49% of respondents said that they bought something after consulting their friends; see . It is rH0 and alternative hypothesis (AH) denoted by HA will be formulated as follows: H0 = Twitter advertising has no significant impact on sales versus HA = Twitter advertising has a significant impact on sales.In this paper, we analyze a real-life data set related to Twitter advertising to see its impact on sales. We will use a well-known statistical methodology called the regression technique to see how much the sales can be increased by spending money on Twitter advertising. To carry out the regression analysis, we will test the hypothesis of “the impact of Twitter advertising on sales.” In this regard, the null hypothesis (NH) expressed by Furthermore, to provide an adequate fit to the sales data, a new heavy-tailed (HT) statistical model is introduced. The new model is introduced by combining the exponential distribution with the exponential TX strategy. The new model is entitled the exponential T-X exponentiated exponential  model. The comparison of the ETXE-exponential distribution is done with the other models. The numerical results show that the ETXE-exponential is a suitable model for dealing with data in business, finance, and management sciences.The regression method helps to carry out the regression analysis to forecast the demand for a particular product. It determines the nature of the relationship between the product's demand and its corresponding determinants. The regression analysis can be carried out via three main approaches, depending upon the nature of the study. Y based on the independent variable(s) usually represented by X. The simple linear regression can be expressed mathematically as follows:θ0 is representing the intercept of the model and θ1 is called the slope of the model. A value 0 of θ1 indicates no effect of X on Y, and in such situation, the model is given by Y=θ0. The quantity ε is the residual error term having a mean value of 0.In this paper, we adopt the simple linear regression (SLReg) approach as we have one independent variable (Twitter advertisement). This approach helps to predict the response variable, usually represented by Y and Twitter advertising X, the simple linear regression becomesIn case of explaining the relationship between sales θ0=5.621 representing the predicted sales in thousands of USD (United States dollars), when there is no investment on the Twitter medium. Therefore, under the circumstances of no investment on Twitter medium, the expected sale would be 5.621∗1000=5621 dollars.After carrying out the analysis using the regression technique, we found that θ1, provided in equation (∗100) units increment in the sales. Henceforth, using the Twitter medium as a marketing tool, the predicted/estimated sale is 5.621+0.193∗1000=198.621, representing a sale of 198621 dollars. Corresponding to Twitter and sales data, the fitted regression model isThe slope  of the regression model equation , is 0.19The visual display of the relationship between Twitter advertising and the sale of goods is provided in θ1 of the regression line is 5.621, not 0, showing that there is a relationship between sales and Twitter advertisement in the sample of 130 observations. However, we want to know if there is a relationship between the observations of all the sales and Twitter advertisement; that is, we want to know that if θ1 is unlikely to be 0.Is there a PR between sales and Twitter advertising medium? Certainly, there is a relationship between them, since the estimated slope θ1. To carry out the procedure, first, we need to accomplish and specify H0 and HA as follows: H0 : θ1=0  versus HA : θ1 ≠ 0 . Second, we obtain the value of t-statistics using the following formula:For these purposes, we adopt a standard hypothesis approach to conduct a hypothesis test for t-statistics using statistical software. By default, the t-statistics is calculated by assuming that θ1=0. Divide the estimated coefficient t-statistics as follows:Third, we compute the value of p value. By default, the p value is calculated by assuming HA is a “not-equal-to.” If the p value is less than 0.05, then we reject HA. After performing the analysis, we observe that the p value is 2e − 16.Fourth, we calculate the p value is very small (less than 0.001), we reject the hypothesis of θ1=0 and accept the hypothesis of θ1 ≠ 0. Henceforth, there is significant evidence that there is a linear relationship between sales and Twitter advertisement.Since the Y. Mathematically, it is given byThe regression line is used to quantify a linear trend in the data. The residual represents the distance between the true value and observed values of Y) varies from the true regression line. In the context of this study, a visual display of the behavior of the residuals is provided in The residual standard error is used to quantify the quality of the regression fit. In this study, it represents the average amount of the sales variable  plot makes an angle of 45 showing that the residuals are approximately normally distributed.Homoscedasticity is one of the basic assumptions of linear regression modeling. If this assumption of homoscedasticity does not hold, then the problem of heteroscedasticity arises. The scale-location plot shows that the residuals satisfy the homoscedasticity property.In regression modeling, the influential observations have a serious effect on the parameters estimates. The residual versus leverage plot shows that there are fewer influential observations in the data.From the plots presented in r and derived asμTwitter and μSales represent the mean values of Twitter and sales, respectively. The significance of r can be tested by using the t-test and is given byA correlation test is a statistical tool used to quantify the relationship between two or more variables. In this study, we have only two variables . Henceforth, we adopt the Pearson correlation approach, which is used to measures a linear relationship between two variables. The coefficient of the Pearson correlation is usually denoted by r, we use the hypothesis procedure as H0=0  versus HA ≠ 0. After performing the required steps of analysis, we observe that r=0.5253, showing a positive relationship between sales and Twitter advertising. Furthermore, we observe that the Spearman rank correlation test represented by Sp, is 161 with p value given by 4.634e − 12. As we see that p-value < 0.05, we reject H0 and conclude that there is a significant relationship between Twitter advertising and sale. This relationship has also been shown graphically in To the test H0= the sale data is not normally distributed versus HA= the sale data is normally distributed. After applying the SW test, we found that (i) for the Twitter advertising data, SW=0.96011 with p value =0.000739 and (ii) for the sale data, SW=0.967900 with p value =0.00356.As we stated above that in the domain of regression modeling, the assessment of the normality of data is one of the basic requirements. Basically, there are two approaches for testing the normality of the data: (i) numerically and (ii) graphically. For numerical analysis, we use the Shapiro–Wilk (SW) test for checking the normality of the sales and Twitter advertising data. This test is carried out in the following manner, p values associated with the Twitter advertising data and sales data are less than 0.05, we reject H0 and accept the assumption of normality.Since the In addition to the numerical test (SW test), we also use a visual approach based on the QQ plot; see This section consists of four sections: (i) in the very first section, we describe how the new model is obtained, (ii) in the second section, the proposed model is introduced and its different PDF (probability density function) plots are sketched, (iii) in the third section, we discuss the regularly varying tail behavior of the proposed model, and (iv) finally, a real application is discussed in the last section.H isδ1 is a shape and δ2 is the scale parameter of the EE distribution. The PDF (probability density function) of the EE model represented by h isBefore investing in a particular domain, it is always of interest to have the best description of that domain. The statistical distributions play a great role in describing the phenomena under consideration. Among the statistical distributions in the available literature, the EE  model applied as an alternative to the two-parameter Weibull and Gamma distributions, has attracted many researchers. The DF (distribution function) of the EE model expressed by a > 0.As we know in the finance sector, the data sets, particularly the financial returns, are frequently right-skewed with heavy tails. Among the class of statistical distributions, the HT distributions have shown better performance in modeling data in the finance sector where the financial returns experience HT, right-skewed, and unimodal behavior. According to Beirlant et al. , a modelAn important characteristic of the HT distributions is the RVP ; see Resnick . A modelσ > 1 and y ∈ ℝ, the DF G, and PDF g of the exponential TX family are given by The distributions which possess the RVP are very useful models for dealing with the financial returns and HT data; see , 14], , , and 1616.NumeroKeeping in view the role of the HT distributions in the finance sectors, we are motivated to study a new HT model called ETXE-exponential distribution. The ETXE-exponential distribution is obtained by incorporating equation  in equatThe ETXE-exponential distribution is very interesting and offers to model the data that are skewed to right with a heavy tail. This fact is shown in the coming three sections. For example, the graphical behavior of its PDF is shown in Y has the ETXE-exponential model with scale parameters δ2 > 0, σ > 1 and shape parameter δ1 > 0, if its DF isA random variable g are sketched in g in δ1=1.2, δ2=0.8, σ=1.8 (red line); δ1=2.8, δ2=0.8, σ=1.8 (green line); and δ1=4.8, δ2=0.8, σ=1.8 (black line), whereas the plots of g in δ1=1.2, δ2=0.8, σ=1.2 (red line); δ1=1.2, δ2=0.8, σ=1.8 (green line); and δ1=1.2, δ2=0.8, σ=2.5 (black line).Some possible behaviors of δ1 increases, the ETXE-exponential distribution tends to a HT distribution. Also, from the PDF plots sketched in σ increases the proposed EXTE-exponential distribution possesses HT behavior.From the PDF plots sketched in This section deals with the regularly varying tail behavior of the ETXE-exponential distribution which is very important to characterize the HT property of a model.According to Karamata's theorem , in termIf k > 0. Using equation ; then, we haveLet us assume that the distribution of L is slowly varying. Note thatUtilizing Karamata's theorem , we can L=σ/σ − 1+yβ−. If L is a SVF (slowly varying function), then the variational result obtained is true. As per Resnick [β > 0, we have to show thatSince  Resnick , for allAfter some computation, we gety⟶∞, then limy⟶∞(1/yβ)=0, and limy⟶∞(1/yβbβ)=0. Therefore, from equation , histogram, and box plot of the sales data are provided in (1)MOW  model :(27)Hy;Φ(2)FW (flexible Weibull) model :(28)Hy;Φ(3)APTW  model :(29)Hy;ΦThe term “better modeling” is used in the sense that the ETXE-exponential distribution has the smaller values of the selected IC (information criterion) considered for comparison. The expressions of the IC are given by the following:(4)The AIC (Akaike IC)  is(30)AI(5)The BIC (Bayesian IC)  is(31)BI(6)The HQIC (Hannan-Quinn IC)  is(32)HQ(7)The CAIC (Corrected Akaike IC)  is(33)CAThe comparison of the ETXE-exponential is made with three well-known distributions available in the literature. The DFs of the competing models are as follows:(1)The AD (Anderson–Darling) test statistic:(2)The CM (Cramer–von Mises) test statistic:(3)The KS (Kolmogorov–Smirnov (KS) test statistic:In addition to the IC, we further considered three goodness of fit measures (g-o-f) including the following:p value are presented in The optimization technique is adopted to get the MLEs (maximum likelihood estimators) of the competing models. In this section, we implement the Newton–Raphson (NR) iteration approach to get the MLEs of the fitted models using the sales data. The MLEs of the fitted models are provided in p value = 0.9985. For the second-best competitive model (FW distribution), these measures are AIC = 648.4654, CAIC = 648.5599, BIC = 654.2005, HQIC = 650.7957, AD = 0.0719, CM = 0.4315, KS = 0.0861, and p value = 0.2896. We can see that the FW is the second-best model, but not in terms of KS and p value. From p value which is 0.8106.Based on the sales data, the results reported in Tables Furthermore, for the best description of the results of the sales data, the estimated PDF and CDF plots of the fitted models , MOW (green line), APTW (pink line), and FW (blue line)) are provided in k predictors is given byIn this work, we restricted our study to simple linear regression analysis by using the Twitter medium as a predictor variable. However, many other online mediums can be used for advertising purposes such as YouTube, Facebook, and Twitter. Therefore, in the future, we are intended to implement the multiple linear regression modeling approach to see the impact of different advertising mediums on sales. Generally, the multiple linear regression model (MLRM) with X1= medium 1, X2  = medium 2) advertising mediums; (ii) in phase two, the MLRM will be used by considering three  advertising mediums; and (iii) in phase three, the MLRM will be applied by taking four  advertising mediums.The future research study will be carried out in three different phases. For example, (i) in phase one, the MLRM will be implemented with two (The MLRM with two predictors is given by(1)Effect of Twitter and YouTube advertising mediums on salesX1=Twitter and X2 = YouTube) is given byThe MLRM with two advertising mediums ((2)Effect of Twitter and Facebook advertising mediums on salesX1=Twitter and X2 = Facebook) is given byThe MLRM with two advertising mediums ((3)Effect of Twitter and Instagram advertising mediums on sales.X1=Twitter and X2 = Instagram) is given byThe MLRM with two advertising mediums Effect of Twitter, YouTube, and Facebook advertising mediums on sales.X1=Twitter, X2 = YouTube, X3 = Facebook) is given byThe MLRM with three different advertising mediums ((2)Effect of Twitter, YouTube, and Instagram advertising mediums on sales.X1=Twitter, X2 = YouTube, X3 = Instagram) is given byThe MLRM with three different advertising mediums Effect of Twitter, YouTube, Facebook, and Instagram advertising mediums on salesX1=Twitter, X2 = YouTube, X3 = Facebook, X4 = Instagram) is given byThe MLRM with four different advertising mediums (In the future, we are also motivated to use the MLRM defined in equation  to see tt-test and correlation test are utilized. Based on the results of the t-test and correlation test, it is observed that Twitter advertising plays a useful role in increasing sales. Furthermore, a new HT model called the ETXE-exponential distribution is developed by combining the exponentiated-exponential model with the ETX family. The ETXE-exponential distribution was introduced to provide a close fit to sales data, which is an important area of the finance sector. For proving the applicability of the proposed model, the sales data is analyzed. The comparison of the ETXE-exponential distribution was made with MOW, APTW, and FW distributions. By taking into account certain analytical tools, we showed that the ETXE-exponential distribution is the best model for modeling data in the finance sector.In this research, we studied the relationship between Twitter advertising and sales by adopting the simple linear regression approach. For testing the impact of Twitter medium as an advertising tool, two main statistical approaches such as


Neuroblastoma (NB) is the most common extra-cranial solid paediatric cancer and is responsible for 15% of childhood cancer deaths. Patients with NB are characterized by presenting a very heterogeneous clinic  and also both spatial and temporal intra-tumour heterogeneity (ITH) reflected in their genetic aberrations, which may be the consequence of the coexistence of different microenvironments within the tumour. Applying pangenomic techniques to detect genomic aberrations in different biopsies (solid and liquid) of high risk NB (HR-NB) we have detected spatial ITH in a surprisingly high percentage  of the studied cohort. Moreover, a positive association between this heterogeneity and survival has been found. Confirming these results, combining tumour material analysis in a large cohort of HR-NB will have a major impact in the genetic diagnosis routine procedure, and would also entail a revision of the prognosis of patients with ITH.MYCN amplification. Moreover, all patients in the studied cohort with high ITH (defined as 50% or more genomic aberration differences between areas of a tumour or simultaneously obtained samples) survived after 48 months. These results clearly support analysing at least two solid tumour areas (separately or mixed) and liquid samples to provide more accurate genomic diagnosis, prognosis and therapy options in HR-NB.Spatial ITH is defined by genomic and biological variations within a tumour acquired by tumour cell evolution under diverse microenvironments, and its role in NB patient prognosis is understudied. In this work, we applied pangenomic techniques to detect chromosomal aberrations in at least two different areas of each tumour and/or in simultaneously obtained solid and liquid biopsies, detecting ITH in the genomic profile of almost 40% of HR-NB. ITH was better detected when comparing one or more tumour pieces and liquid biopsy (50%) than between different tumour pieces (21%). Interestingly, we found that patients with ITH analysed by pangenomic techniques had a significantly better survival rate that those with non-heterogeneous tumours, especially in cases without  In addition to genetic and clinical inter-tumour heterogeneity, which refers to variations found between different patients in tumours and/or behaviour, tumours can also present with spatial and/or temporal intra-tumour heterogeneity (ITH). Spatial genomic ITH is characterised by genomic variations within a tumour lesion, or between different tumours located in the patient at the same time point in the case of metastatic disease, and consists of multiple cells or sub-clones, each possessing different genomic profiles . TemporaMYCN amplification (MNA) and segmental chromosomal aberrations (SCAs) [ALK, TERT, ATRX, TP53 and alternative lengthening of telomeres (ALT) abnormalities, to typify around 80% of HR-NB [ALK mutated NB cell-line [In neuroblastoma (NB), ITH has been associated with a spectrum from favourable to unfavourable patient outcomes ,6. NB iss (SCAs) . Recent of HR-NB . Tumour of HR-NB ,13. In tof HR-NB , rich inof HR-NB ,16,17,18ell-line . A contiell-line .In circulating tumour DNA (ctDNA) studies, liquid biopsy is viewed as an emerging tool for identifying genetic alterations across the whole tumour, and is particularly significant in highly heterogeneous neoplasia. Generally, HR-NB has been shown to release large quantities of ctDNA . In factALK gene [ITH of both MNA (hetMNA) and typical deletion at 1p36 of the NB have been detected by fluorescence in situ hybridisation (FISH) in different studies ,25, one ALK gene ,22,26,27ALK gene .MYCN non-amplified (non-MNA), homogeneously amplified (MNA), and hetMNA primary HR-NB tumours. We applied pangenomic techniques to detect chromosomal aberrations in multiple areas of each tumour and/or in ctDNA from liquid biopsies. Our results highlight the need to analyse at least two solid tumour areas and liquid samples to gain an overview of the entire chromosome landscape at a particular time point, which provides more accurate genomic diagnosis, prognosis and therapy options.In this paper we document an in-depth study of spatial ITH, including several pieces of A total of 129 samples from 58 NB patients diagnosed between 2002 and 2018 were included in this study, all classified as HR-NB following INRG criteria [All samples were sent to the Spanish Reference Centre for NB Molecular and Pathological studies . Histopathological data were provided by the group pathologist. Clinical data were provided by the attending paediatric oncologist where possible, or by the Reference Centre for NB Clinical Studies, including outcome data on event free survival (EFS), defined as length of time from date of diagnosis to any progression, death or to the date of last contact; and overall survival (OS), defined as length of time from date of diagnosis until death or last medical check-up in surviving patients.MYCN gene were performed on tumour imprints and/or FFPE sections of the tumour with MYCN (2p24)/AFF3 (2q11)  probe and 4,6-diamidino-2-phenylindole (DAPI) probe as a counterstain. Definition of MNA versus hetMNA samples was as previously described [MYCN gain also allowed), versus some with MNA and others with whole or numerical copy number anomalies of chromosome 2. FISH of 1p and 11q regions were also performed in most samples, with the SRD (1p36)/SE1 and KMT2A/SE11  probes, respectively. FISH results were completed with pangenomic techniques.FISH analyses for the escribed : all malDNA from 83 frozen and 10 FFPE tumours was extracted as described elsewhere . A ctDNAThe vast majority of tumour samples (112) were analysed using different high density single nucleotide polymorphism arrays (HD-SNPa): Affymetrix kits used were CytoScan HD DNA for frozen tumours, and OncoScan CNV gene chips for DNA from FFPE tumors and for ctDNA from plasma samples, following by analyses using Chromosome Analysis Suite 3.2 (ChAS) software  and Nexus 10.0 Copy Number Discovery . The Illumina kits  used were HumanCytoSNP-12 for frozen tumour DNA samples and HumanCytoSNP FFPE-12 for FFPE ones, following analysis with GenomeStudio and KaryoStudio software, version 1.4. Only 12 tumour DNA samples were analysed by SNPa with GeneChip Human Mapping 250K . Primary data analysis was performed using GDAS 3.0.2 software , while genomic profiles were generated using CNAG  version 3.0, with the AsCNAR function . DNA amplification, tagging and hybridization to gene chips were performed according to the manufacturer’s protocol.Only five tumour DNA samples belonging to five patients were analysed with this technique and were compared to HD-SNPa results to study ITH. MLPA was performed using the SALSA MLPA Kit P251/P252/P253 developed by MRC-Holland in cooperation with the International Society of Paediatric Oncology European Neuroblastoma (SIOPEN). The technique and interpretation guidelines are as described elsewhere .ALK mutations were studied in 37 out of 58 cases, using Sanger, next generation sequencing (mean depth 80×), or targeted deep sequencing (TDS) approaches. All detected mutations were validated by a second independent experiment [periment . Alternaperiment  in 17 ofp-values under 0.05 were considered statistically significant. Survival analyses were carried out using Kaplan-Meier curves. Additionally, multivariate Cox regression analysis was undertaken with stepwise, forward and backward  methods to calculate the impact of ITH on patient survival.All data were analysed using SPSS analysis software (version 26). Pearson’s χ2 statistic was used to identify differences in frequency of each genomic aberration between HR-NB groups. Among the 58 HR-NB cases included in our study, 22 cases (37.9%) had ITH from the SCAs observed in pangenomic analysis between patient samples, from either tumour or liquid biopsy . SpecifiDifferent solid areas of the same tumour were analysed in 24 out of 58 cases (49 out of 93 studied DNA samples), and five of them (21%) showed ITH: 13 tumours were non-MNA, of which only two had ITH (15%), six were MNA and three of them presented ITH (50%) and the remaining five were hetMNA, in which HD-SNPa found no differences in SCAs . We analMYCN amplification and also in loss at 1p  and ATRX genes (2 non-MNA cases). Presence of any of these genomic aberrations was considered a genomic event for statistical analysis. Finally, only one genomic event, gain at 12q, was significantly positively associated with ITH , but this event was not associated with survival.The number of chromosomal aberrations in ITH cases ranged from 4 to 23, with an average of 3.55 genomic events which differed between ITH tumour samples (an average of 36% differing genomic events) . Besidesp-value < 0.01) in the number of SCAs between tumour types related MYCN status when we distributed data into quartiles, with ITH non-MNA tumours having more SCAs  in frequency between non-MNA, MNA and hetMNA tumours. Most notable among the typical SCAs were losses at 3p and 4p, while gains at 7q and 12q were predominant among the atypical ones with more prevalence in non-MNA and hetMNA cases. As is already known, 11q− is also less common in MNA tumours , 2 of which displayed ITH. No FSCAs of either gene were detected in MNA cases. ALK mutation was found in 3 of 37 studied tumours (2 MNA and 1 non-MNA) and amplification in 2 out of 58 cases (both MNA). Finally, ALT was present in 5 out of 17 analysed cases  was present in 18 (31%) cases. A chromotripsis-like phenomenon involving eight chromosomes was detected in 6 cases  without ITH being observed in this genomic event. FSCAs affected 11 cases (18%) of ed cases .Of the 32 non-MNA primary tumours, 14 showed spatial ITH (43.75%), found in 65% of non-MNA tumours when at least one solid and one liquid biopsy were analysed (11 out of 17), and 15% of cases with two or more solid areas analysed (2 out of 13). Total number of SCAs was higher in solid tumour DNA than in ctDNA in 4 tumours, with the opposite in 7. However, in some cases SCAs were only detectable in solid samples, and others only in liquid biopsy A. One paTypSCAs and some atypSCAs were found in greater proportion in ITH than non-ITH cases . MoreoveOf 20 MNA cases, 8 showed between-sample differences in pangenomic profile A,B. SpecSmall differences between ITH and non-ITH cases were found in the number of SCAs: an average of 5.5 (median 5) vs. 5.3 (median 4.5), respectively, and in the type of typ- and atypSCAs . The nump-value = 0.043, OS p-value = 0.041, p-value = 0.009 and 0.006, respectively) . Notably, longer survival was observed at 36 and 60 months (EFS ctively) D. No sigctively) E.MYCN status. In addition, we investigated the association with survival of number of SCAs, typSCAs and the most frequently found atypSCAs, presence of cnLOH and FSCAs in TERT and ATRX genes, none of which showed significant p-values  Cox regression analysis. However, EFS cox regression test  also showed as significant An aberrant TME can promote genetic instability and could even compromise the DNA repair pathways needed to prevent malignant transformation ,33,34. TThe INRG Biology Committee has already recommended ensuring that a sufficient amount of tumour material is analysed, as determined by a pathologist, from at least two different tumour regions in NB genetic diagnosis . GuideliIn our study we detected genetic spatial genomic ITH with HD-SNPa in 21% of cases with at least two tumour pieces analysed, which permitted a greater representation of overall TME involvement. This considerable percentage shows the importance of analysing the largest possible area of the tumour, which can be achieved by mixing material from different tumour areas to obtain DNA from diverse TME locations. In 50% of cases ITH detection was enhanced, when we obtained DNA from one or more tumour specimens and ctDNA from liquid biopsy. These results demonstrate not only the extent of tumour heterogeneity, but also the sensitivity of liquid biopsy in SCA detection, in agreement with reports over recent years ,43,44,45A recent study comparing genetic results derived from solid tumours and liquid samples of NB suggested that solid tumour-specific alterations could correspond to clones that might release less ctDNA, meaning less aggressive cells, while ctDNA-specific alterations could correspond to more aggressive clones or to those originating from metastatic sites . We founInterestingly, patients with ITH had better EFS and OS when we included all the studied cases in the Kaplan-Meier analysis, results that were replicated when patients with non-MNA tumours were analysed separately, but in contrast were not statistically significant for MNA cases, despite their higher rate of survival in ITH ones. Taking into account these results, we expanded our ITH analysis to study whether the number of between-sample differences in genomic events in the same patient was associated with survival. When at least 50% of genomic events differed between samples, indicating with high ITH tumours, all patients were found to survive.Various hypotheses could explain this finding. Patients with greater ITH could survive due to the competition between several clones, possibly influenced by a heterogeneous TME. This has been described as occurring most typically during early tumour formation and proliferation, when tumour cells are trying to adapt to the niche, in contrast to non-heterogeneous tumours in which an aggressive and predominant clone has already become established, conferring worse prognosis ,3. AmongTERT and ARTX genes, which are involved in telomere maintenance through telomerase activity and alternative lengthening of telomeres [Our findings support a revised approach to the currently reported frequency of several recurrent SCAs and their involvement in HR-NB. We detected greater incidence of certain atypical SCAs  than of other typical SCAs like 1q gain or 4p loss. Moreover, some had differing prevalence in non-MNA, MNA and hetMNA tumours, and these associations should be taken into account. Gain at 7q was mainly found in non-MNA and hetMNA cases, and was differentially found across tumour samples. Various publications have also described a high presence of this aberration in NB, especially in stage four ,58. Althelomeres . Both geelomeres , detectiALK mutation, ALT alterations). Moreover, beyond its importance in diagnosis, prognosis and therapy, ITH detection can also provide greater insight into the internal composition of tumours [We detected ITH in almost 40% of our samples, and the true percentage could even be higher. The HD-SNPa obtained from each tumour sample reflects the chromosomic aberrations of an aggregate of minor clones that coexist in that piece, a metagenome. However, HD-SNPa software analysis leads to easy ITH detection with at least 30% allelic frequency, or even at lower proportions in good quality samples , althoug tumours . In-dept tumours .As ITH is associated with survival in our HR-NB patients, and especially in non-MNA tumours, further studies with larger NB cohorts are clearly warranted to investigate its value as an independent prognosis factor. If ITH really implies increased survival, then this should be taken into consideration and review and possible adjustment of standard therapy should be encouraged. A more detailed study of ITH, potentially revealing genomic aberrations present in only a few cells, will also help decision-making on targeted treatment. Analysing multiple tumour samples (a mix of their DNAs) plus liquid biopsy to make a routine accurate genomic diagnosis seems an optimal approach to detect ITH and move towards improved precision medicine. Whole genome (or exome) sequencing, digital PCR and single-cell RNA-Seq are powerful techniques to detect and confirm genomic ITH, not only in SCAs but also in single nucleotide variants. Combined with digital image analysis of the TME, these genomic techniques will be useful to detect the ITH of the whole tumour, at genomic and morphological/topological levels, to predict the course of tumour clones and ultimately to determine prognosis and the best treatment options for each patient.


This study examines experimental evidence showing how ethics power allocation mechanisms affect an individual’s in-organization resource division and ethical behavior. We used two two-stage lab experiments to explore power seeking and usage; the experiments contained two stages of power contending and power usage. Stage one used two different power-seeking mechanisms in the honesty game. Stage two was based on the dictator game and the ultimatum game to measure an individual’s power usage. The results show that the decisions taken by power-holders could influence the optimization of collective resources, and power-holders who gain power with unethical methods could result in collective resource allocation inequities. With more balanced in-organization power, members tend to be more honest. Subjects also adjust their unethical behavior to adapt to the environment, which could cause the diffusion of unethical behavior. This paper re-designed the dictator game and the ultimatum game by adding an ethically vulnerable power acquisition mechanism. For organizations to prevent the disproportionate dispersion of resources and achieve more public benefits, it is meaningful for managers to create a proper in-organization ethical power allocation mechanism. The process of achieving positive organizational aims and increasing collective benefits requires efforts from both leaders and other members in an organization. However, in an organization, ethical scandals often accompany the process of fighting for higher power status, and the potential risks of gaining power through unethical methods have been noted ,2. This In organizations or groups, internal power is directly related to an individual’s ability to access resources in groups or organizations. Power-holders in organizations can make decisions regarding collective resource allocation and could be a key factor influencing the process of achieving optimized resources. Increased resource allocation efficiency and the positive use of internal power can encourage organization members to achieve pro-social goals, for example, by enhancing green organizational culture. Power-holders can enhance collective interests and build a harmonious organization atmosphere, while their selfish behavior promotes self-aggrandizement and results in a centralization of resources in the organization ,4.However, in-organization power inequities can negatively impact a group’s emotions, and the power-holders in these circumstances are typically considered responsible . A tradiIn this article, we experimentally studied the influence of different ethics power-allocation conditions on the division of collective resources using a two-stage laboratory experiment. We explored the relations between an individual’s unethical behavior to gain power and the resource allocation within organization members. We used two classic games in economics: the dictator game and the ultimatum game. In our experiments, we added an initial stage with two power-seeking mechanisms, dishonesty inducing and dishonesty proof, to assign roles to players and create a distinguished ethical environment for power seeking. In the dishonesty-inducing scenario, subjects can act dishonestly to acquire an advantage in power seeking. In the dishonesty-proof scenario, the roles are randomly assigned. Our 2 × 2 experimental design features four experimental treatments with different ethical environments and power conditions in order to study participants’ behavior in the power seeking and using stages.We found significant deceptive behavior in the dishonesty-inducing scenario for subjects with both higher and lower power states. The multiple-round experimental results show that different power states between members can impact their dishonesty. Power-holders in a dishonesty-inducing scenario presented significantly lower intentions of behaving dishonestly in an organization with more balanced power states than unbalanced power states. The subjects tended to increase dishonesty degrees over time and adjust their intentions of behaving dishonestly after the results from previous rounds. This adjustment of a dishonesty-inducing environment can cause a diffusion of dishonesty among organization members. Our results also show that the power-holders in previous experiment rounds, especially those who behaved unethically, will have higher intentions of being dishonest to gain advantages in gaining power in the future. However, generous power-holders in previous rounds have relatively lower intentions of behaving dishonestly in future power-seeking procedures. When power-holders and other subjects have more balanced power states in the organization, the former will become more generous over time. Our research finds the negative impacts of dishonest behavior on a power-holder’s distribution of collective resources. The interactive impact of dishonest behavior and power-balance also indicates that when the power states between members are more balanced, the dishonest power-holders will increase their altruistic actions to prevent unwanted punishment, which could decrease collective resources.The primary contribution of this article was the exploration of how different ethics power-allocation mechanisms affect individual behavior in both the acquisition and use of power. In addition to financial gains, the pursuit of power itself motivates individuals to engage in deception. We find that power gained through unethical methods typically results in larger profit dispersion and less altruistic behavior in groups. In contrast to the traditional negative stereotypes surrounding power and dictators, we find that different ethical power-allocation mechanisms are the key factors causing inequality between subjects in organizations. The unethical method of the power-allocation method in unbalanced power groups or organizations can decrease collective resources.The remainder of this article is structured as follows. Previous studies have highlighted the importance of members’ behavior for the benefits of ecosystem services programs ,10, and Power-holders can have individual-focused and/or group-focused goals; however, achieving individual-focused goals makes power-holders cooperate less with individuals of low-power status ,20,21. IAltruistic behavior can decrease the psychological costs of power-holders’ unethical actions . PreviouHowever, lying does not necessarily result in moral costs. Moral awareness may vary as individuals achieve different goals; under some circumstances, individuals will consider benevolent lies to be more ethical than selfish honesty ,31. OrgaA group’s ethical environment can influence members’ actions ,34. The Previous studies by economics and psychology experts using experimental methods have widely used the dictator game  and the Previous literature has indicated that power has different impacts on individuals’ self-interested and altruistic behavior. It has also indicated the importance of equities of power and resources in organization members’ behavior. Although power inequities lead to negative outcomes, positive inequities may lead to collective benefits. Based on the above literature, we predicted that different power states will impact an individual’s ethical behavior. We then designed our experiment based on previous theories and designs to achieve our research aims.The experiments were designed as a 2 × 2 (dishonesty proof versus dishonesty inducing × dictator game versus ultimatum game) two-stage experiment. In Stage 1, the participants compete via a dice-rolling procedure for the power to allocate resources by moving first in the second stage. We adopted two power-seeking mechanisms: dishonesty proof and dishonesty inducing. In the dishonesty-proof group, participants who roll larger dice numbers In each treatment, two subjects set up an experiment unit and hold a collective initial asset of In the dictator game treatments , the firIn the ultimatum game  treatmenWe conducted laboratory experiments from September to October 2019 at a Chinese university. We recruited 116 university students  from a pool of registered volunteers; 52 subjects participated in the dictator game treatments (22 in the dishonesty-proof scenario and 30 in the dishonesty-inducing scenario), and 64 subjects participated in the ultimatum game treatments (34 in the dishonesty-proof scenario and 30 in the dishonesty-inducing scenario). The experiments were conducted on computers using z-Tree . Before We used financial incentives for the participants in the experiments. In this experiment, 15 ECUs equaled CNY 1 (nearly EUR 0.14). All the experiments lasted for approximately 45–60 min, and the participants gained an average profit of CNY 35.26 (approximately EUR 5), including a CNY 10 (approximately EUR 1.4) attendance fee. This financial incentive matched the local hourly income. According to the post-experiment anonymous questionnaire, participants expressed high satisfaction regarding the monetary payoffs in this experiment.We predicted that power-balance can lead to a decline in holistic profit in the whole group for compensation for unethical behavior. Power-holders will learn to avoid punishment by becoming more honest because low-power individuals, who value procedural justice, can punish power-holders. Considering the role of intentions, the kindness and ethics of other subjects’ actions will affect the subject’s decision-making ,51. The We present the following hypotheses:Hypothesis 1.Subjects in dishonesty-inducing treatments are more likely to behave dishonestly in the dictator game than in the ultimatum game because the power statuses among subjects is more balanced.Hypothesis 2.Subjects in dishonesty-inducing treatments have a greater degree of dishonesty in the dictator game than the ultimatum game. The deviations between self-reported and true numbers will be greater in dictator game treatments.Hypothesis 3.Power-holders will increase their future intentions of behaving dishonestly to maintain their power.Hypothesis 4.Generous power-holders having offered more balanced resource allocation schemes in previous periods will increase their honesty.Hypothesis 5.Power-holders will be more generous when receivers hold more power and will increase their offers over time. In the dishonesty-inducing environment, the power-balance will be an important factor influencing the resource allocation scheme.Hypothesis 6.Subjects’ dishonest behavior affects the offers to receivers. Power-holders who gain power with dishonest behavior will offer less to receivers. The greater the deception degree that power-holders have, the less generous they will be.Hypothesis 7.Subjects’ dishonesty behavior and power-balance have interactive effects on the resource allocation scheme. In the ultimatum game treatments, when there is less power inequity, the dishonest power-holders will offer more to receivers to decline moral costs and prevent rejections.Hypothesis 8.In the dishonesty-inducing treatments, receivers are more likely to accept the offers because they are larger in these treatments than in dishonesty-proof treatments. The resource allocation will be more equal in this condition.In this section, we present our findings in two primary sub-sections: (1) dishonest behavior from subjects in the power-allocation stage and (2) resource allocation behavior within subjects in the power-using stage.In this section, we present our findings in two main sections: dishonesty behavior from subjects, and resource allocation behavior in power usage.p-value from the t-test for the differences between the dishonesty-inducing and dishonesty-proof treatments. The results show the that the self-reported numbers in dishonesty-inducing treatments are both larger than dice numbers in the two dishonesty-proof treatments. The p-values also determine that the observed differences are statistically significant (p < 0.01).We present the descriptive statistics in SD = 3.05) and 2.17 (SD = 2.89) in the ultimatum treatment. The difference between U-test result . Subjects tend to tell greater lies to gain power when they report fake dice numbers in dictator game treatments.We present the percentage of subjects’ dice numbers (self-reported or true numbers) from four treatments in Previous research has indicated that power can encourage deceptive behavior in organizations. We found that in a dictator game with greater power inequity, subjects present a higher degree of dishonest behavior. We also examined the probability of subjects being dishonest under different power-balance conditions. p < 0.01). The ultimatum game type has a significant negative impact on subjects’ willingness to behave dishonestly. Period significantly positively impacts dishonesty (p < 0.01). Subjects will increase their intention of behaving dishonestly over time. A true number is a significant negative factor for subjects’ dishonest behavior (p < 0.01). Subjects who have already generated a larger dice number have a very low intention of behaving dishonestly. It can be deduced from the descriptive results presented above that subjects are more honest in ultimatum games. Subjects in the dishonesty-inducing ultimatum game have a lower intention of reporting fake numbers. Subjects have an increased probability of behaving dishonestly in the dishonesty-inducing dictator game, in which the power states are less balanced. From these results, we could deduce that the following.The coefficients in model 1 indicate that the subject’s dishonest behavior is significantly affected by the type of game . If the subject acted as a proposer in previous Stage 1, they would significantly increase dishonest behavior in the next period. The power gained from the previous period could affect subjects’ dishonesty. Subjects who hold more power beforehand are more likely to act dishonestly in the future to take advantage in order to gain more power. A previous offer has a significant negative effect (p < 0.01). When subjects have witnessed a larger offer in the previous period, they behave more honestly in the next period. The large offer to receivers is a fairer resource allocation plan and can decrease subjects’ sense of inequity in the resource allocation stage. Moreover, previous deviations can positively and significantly affect dishonesty (p < 0.01). From the results in model 2, we find that subjects will adjust their dishonesty and learn from the results from the previous period.The results from model 1 and Result 2.In the dishonesty-inducing environment, subjects’ intentions of behaving dishonestly increases over time. Subjects will adjust their dishonest behavior over time to adapt to the environment, resulting in the diffusion of dishonesty among subjects. Previous power-holders will significantly increase their dishonesty intentions to take advantage in order to gain power in the future. Providing or receiving larger offers can significantly increase subjects’ future honesty. Larger previous deviations between two subjects’ numbers can increase subjects’ future dishonesty.p < 0.01). Then, we conducted further analyses to explore the potential factors that could result in the differences in resource allocation behavior between treatments.In p < 0.01). Proposers in dishonesty-inducing dictator treatment gained the highest profits in their offer period  and proposers in the dishonesty-inducing ultimatum game gained the lowest profits in their offer period . We calculated the rates of acceptance in the two ultimatum treatments and found no differences; both were 75%. Then, the Mann–Whitney U-tests of acceptance between dishonesty-inducing and dishonesty-proof ultimatum treatments showed no significant difference of acceptance behavior . The receiver’s rejection actions are not the sole factor resulting in the differences between the subject’s profits. We conducted Tobit regressions on the offers provided by proposers to explore other potential factors. The offer value is directly related to the subject’s profit. We conduct rank sum tests of proposers’ profits in each period to examine the differences between four treatments. The results in In the Tobit regression models, we focus on the factors that may result in distinguished resource allocation behavior on the behalf of power-holders: the power-balance among subjects, dishonesty inducing or dishonesty proof, period, dishonest behavior, and inequity of self-reported numbers. The dependent variables are the offers provided by proposers.p < 0.01). Proposers provide significantly larger offers to receivers in the ultimatum games (p < 0.01). Proposers in a more balanced power status offer more. Dishonesty inducing significantly negatively impacts offers, and the interaction dishonesty inducing × ultimatum has significant positive influence (p < 0.01). In the dishonesty-inducing ultimatum treatment, proposers tend to be more generous and provide larger offers.In models 1 and 2, we included all treatments and examined the impact of the power-balance between subjects and two power-seeking mechanisms indicating the different ethical environments in organizations. The primary independent variables in these two models are the dummy variable dishonesty inducing, which is equal to 1 if the treatment is dishonesty inducing; ultimatum, which is equal to 1 if the treatment is an ultimatum game; period; self-reported numbers; gender, which is equal to 1 if this subject is female; and age. We also introduced a two-way interaction term in model 2, dishonesty inducing × ultimatum. From the regression models, we can see that the ultimatum game significantly positively impacts the offers from proposers . We can see that proposers in the dishonesty-inducing ethical environment would increase their offers to receivers over time. The results also confirm the observed tendency of offers in different treatments in p < 0.01). The subjects who report larger dice numbers provide significantly smaller offers to receivers (p < 0.01). Since we found that subjects tell lies to gain power in Stage 1, the larger self-reported numbers have direct relations with dishonesty behavior. We can see that subjects who told greater lies in Stage 1 tend to reduce their offers to receivers.In models 3–5, we considered the dishonest behavior of subjects. These three models only included dishonesty-inducing treatments. We excluded the independent variable dishonest -inducing and added a dummy variable dishonesty, which is equal to 1 if the subject reports fake numbers in Stage 1. We also introduced an interactive variable dishonesty × ultimatum. In these three models, the period positively and significantly impacted the offer (p < 0.01). The two-way interaction term dishonesty × ultimatum also significantly positively impacts offers . The dishonesty-inducing or dishonesty-proof treatments are not key variables influencing acceptance. Therefore, we conducted Probit regressions to further analyze the receivers’ acceptance in the two ultimatum game treatments.First, p < 0.01). Dishonesty inducing has a significant impact on accepting offers (p < 0.01). Receivers would be more likely to accept the offer in the dishonesty-inducing treatments. We compared this result with previous findings in the Tobit regression results. The offer numbers in dishonesty-inducing ultimatum treatments are significantly larger and dishonest power-holders tend to give larger offers. Model 2 only considered the dishonesty-inducing ultimatum treatment. In model 2, when considering subjects’ dishonesty, the results show that receivers significantly increase their rejection actions over time (p < 0.05).Result 5.In the ultimatum game—in which subjects have more balanced power—receivers have a higher probability of accepting offers, which decreases over time in the dishonesty-inducing treatment.Individuals compete for power in organizations when it can serve their personal interests. In a dishonesty-inducing environment, individuals tend to behave dishonestly to gain power. Our knowledge of the relationship between ethics, deceptive behavior, and the process of gaining and using power remains limited. We examined individuals’ dishonest behavior in different power-balance scenarios and ethical environments. Subjects could experience a sense of inequity in both stages of the experiments: the inequity of power and the inequity of monetary profit. The results showed that subjects in a more balanced power status will be more honest. This could reduce the negative psychological effects of subjects’ dishonest behavior . SubjectWe observed that previous power-holders would have larger intentions of behaving dishonestly to take advantage in order to gain and maintain power. In this study, the two Nash equilibriums of Stage 2 are that if subjects behave on pure self-interest, the proposers should offer nothing or the minimum possible amount. When subjects do not obey the Nash equilibriums and offer more than the minimum amount, it is called altruistic behavior that could reduce moral costs. The offers to others also significantly positively impact their further intentions of behaving dishonestly. Generous power-holders tend to behave more ethically in the future. We observed in our experiment that unethical behavior significantly increases over time in groups. Individuals can learn from the results in previous periods and adjust their dishonest behavior and self-interested behavior in a dishonesty-inducing environment.Power-holders’ dishonesty has significant negative effects on their offers to receivers. The degree of power-holders’ deception can also significantly negatively affect the offer amounts. Power-holders using unethical methods to gain power are a primary factor that causes inequities in the allocation of collective resources. Our results show that dishonest power-holders with more inequities of power can be less generous and try to maximize personal interest. However, in a more balanced power structure, when receivers hold veto power, power-holders in dishonesty-inducing groups tend to be more generous to others. The interactive factor of dishonest power-holders in the ultimatum treatments significantly positively impacts the offers to receivers. In turn, the receivers tend to accept more in the dishonesty-inducing scenario. However, this mutual benefit action is relatively fragile and the probability of acceptance is negatively impacted by the period.In our research, we examined how individuals gained and used power in different ethical environments. Our experiments established that power gained through unethical behavior can negatively impact collective interests and produce negative outcomes in the optimization of collective resources for positive organizational aims. From an organizational perspective, we doubt the traditional view that an unbalanced power status causes inequitable distributions of profit. Our results show that when power-holders use unethical methods to gain power, the inequities of organizational resource allocations will be larger and harm collective interests. From a practical perspective, this research suggests that leaders of organizations should avoid the negative impacts of power gained through unethical methods. Collective resource dispersion can negatively influence a workplace climate and workers’ performance and reduce the efficiency and increase invalid competitions in organizations, which could harm the organization’s sustainable value. To prevent disproportionate resource dispersion, managers should create proper ethical environments.The primary limitation of this research is the small sample size. Another limitation is the fact that the experiments were performed using computers, which may have limited their authenticity. Field experiments should be used in further research to test these relationships under natural conditions. Further studies can also be conducted in cooperative situations to explore ethical behavior in organizations.


The primary objective was to examine the role of pelvic fluid observed during transvaginal ultrasonography (TVS) in identifying ovarian malignancy. A single-institution, observational study was conducted within the University of Kentucky Ovarian Cancer Screening trial from January 1987 to September 2019. We analyzed true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) groups for the presence of pelvic fluid during screening encounters. Measured outcomes were the presence and duration of fluid over successive screening encounters. Of the 48,925 women surveyed, 2001 (4.1%) had pelvic fluid present during a TVS exam. The odds ratio (OR) of detecting fluid in the comparison group  significantly differed from that of the FP cases : 9.1–19.8), the TP cases with a low malignant potential , TP ovarian cancer cases , and FN ovarian cancer cases . The mean duration that pelvic fluid was present for women with TN screens was 2.2 ± 0.05 encounters, lasting 38.7 ± 1.3 months. In an asymptomatic screening population, free fluid identified in TVS exams was more associated with ovarian malignancy than in the control group or benign ovarian tumors. While pelvic free fluid may not solely discriminate malignancy from non-malignancy, it appears to be clinically relevant and warrants thoughtful consideration. Ovarian cancer continues to be the most lethal gynecological cancer with most patients facing a diagnosis of late stage metastatic disease that is associated with a five-year survival of only 30%. While the lifetime risk of ovarian cancer is less than breast cancer, it has a death-to-incidence ratio that is three to four times more than breast cancer ,2,3. OvaTransvaginal ultrasonography (TVS) is a safe, minimally invasive  and costIn this report, we examined the frequency and duration of free fluid during TVS exams in a large screening population and correlated the findings with the patient diagnosis. Our primary objective was to determine if free fluid provides additional information in predicting ovarian malignancy compared to ultrasound alone.Women enrolled in the KYOVS trial from January 1987 to September 2019 were eligible for study inclusion. The University of Kentucky institutional review board for human studies approved this prospective cohort trial . Eligibility criteria included asymptomatic women aged ≥50 years and asymptomatic women aged ≥25 years with a documented family history of ovarian cancer in at least one primary or secondary relative. Excluded from enrollment were women with a known ovarian tumor or a personal medical history of ovarian cancer. All study patients provided written informed consent before undergoing screening with transvaginal ultrasound. A board-certified gynecologic oncologist reviewed all ultrasound images. The follow-up algorithm of normal and abnormal scans has been previously published .Individuals were designated as a true-positive (TP), if they had histologically confirmed primary epithelial ovarian cancer, a low malignant potential ovarian tumor, a non-epithelial ovarian malignancy, or metastatic disease to the ovary. A designation of a true-negative (TN) screen was applied when a diagnosis of ovarian cancer did not occur for at least 12 months after a “normal” TVS exam. False-positive (FP) screens included an abnormal TVS screen found surgically to have benign ovarian histology, including serous cystadenoma, endometrioma, mucinous cystadenoma, cystic teratoma, fibroma, thecoma, Brenner tumor, leiomyoma, hydrosalpinx, paratubal cyst, and hemorrhagic cyst. We categorized study patients with a diagnosis of ovarian cancer occurring within 12 months of a screening TVS exam that reported “normal” findings as a false-negative (FN).All sonographers in the University of Kentucky Ovarian Cancer Screening Program are certified by the American Registry for Diagnostic Medical Sonography . We digitally recorded the findings of each ultrasound, including the presence or absence of pelvic fluid, in case report forms maintained on a Medlog database . We interpreted any amount of free fluid as a positive finding, including the trace free fluid, small volume fluid (≤10 mL), and fluid collections exceeding 10 mL. We determined the overall rate of free fluid identification as the ratio of fluid positive cases to the total number of cases in individual categories.2 or Fisher’s exact test for categorical data. We assessed continuous variables for normality and used non-paired t-tests or Wilcoxon rank-sum tests as appropriate. For all analyses, we used a two-tailed test with a significance level of p < 0.05. We used GraphPad Prism 5.01  to perform the analyses.We compared demographic characteristics using Χ2-square tests, Fisher’s exact probability, and confidence intervals (CIs) [We calculated the probability ratio (PR) for each group as the probability of an event in one of the screening groups . For example, the screening groups consisted of women with fluid and without fluid relative to women with and without fluid in the comparison group. The comparison group consisted of premenopausal normal women (TN) with a body mass index (BMI) of <30. Pre- and post-menopausal women with a BMI of ≥30 were found to have a lower PR of free fluid than those with a BMI of <30; however, a very large body habitus can limit the detection of free fluid by TVS. For this reason, we defined the comparison group as subjects with a BMI of <30. We used VassarStat based on logistic regression to calculate PRs, odds, odds ratios (ORs), log odds, Phi coefficients of association, association identified by Χls (CIs) .The duration of free fluid was expressed both as the number of consecutive TVS exams during which fluid was present and as a time duration during consecutive TVS exams.Since 1987, the University of Kentucky Ovarian Screening Program has performed 326,998 TVS screens on 48,925 women. We observed free fluid in 2001 (4.1%) of those encounters. TP screens included 78 ovarian malignancies , 20 tumors of a low malignant potential , and 23 malignancies of a non-ovarian origin . Only one of the TP cases was observed to have fluid present in a prior normal TVS exam. There were 614 FP screens classified as high risk for ovarian cancer but found to have benign pathology . Nine of these cases occurred in a prior normal TVS examination. The TN screens included 41,996 cases that screened negative for malignancy and did not develop ovarian carcinoma (1948 fluid-positive (4.6%): 1071 fluid-positive cases were associated with a normal TVS examination, while 877 were associated with an abnormal TVS examination). There were 21 cases of ovarian cancer diagnosed within 12 months of an FN TVS scan . All of these cases occurred in the absence of an abnormal TVS exam.The demographic characteristics of the TN cohort demonstrated some significant differences between fluid-positive and -negative subgroups. The fluid-negative subgroup was older and had a higher parity and a higher BMI. They were also less likely to be nulliparous or have a family history of ovarian or colon cancer. Although they were less likely to have ever used hormone replacement therapy (HRT), more were using HRT at the time of their last visits .p = 0.0344, The fluid-positive and fluid-negative subgroups with a TP  or FP scThe PRs and the ORs were determined for women designated as TN for ovarian cancer based on TVS findings. These women were then grouped on the basis of the menopausal status, their BMI, and normal vs. abnormal TVS exams .Using this categorical basis to normalize the PR and OR determination, we observed that TN premenopausal women with a BMI ≥30 had the lowest PR and OR of fluid observed during TVS exams. However, it was possible that a large body habitus might have interfered with detecting fluid in these women during their TVS exams. This effect would be least likely in TN premenopausal women with a BMI <30 that had received a normal TVS exam, and we selected this group to normalize the PRs and the ORs. In this analysis, the PRs ranged from 0.24 to 11.73, while the ORs ranged from 0.24 to 12.26 , so thatOvCa = 36.14–40.48 vs. PRBenign = 1–12.16; p < 0.001). There was no statistical difference in the detection of free fluid between TP women with a low malignant potential  and TP women with ovarian cancer  or women with benign findings   .p > 0.05) from women who were TN for ovarian cancer (p < 0.001) from any category of women who were TN for ovarian cancer (p < 0.001) from any category of women who were TN for ovarian cancer while not significantly different (p > 0.05) from women with fluid who were TP for ovarian cancer (The PRs for women with benign findings (identified by red font “1”) or women with tumors of an LMP (identified by red font “2”) were not significantly different ; however, fluid persisted beyond three encounters for only 9% of the study group. On average, fluid persisted for a mean of 2.2 TVS exams (SEM: standard error of the mean = ±0.05 TVS exams). Notably, 28 TN women (1.4%) had persistent fluid for over 10 encounters . The majn = 843), fluid persisted for a mean of 38.7 months (SEM: ± 1.3 months) (In TN cases where pelvic fluid was present for more than one TVS exam ( months) . The medOverall, fluid resolved in 77% of TN cases with no statistical difference between persisting fluid-positive cases for normal (2.21 ± 0.07) versus abnormal (2.08 ± 0.07) encounters.TP = 36.14; ORFN = 40.48). This is in keeping with the evidence that potentially malignant ovarian tumors become morphologically more complex as they progress toward malignancy over time [Abdominopelvic ascites is a well-known sign of advanced ovarian, fallopian tube, or peritoneal malignancy, but the importance of free fluid during screening or diagnostic ovarian TVS is less evident. The findings of our investigation confirmed an association between free fluid identified on ovarian TVS and ovarian malignancy. To predict ovarian malignancy, the ADNEX model (Assessment of Different NEoplasias in the adneXa) created by the European International Ovarian Tumour Analysis study group considers nine variables in a polytomous prediction to categorize malignant risk ,28. The p < 0.05). For non-malignant cases, more than half (56.7%) of the TN screens had fluid present for one encounter, 9% for more than three, and 1.4% for more than 10 encounters. Thus, our data indicates that free fluid mostly disappears in non-malignant scenarios if serially monitored. Because of this observation, it can be asked if fluid might be due to follicular fluid entering the peritoneal cavity during ovulation. This possibility is unlikely, because premenopausal TN screens were similarly low in the observation of fluid as postmenopausal cases . Another study of 60 women with histologically confirmed primary peritoneal cancer investigated disseminated peritoneal carcinomatosis using TVS and documented the presence of free pelvic fluid [Only a few studies have reported the presence of pelvic fluid in investigations of ovarian cancer. An underpowered study of 17 women evaluated an intra-ovarian blood flow with 3-dimensional power Doppler ultrasound before surgical intervention in women suspected of having ovarian carcinoma  and founic fluid . Despiteic fluid ,41.This study has several strengths. These data resulted from a substantial cohort of patients all screened in one program where operations and techniques were well standardized. Throughout this study, the same Director of Research, Edward J. Pavlik (PhD) provided consistent oversight and the same clinician, John R. VanNagell Jr., (MD) reviewed all TVS scans. Lastly, it is relatively easy to identify free fluid on pelvic ultrasound because of the inherent contrast between water-dense fluid and pelvic soft tissue.There are also some study limitations. First, sonographers did not quantify the volume of the pelvic free fluid on all studies. As a result, we included those with recorded fluid volumes and test entries indicating fluid, possible fluid, ascites, and possible ascites. This approach may have underestimated the number of subjects who had pelvic fluid present, because it was not a required outcome measure. Since the peritoneal cavity contains typically 50–75 mL of physiologic fluid that serves to lubricate the abdominal wall and viscera ,43,44, sPelvic fluid identified during TVS screening is associated with ovarian malignancy and should not be disregarded. The presence of free fluid during TVS should heighten awareness for malignancy, particularly for an ovarian tumor with a high-risk morphology. When free fluid associated with benign ovarian tumors is present for more than one encounter, half of the cases resolve within 24 months. Since free fluid is associated with ovarian malignancy, we recommend serial ultrasound surveillance, until a malignant morphology is suspected or the fluid resolves.


In “A Novel Remote Follow-Up Tool Based on an Instant Messaging/Social Media App for the Management of Patients With Low Anterior Resection Syndrome: Pilot Prospective Self-Control Study” :e22647) the authors noted one error.In the “Acknowledgments” section, a correction has been made to show the correct Grant Number.The following sentence appeared in the originally published manuscript:This project is supported by the National Key R&D Program of China (Grant No. 2145000042).This sentence has been corrected to:This project is supported by the National Key R&D Program of China (Grant No. 2017YF0908203).The correction will appear in the online version of the paper on the JMIR Publications website on April 19, 2021, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.