Locally Advanced oral Squamous cell Carcinomas: Auditing and Outcome Appraisal.

Introduction: Patients with OSCC in India (oral squamous cell carcinoma) presents at a later stage with approximately 28% presenting at stage III and 64% at stage IV disease. In this retrospective study we have reviewed the treatment modalities rendered and outcomes associated for the management of locally advanced oral squamous cell carcinoma in our Institute. We evaluated the survival data and the factors effecting survival. Methods: Kaplan Meir method was used to evaluate OS and DFS rate and log rank test was used to compare the survival amongst groups. Cox regression analysis (univariate and multivariate) was used to evaluate the hazard ratio to find out the possible factors influencing risk of death and disease. Results: The median OS and DFS in our study were 32 and 24 months respectively. On a subset analysis of only T4b patients who underwent either upfront surgery or induction chemotherapy followed by surgery there was no significant difference in OS and DFS. All patients with TURD had partial response after induction chemotherapy and were subjected to surgical resection followed by adjuvant therapy. Conclusion: Extracapsular spread, bone involvement, skin infiltration, treatments, surgical margins and Lymph node size are the prime predictors of survival.Upfront surgery remains the standard of care for resectable LAOSCC. Induction chemotherapy might improve the resectability in technically unresectable OSCC. There is no difference in survival between concurrent chemoradiation, sequential chemoradiation and radical radiotherapy in the management of unresectable disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s12070-023-04168-4.

A remark on "COVID-19: Perturbation dynamics resulting chaos to stable with seasonality transmission" [Chaos, Solitons and Fractals 145 (2021) 110772].

In Batabyal (2021)[2], introducing an extension of the well-known susceptible-exposed-infected-recovered (SEIR) model with seasonality transmission of SARS-CoV-2, the author has derived and discussed various analytical and numerical results. Careful scrutiny of the said article brings about some genuine issues pertaining to the model formulation, analysis and numerical studies carried out in Batabyal (2021)[2]. Given the present pandemic and the havoc it has been causing throughout the world, and the responsibility of giving out rightful information/results backed by scientific proofs, there is a pressing need to address issues of such kind right away.

A novel logarithmic operational law and aggregation operators for trapezoidal neutrosophic number with MCGDM skill to determine most harmful virus.

In the current era, the theory of vagueness and multi-criteria group decision making (MCGDM) techniques are extensively applied by the researchers in disjunctive fields like recruitment policies, financial investment, design of the complex circuit, clinical diagnosis of disease, material management, etc. Recently, trapezoidal neutrosophic number (TNN) draws a major awareness to the researchers as it plays an essential role to grab the vagueness and uncertainty of daily life problems. In this article, we have focused, derived and established new logarithmic operational laws of trapezoidal neutrosophic number (TNN) where the logarithmic base µ is a positive real number. Here, logarithmic trapezoidal neutrosophic weighted arithmetic aggregation (L a r m ) operator and logarithmic trapezoidal neutrosophic weighted geometric aggregation (L g e o ) operator have been introduced using the logarithmic operational law. Furthermore, a new MCGDM approach is being demonstrated with the help of logarithmic operational law and aggregation operators, which has been successfully deployed to solve numerical problems. We have shown the stability and reliability of the proposed technique through sensitivity analysis. Finally, a comparative analysis has been presented to legitimize the rationality and efficiency of our proposed technique with the existing methods.

A new approach to evaluate linear programming problem in pentagonal neutrosophic environment.

In this paper, authors disclose a new concept of pentagonal neutrosophic (PN) approach to solve linear programming (LP) problem. To best of our insight, there is no approach for solving PNLP problem. For the first time, we take up the PNLP problem where the objectives, constraints are considered as pentagonal neutrosophic numbers (PNN). To deign our algorithm, we described the PN arithmetic operation laws and mathematical computation in PNN environment. This proposed method is based on ranking function and convert to its equivalent crisp LP (CrLP) problem. The obtained CrLP issue is presently being tackled by any LP method which is effectively accessible. To legitimize the proposed technique, some numerical tests are given to show the adequacy of the new model.

Quantifying the statistical power of monitoring programs for marine protected areas.

Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500-m ROV transects) and temporal replication (every 1-3 yr) to detect expected post-MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

Melanoma Diagnosis Using Deep Learning and Fuzzy Logic.

Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features. In recent years, deep convolutional neural networks (DCNNs) have succeeded in achieving more encouraging results yet faster and computationally effective systems for detection of the fatal disease are the need of the hour. This paper presents a deep learning-based 'You Only Look Once (YOLO)' algorithm, which is based on the application of DCNNs to detect melanoma from dermoscopic and digital images and offer faster and more precise output as compared to conventional CNNs. In terms with the location of the identified object in the cell, this network predicts the bounding box of the detected object and the class confidence score. The highlight of the paper, however, lies in its infusion of certain resourceful concepts like two phase segmentation done by a combination of the graph theory using minimal spanning tree concept and L-type fuzzy number based approximations and mathematical extraction of the actual affected area of the lesion region during feature extraction process. Experimented on a total of 20250 images from three publicly accessible datasets-PH2, International Symposium on Biomedical Imaging (ISBI) 2017 and The International Skin Imaging Collaboration (ISIC) 2019, encouraging results have been obtained. It achieved a Jac score of 79.84% on ISIC 2019 dataset and 86.99% and 88.64% on ISBI 2017 and PH2 datasets, respectively. Upon comparison of the pre-defined parameters with recent works in this area yielded comparatively superior output in most cases.

Size-dependent apoptotic activity of gold nanoparticles on osteosarcoma cells correlated with SERS signal.

In the last decade, gold nanoparticles have emerged as promising agents for in vitro bio-sensing and in vivo cancer theranostics. However, different investigations have reported widely varying cytotoxicity and uptake efficiency of gold nanoparticles depending upon their size. Therefore, more extensive studies are needed to standardize these biological effects as a function of size on a particular cell line. In addition, to obtain robust confirmation on the correlation of a size to biological effect, thorough mechanistic study must also be performed. In this study, the size dependent biological activities of gold nanoparticles on osteosarcoma cells is investigated towards exploring their potential theranostic application in bone cancer, for which very scarce literature reports are available. Tris-assisted citrate based method was optimized to synthesize stable gold naoparticles of 40-60 nm sizes. Nanoparticles were characterized through UV-Vis spectroscopy, field emission scanning electron microscope (FESEM) and dynamic light scattering (DLS). Increasing concentrations of gold nanoparticles (AuNPs) of 46 nm size, enhanced the rate of reactive oxygen species (ROS)-induced apoptosis in MG63 cells by disrupting their mitochondrial membrane potential. Considerably higher cell death was observed for 46 and 60 nm AuNPs compared to 38 nm at all concentrations of 200, 400 and 800 ng/mL. Further, molecular signatures of cellular apoptosis under nanoparticle treatment were optically assessed through surface enhanced Raman scattering (SERS). A significant Raman enhancement in cancer cells under treatment of larger gold nanoparticles (46 and 60 nm) at fixed wavelength of 785 nm and laser power of 8.0 mW was evident. In corroboration with molecular biology techniques, SERS observation confirmed the size-dependent apoptotic phenomena in osteosarcoma cells under treatment of gold nanoparticles. Study demonstrates a facile, non-active targeting approach for detection of size-dependent AuNP-induced apoptosis in osteosarcoma cells through label-free SERS method.

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors.

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

An Empirically Derived Pediatric Cardiac Inotrope Score Associated With Pediatric Heart Surgery.

We aimed to empirically derive an inotrope score to predict real-time outcomes using the doses of inotropes after pediatric cardiac surgery. The outcomes evaluated included in-hospital mortality, prolonged hospital length of stay, and composite poor outcome (mortality or prolonged hospital length of stay). The study population included patients <18 years of age undergoing heart operations (with or without cardiopulmonary bypass) of varying complexity. To create this novel pediatric cardiac inotrope score (PCIS), we collected the data on the highest doses of 4 commonly used inotropes (epinephrine, norepinephrine, dopamine, and milrinone) in the first 24 hours after heart operation. We employed a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the dosage of drugs for a particular patient. We used Bayesian conditional probit regression to model the effects of the inotropes on the mean of the latent variables. We then used Markov chain Monte Carlo simulations for simulating posterior samples to create a score function for each of the study outcomes. The training dataset utilized 1030 patients to make the scientific model. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/InotropeScoreApp. The newly proposed empiric PCIS demonstrated a high degree of discrimination for predicting study outcomes in children undergoing heart operations. The newly proposed empiric PCIS provides a novel measure to predict real-time outcomes using the doses of inotropes among children undergoing heart operations of varying complexity.

A prognostic tool to predict outcomes in children undergoing the Norwood operation.

OBJECTIVES: To create and validate a prediction model to assess outcomes associated with the Norwood operation. METHODS: The public-use dataset from a multicenter, prospective, randomized single-ventricle reconstruction trial was used to create this novel prediction tool. A Bayesian lasso logistic regression model was used for variable selection. We used a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the risk factors for a particular patient. Bayesian conditional probit regression and Markov chain Monte Carlo simulations were then used to estimate the effects of the predictors on the means of these latent variables to create a score function for each of the study outcomes. We also devised a method to calculate the risk of outcomes associated with the Norwood operation before the actual heart operation. The 2 study outcomes evaluated were in-hospital mortality and composite poor outcome. RESULTS: The training dataset used 520 patients to generate the prediction model. The model included patient demographics, baseline characteristics, cardiac diagnosis, operation details, site volume, and surgeon experience. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/NorwoodScoreApp/. Model validation was performed on 520 observations using an internal 10-fold cross-validation approach. The prediction model had an area under the curve of 0.77 for mortality and 0.72 for composite poor outcome on the validation dataset. CONCLUSIONS: Our new prognostic tool is a promising first step in creating real-time risk stratification in children undergoing a Norwood operation; this tool will be beneficial for the purposes of benchmarking, family counseling, and research.

Imputation of confidential data sets with spatial locations using disease mapping models.

Data that include fine geographic information, such as census tract or street block identifiers, can be difficult to release as public use files. Fine geography provides information that ill-intentioned data users can use to identify individuals. We propose to release data with simulated geographies, so as to enable spatial analyses while reducing disclosure risks. We fit disease mapping models that predict areal-level counts from attributes in the file and sample new locations based on the estimated models. We illustrate this approach using data on causes of death in North Carolina, including evaluations of the disclosure risks and analytic validity that can result from releasing synthetic geographies.