Oral Wound Healing: A Scoping Review and Proposal of a New Index for Palatal Mucosa.

Purpose: The aim of this scoping review was to evaluate the wound healing indices available in literature and propose a new intra-oral wound healing index to assess the healing of palatal mucosa. Materials and Methods: A PubMed database search was conducted to identify relevant studies using the search strategy: ('Oral Wound healing') OR ('Palatal tissue healing') OR ('Healing indices in Oral and Maxillofacial Surgery') OR ('Palatal wound healing') OR ('Complications in wound healing'). A qualitative and quantitative synthesis of the results was done and data was presented following the PRISMA-ScR guidelines. Results: The search resulted in 9 articles published between 2019 and 2022, which were eligible for inclusion in the study. The data revealed that the indices currently available for the assessment of intra-oral healing were limited and primarily concerned with the assessment of gingival and periodontal tissues. Conclusion: The healing indices devised for gingival and periodontal tissues cannot be applied to palatal healing due to the differences in clinical and histological aspects. Therefore, a new index to monitor the healing response specifically for the soft tissues in the palate has been proposed. This maybe particularly useful in cleft palate repair and other procedures performed over the palatal tissues.

Signet cell rectal carcinoma with prostatic involvement detected by FAPI-04 PET-MRI fusion.

A 60-year-old male patient diagnosed with mucinous adenocarcinoma of lower third of rectum underwent abdominoperineal resection and permanent colostomy followed by adjuvant chemotherapy. Response evaluation with F-18 FDG PET-CT showed a complete metabolic response. After 6 months, CEA levels started increasing and clinically a recurrence was suspected. A restaging FDG PET-CT showed no obvious malignant disease. Patient presented again within a month with complaints of urinary retention and haematuria. CEA levels were further elevated, and Ga-68 FAPI-04 (FAPI) PET-CT was performed. FAPI PET-CT revealed prostatic and seminal vesicle disease involvement. Additionally, an MRI of pelvis was done and fused with FAPI PET for confirmation of prostatic involvement.

Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.

Ovarian cancer is a serious sickness for elderly women. According to data, it is the seventh leading cause of death in women as well as the fifth most frequent disease worldwide. Many researchers classified ovarian cancer using Artificial Neural Networks (ANNs). Doctors consider classification accuracy to be an important aspect of making decisions. Doctors consider improved classification accuracy for providing proper treatment. Early and precise diagnosis lowers mortality rates and saves lives. On basis of ROI (region of interest) segmentation, this research presents a novel annotated ovarian image classification utilizing FaRe-ConvNN (rapid region-based Convolutional neural network). The input photos were divided into three categories: epithelial, germ, and stroma cells. This image is segmented as well as preprocessed. After that, FaRe-ConvNN is used to perform the annotation procedure. For region-based classification, the method compares manually annotated features as well as trained feature in FaRe-ConvNN. This will aid in the analysis of higher accuracy in disease identification, as human annotation has lesser accuracy in previous studies; therefore, this effort will empirically prove that ML classification will provide higher accuracy. Classification is done using a combination of SVC and Gaussian NB classifiers after the region-based training in FaRe-ConvNN. The ensemble technique was employed in feature classification due to better data indexing. To diagnose ovarian cancer, the simulation provides an accurate portion of the input image. FaRe-ConvNN has a precision value of more than 95%, SVC has a precision value of 95.96%, and Gaussian NB has a precision value of 97.7%, with FR-CNN enhancing precision in Gaussian NB. For recall/sensitivity, SVC is 94.31 percent and Gaussian NB is 97.7 percent, while for specificity, SVC is 97.39 percent and Gaussian NB is 98.69 percent using FaRe-ConvNN.

Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network.

Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit's surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%.

Analytical and Experimental Study on Cold-Formed Steel Built-Up Sections for Bending.

In the construction of steel structures, the two most common types of structural members are hot-formed and cold-formed members. This paper mainly describes the analytical and experimental research on the strength and characteristics of CFS bolted built-up sigma sections having different structural arrangements under bending. The cross-sectional dimensions for the parametric study were selected by the sizes available in the market. In this paper, ANSYS workbench software was used to perform FE modeling and observe the local, flexural, and interaction of these buckling. Then, experimental study was performed by varying the arrangement of open section beams between face-to-face and back-to-back, connected using bolts or fasteners different spacings. Further, we conducted bending tests on cold-formed steel built-up members having simple edge stiffeners in the middle. Comparing both analytical and experimental studies, the results indicate that the back-to-back connected built-up beam section provides a flexural capacity higher than the face-to-face built-up section. Moreover, increasing the bolt spacing enhanced the load-carrying capacity of back-to-back sigma section built-up beams. It has also been discovered that the flexural strength of beams is primarily determined by bolt spacing or itsposition.

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis.

BACKGROUND AND OBJECTIVES: The prediction of multiple drug efficacies using machine learning prediction techniques based on clinical and molecular attributes of tumors is a new approach in the field of precision medicine of oncology. The selection of suitable and effective therapeutic drugs among the potential drugs is performed computationally considering the tumor features. In this study, we developed and validated machine learning models to predict the efficacy of five anti-cancer drugs according to the clinical and molecular attributes of 30 oral squamous cell carcinoma (OSCC) cohorts. This sounds a bit odd - consider: Ranking of the drugs was achieved using their apoptotic priming. METHODS: We developed multiple drug efficacy prediction models based on three types of tumor characteristics by applying machine learning methods, including multi-target regression (MTR) and support vector regression (SVR). The prediction accuracy of existing machine learning methods was enhanced by introducing novel pre-processing techniques to develop Enhanced MTR (E_MTR), Enhanced Log-based MTR (EL_MTR), Enhanced Multi-target SVR (EM_SVR), and Enhanced Log-based Multi-target SVR (ELM_SVR). As a unique capability, ELM_SVR and EL_MTR rank the drugs based on their predicted efficacy. All the drug efficacy prediction models were built using OSCC real samples and theoretical samples. The best model was selected was based on dataset size and evaluation metrics, such as error terms, residuals and parameter tuning, and cross-validated (CV) using 30 real samples and 340 theoretical samples. RESULTS: When 30 real tumor samples were used for the train-test and CV methods, MTR models predicted the efficacy with less error than SVR models. Comparatively, using 340 theoretical samples for the train-test and CV methods, though MTR improved the performance, SVR predicted the efficacy with zero error. We found that, for small samples, the proposed MTR provided a 0.01 difference between actual apoptotic priming and predicted priming of five drugs. For large samples, the predicted values by the proposed SVR had a difference of 0.00001. The error terms (Actual vs. Predicted) also reveal that the enhanced log model is suitable when MTR is applied. Meanwhile, the enhanced model is suitable for SVR learning for multiple drug efficacy prediction. It was found that the predicted ranks of the drugs based on the multi-targeted efficacy prediction exactly match the actual rankings. CONCLUSION: We developed efficient statistical and machine learning models using MTR and SVR analysis for anticancer drug efficacy, which will be useful in the field of precision medicine to choose the most suitable drugs in personalized manner. The performance results of the proposed enhanced ranking techniques are described as follows: i) EL_MTR is the best to predict multiple anticancer drug efficacies and improve the accuracy of ranking drugs, irrespective of sample size; and ii) ELM_SVR performs better than other MTR models with a large sample size and precise ranking process.

Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier.

The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.

Influential role of 7-Ketocholesterol in the progression of Alzheimer's disease.

Millions of people are affected by neurodegenerative diseases worldwide. They occur due to the loss of brain functions or peripheral nervous system dysfunction. If untreated, prolonged condition ultimately leads to death. Mostly they are associated with stress, altered cholesterol metabolism, inflammation and organelle dysfunction. Endogenous cholesterol and phospholipids in brain undergo auto-oxidation by enzymatic as well as non-enzymatic modes leading to the formation of by-products such as 4-hydroxynonenal and oxysterols. Among various oxysterols, 7-ketocholesterol (7KCh) is one of the major toxic components involved in altering neuronal lipid metabolism, contributing to inflammation and nerve cell damage. More evidently 7KCh is proven to induce oxidative stress and affects membrane permeability. Loss in mitochondrial membrane potential affects metabolism of cell organelles such as lysosomes and peroxisomes which are involved in lipid and protein homeostasis. This in turn could affect amyloidogenesis, tau protein phosphorylation and accumulation in pathological conditions of neurodegenerative diseases. Lipid alterations and the consequent pathogenic protein accumulation, results in the damage of cell organelles and microglial cells. This could be a reason behind disease progression and predominantly reported characteristics of neurodegenerative disorders such as Alzheimer's disease. This review focuses on the role of 7KCh mediated neurodegenerative Alzheimer's disease with emphasis on alterations in the lipid raft microdomain. In addition, current trends in the significant therapies related to 7KCh inhibition are highlighted.

Optimized Attribute Selection Using Artificial Plant (AP) Algorithm with ESVM Classifier (AP-ESVM) and Improved Singular Value Decomposition (ISVD)-Based Dimensionality Reduction for Large Micro-array Biological Data.

In the tremendous field of the bioinformatics look into, enormous volume of genetic information has been produced. Higher throughput gadgets are made accessible at lower cost made the age of Big data. In a time of developing information multifaceted nature and volume and the approach of huge information, feature selection has a key task to carry out in decreasing high dimensionality in AI issues. Dealing with such huge data has turned out to be incredibly testing strategy for choosing the exact features in enormous medical databases. Large clinical data frequently comprise of an enormous number of identifiers of the disease. Data mining when applied to clinical data for identification of diseases, a few identifiers are will not be much useful and sometimes may even have negative impacts. Consequently, when the FS is applied, it is vital as it can expel those insignificant disease identifiers. It likewise builds the adequacy of decision by a physician emotionally supportive network by viably diminishing the time of learning of the framework. In this paper, a unique approach is presented for the feature selection utilizing the Artificial Plant algorithm which uses the Enhanced Support Vector Machine classifier. The features got are additionally dimensionally decreased by presenting the Improved Singular Value Decomposition strategy; finally, enhancement is done by the outstanding BAT streamlining method. The examinations are completed with real-time large cervical cancer data and it demonstrated to be more effective than the current methods.

An overview on human serum lectins.

An extensive literature survey done on the various naturally occurring lectins in human serum upon its salient features such as methods of detection, level and sites of synthesis, binding specificity, cation dependency, modes of isolation, molecular and functional characterization way back from 1930s to till date was presented in a tabulated section. In addition, the generation of lectin and other immune molecules in vertebrates upon treatment with exogenous elicitors has also been framed in a tabular form. Furthermore, ANEW lectin induced in human serum for the very first time by an exogenous elicitor was detected, isolated and characterized by us whose features are also tabulated explicitly.

Eosinophilic Leukemoid Reaction with Eosinophilic Tumor Tissue Infiltration as an Extermely Poor Prognostic Factor in Urinary Bladder Cancer- a Known Entity Revisited.

Eosinophilia can be a manifestation of a variety of causes such as infections, allergic reactions and autoimmune processes. Also, it is described in various solid malignancies in the presence of tumour eosinophilic infiltration. We report a patient of high-grade urinary bladder cancer with eosinophilic leukemoid reaction and tumour histopathology demonstrated diffuse infiltration of eosinophils. Though the entity is described to carry a good prognosis in literature, our experience is totally different as the patient deteriorated rapidly in a matter of days, was deemed inoperable in view of worsening performance status and was referred for palliative management.

A Comparative Study of Compressible and Conductive Vertically Aligned Carbon Nanotube Forest in Different Polymer Matrixes for High-Performance Piezoresistive Force Sensors.

In the present scenario, conducting and lightweight flexible polymer nanocomposites rival metallic and inorganic semiconducting materials as highly sensitive piezoresistive force sensors. Herein, we explore the feasibility of vertically aligned carbon nanotube (VACNT) nanocomposites impregnated in different polymer matrixes, envisioned as highly efficient piezoresistors in sensor applications. Polymer nanocomposites are selectively designed and fabricated using three different polymer matrixes, i.e., polydimethylsiloxane (PDMS), polyurethane (PU), and epoxy resins with ideal reinforcement of VACNTs to enhance the thermal stability, conductivity, compressibility, piezoresistivity, and sensitivity of these nanocomposites. To predict the best piezoresistive force sensor, we evaluated the structural, optical, thermal, electrical, mechanical, and piezoresistive properties of the nanocomposites using field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), I-V measurements, compressive stress-strain measurements, hysteresis, sensitivity, and force studies. The results demonstrate that the PDMS/VACNT nanocomposite is capable of sustaining large force with almost complete recovery and enhanced sensitivity, thereby fulfilling the desirable need for a highly efficient conductive and flexible force sensor as compared to PU/VACNT and epoxy/VACNT nanocomposites.

Prospective audit of complications after ultrasonography-guided percutaneous nephrostomy for upper urinary tract obstruction using modified Clavien classification system.

INTRODUCTION: Percutaneous nephrostomy (PCN) is a commonly performed intervention in urology for various benign and malignant conditions causing upper urinary tract obstruction. We present a prospective audit of complications of ultrasonography (USG) guided PCN using modified Clavien classification system (mCCS). METHODS: The data were prospectively collected for 368 PCN performed in 344 patients from June 2015 to January 2017, for various benign and malignant diseases causing upper urinary tract obstruction. Patients were followed for 1 month, and complications arisen of PCN were noted. RESULTS: PCN was successful in 356 renal units. The 12 patients in which PCN failed was due to minimal pelvicalyceal dilatation and PCN was successfully performed after 48 h by a senior urologist. 207 patients had malignant disease and 161 patients had benign condition. Most common malignant disease was carcinoma cervix. 238 were noninfected while 130 had infected renal units. 62 (16.84%) patients had Grade I (self-limiting hematuria/cot/debris/fever). 37 (10.0%) patients had Grade II (7 - transfusion and 30 - urinary tract infection). 34 (9.2%) had Grade III a (repositioning/change/reinsertion of PCN tube under local anesthesia) and 4 (1.1%) had Grade III b (repositioning under anesthesia). 8 (2.2%) Grade IV a (Sepsis), 0 Grade IV b, and 0 Grade V complications were observed. CONCLUSION: USG-guided PCN is a safe, minimally invasive, and effective procedure for upper urinary tract diversion with a low rate of morbidity. Individual complications are within the threshold limits set by the American College of Radiology, the Society of Interventional Radiology. mCCS is well applicable and easily reproducible tool for reporting the complications of PCN.

Luminescent chitosan/carbon dots as an effective nano-drug carrier for neurodegenerative diseases.

Designing new materials for effective and targeted drug delivery is pivotal in biomedical research. Herein, we report on the development of a chitosan/carbon dot-based nanocomposite and investigate its efficacy as a carrier for the sustained release of dopamine drug. The carbon dots (CDs) were synthesized from the carbonization of chitosan and were further conjugated with chitosan (CS) to obtain a chitosan/carbon dot (CS/CD) matrix. Dopamine was later encapsulated in the matrix to form a dopamine@CS/CD nanocomposite. The cytotoxicity of IC-21 and SH-SY5Y cell lines was studied at various concentrations of the nanocomposite and the results demonstrate around 97% cell viability. The photoluminescence property revealed the characteristic property of the carbon dots. When excited at 510 nm an emission peak was observed at 550 nm which enables the use of carbon dots as a tracer for bioimaging. The HRTEM images and the D, G, and 2D bands of the Raman spectra confirm the successful synthesis of carbon dots and through DLS the particle size is estimated to be ∼3 nm. The release studies of the encapsulated drug from the composite were analyzed in an in vitro medium at different pH levels. The novelty of this method is the use of a non-toxic vehicle to administer drugs effectively towards any ailment and in particular, the carbon dots facilitate the consistent release of dopamine towards neurodegenerative diseases and tracing delivery through bioimaging.

Predictive Analysis on Behavioural Risk Factor Surveillance System through Ensemble Classifiers and Clusters.

INTRODUCTION: The World Health Organization (WHO) has been asserting the importance of health care in today's world. The objective of this research is to find out the type of medication that needs to be provided to the people at early stages to prevent behavioural risk factors. The health department of United States has a great vision to improve the immune system of the people and has taken measures to do the same through a Behavioural Risk Factor Surveillance System (BRFSS). This research aims to prevent behavioural risk factors by predictive analysis using the above mentioned dataset from the Centres for Disease Control and Prevention (CDC). METHODOLOGY: The methods of ensemble classification and clustering is applied on the dataset, pre and post weighted classification, thereby classifying and prescribing the type of healthcare required for people exhibiting behaviours such as obesity, nutrition and physical activity. RESULTS AND DISCUSSION: This analyses help improve the quality of health of the citizens. In an extensive study, it was observed that the result obtained was 92.87% accurate.

Role of extra oral monofocal distractor device in the correction of the facial asymmetry, sleep apnoea, and quality of life associated with TMJ ankylosis.

OBJECTIVES: The purpose of this study is to evaluate the role of primary osteo-distraction prior to ankylosis release in patients, diagnosed with sleep apnoea, facial asymmetry, and reduced quality of life secondary to temporomandibular joint (TMJ) ankylosis. METHODS: Ten patients in the age group of 13-40 years with TMJ ankylosis underwent primary osteo-distraction for mandibular advancement. They were evaluated pre- and post-operatively using radiographs, various questionnaires, and subjective evaluation of facial asymmetry, sleep apnoea, and quality of life (QOL). RESULTS: All the ten patients showed significant improvement in their sleep apnoea symptoms with a mean of 6.20 ± 1.39 (P < 0.05). The mean advancement of the mandible in all the ten patients (both bilateral and unilateral ankylosis) was 15.8 mm (P < 0.05). The quality of life showed marked improvement from very poor to very satisfactory (P < 0.001). CONCLUSION: Primary mandibular distraction is an effective method of correction of facial asymmetry, sleep apnoea, and quality of life in patients with TMJ ankylosis.

Cefotaxime induced generalized bullous fixed drug eruption - A case report.

Fixed drug eruption (FDE) is the most common cutaneous adverse drug reaction. Cefotaxime, a broad-spectrum third-generation cephalosporin, appeared to be a safe and effective therapy in greater than 90% of infections including cellulitis, abscesses and necrotizing ulcers of the skin and subcutaneous tissues but here we report a rare case of 36 years old female patient developed generalized bullous FDE after intravenous administration of Cefotaxime.

On the emergence of critical regions at the onset of thermoacoustic instability in a turbulent combustor.

We use complex network theory to investigate the dynamical transition from stable operation to thermoacoustic instability via intermittency in a turbulent combustor with a bluff body stabilized flame. A spatial network is constructed, representing each of these three dynamical regimes of combustor operation, based on the correlation between time series of local velocity obtained from particle image velocimetry. Network centrality measures enable us to identify critical regions of the flow field during combustion noise, intermittency, and thermoacoustic instability. We find that during combustion noise, the bluff body wake turns out to be the critical region that determines the dynamics of the combustor. As the turbulent combustor transitions to thermoacoustic instability, during intermittency, the wake of the bluff body loses its significance in determining the flow dynamics and the region on top of the bluff body emerges as the most critical region in determining the flow dynamics during thermoacoustic instability. The knowledge about this critical region of the reactive flow field can help us devise optimal control strategies to evade thermoacoustic instability.