A tale of recurrent secretory carcinoma of submandibular gland in an adolescent male: A rare presentation of a rare tumor.

Mammary analogue secretory carcinoma (MASC) is a rare salivary gland tumor which shares its histologic, immunohistochemical, and genetic features with the secretory carcinoma (SC) of breast. In this case report, we describe a case of MASC in a young adolescent male with swelling in the right angle of mandible which is a relatively rare site to present along with its correlation of cytological, histological, and immunohistochemical features. A 16-year-old male came with the complaint of swelling in the right angle of mandible since 2 years. Contrast-enhanced computed tomography (CECT) neck revealed differential diagnosis of nerve sheath tumor, pleomorphic adenoma, and adenoid cystic neoplasm was kept, and subsequently fine-needle aspiration cytology (FNAC) was done. FNAC was done in which differential diagnosis of myoepithelial neoplasm, acinic cell carcinoma, and SC was given. Surgical excision was done followed by histopathological examination. Immunohistochemistry panel was also applied, and final diagnosis of SC was rendered. SC has distinct cytological, histological, and immunohistochemical features which should be recognized by the pathologists for the appropriate management of the patient.

Pseudomyxoma peritonei leading to "jelly belly" abdomen: a case report and review of the literature.

BACKGROUND: Pseudomyxoma peritonei is an infrequent condition with a global annual incidence of only one to two cases per million people. Mucinous neoplasms, widespread intraperitoneal implants, and mucinous ascites characterize it. Currently, most clinicians misdiagnose this condition, which leads to delayed management. CASE PRESENTATION: A 44-year-old North Indian female presented with a 1.5-month history of an abdominal lump. Physical examination revealed a sizeable abdominopelvic mass at 36 weeks. Contrast-enhanced computed tomography showed a massive multiloculated right ovarian cystic mass measuring 28 × 23 × 13 cm with mild ascites and elevated carcinoembryonic antigen levels (113.75 ng/ml). A provisional diagnosis of ovarian mucinous neoplasm was made, for which the patient underwent laparotomy. Intraoperatively, there were gross mucinous ascites, along with a large, circumscribed, ruptured right ovarian tumor filled with gelatinous material. The appendicular lump was also filled with mucinous material along with the omentum, ascending colon, right lateral aspect of the rectum, splenic surface, and small bowel mesentery. Cytoreductive surgery was performed along with an oncosurgeon, including total abdominal hysterectomy with bilateral salpingoophorectomy, omentectomy, right hemicolectomy, lower anterior resection, ileo-transverse stapled anastomosis with proximal ileal loop diversion stoma, excision of multiple peritoneal gelatinous implants, and peritoneal lavage. Histopathology and immunohistochemistry confirmed the presence of intestinal-type mucinous carcinoma. Postoperatively, the patient was given six cycles of chemotherapy. She tolerated it without any specific morbidity and had an uneventful recovery. Postoperative follow-up at 15 months revealed normal tumor marker levels and abdominal computed tomography findings and no signs suggestive of local recurrence or distal metastases. CONCLUSIONS: Pseudomyxoma peritonei is a rare disease that is frequently misdiagnosed in the preoperative phase. Therefore, radiologists and clinicians should maintain a high index of suspicion for accurate diagnosis and multidisciplinary management.

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system's efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

A single-center review of outcomes between COVID-19 vaccinated and unvaccinated liver transplant recipients.

BACKGROUND: With the availability of vaccines against SARS-COV-2, recommendations for vaccination of transplant candidates are widespread. At our institution, patients may receive liver transplant (LTx) regardless of vaccine status. The purpose of this study is to compare post-LTx outcomes between vaccinated (VAX) and unvaccinated (UNVAX) LTx recipients. METHODS: This is a retrospective, single-center study of LTx from January 1, 2021-March 30, 2022. The primary outcome is incidence of post-LTx COVID-19. Secondary outcomes include graft function, mortality, graft loss, and COVID-19 treatment. RESULTS: One hundred and seventy-seven LTx recipients were included, 57% [101/177] VAX and 43% [76/177] UNVAX. Baseline characteristics were similar between groups. Overall, 28 (36.8%) UNVAX and 34 (33.7%) VAX tested COVID-19 positive during the study period (p = .193) at a mean of 312.6 [255.4-369.8] days for UNVAX versus 254.6 [215.2-293.9] days for VAX (p = .084). COVID-19 treatment was administered in 15 (53.6%) of the UNVAX compared to 22 (64.7%) in the VAX (p = .374), although eight (28.6%) of UNVAX required hospital admission for treatment compared with two (5.9%) of VAX (p = .016). There were no statistically significant differences in death, and no COVID-19 related death or graft loss. There were no statistically significant differences in liver function tests at 3- and 12-months post LTx. CONCLUSION: In a series with a large percentage of UNVAX patients, LTx appears to be safe, with no difference in the rate of COVID-19 or transplant-related outcomes compared to VAX. While we encourage vaccination to prevent severe COVID, based on our results, vaccine status should not be reason to deny lifesaving transplant.

Comparison of outcomes in vaccinated versus unvaccinated COVID-19 kidney transplant recipients, a single center retrospective study-Is the taboo justified?

INTRODUCTION: We assessed differences in the post-transplant outcomes between COVID-19 vaccinated and unvaccinated Kidney transplant (KTx) recipients. METHODS: We conducted a retrospective, single-center study of 400 KTx from 2/1/2021 to 4/30/2022 with 6-21 months follow-up. Primary outcomes included differences in the incidence of post-transplant COVID-19, ICU admission for COVID-19, death, and graft failure between the two groups. Secondary outcomes were inpatient floor admission, outpatient-management, length of hospital stay during COVID-19 admission. We also reported rejection, DGF, CMV needing treatment, and BK PCR >10 000 in baseline characteristics. RESULT: 70.5% (282/400) were fully vaccinated, and 29.5% (118/400) were unvaccinated. 33% (92/282) of vaccinated and 39% (46/118) of unvaccinated patients developed COVID-19 (p-value .03). In both groups, 16% received outpatient treatments for COVID-19. 3% (12/282) of the vaccinated and 8% (11/118) unvaccinated were admitted to the general floors (p-value .06), and 1% (3/282) of the vaccinated and 3.3% (4/118) of the unvaccinated patients needed admission to the ICU (p-value .2). The length of stay was 12 days in both groups. 13/282 (4.6%) vaccinated patients and 7/118 (5.93%) unvaccinated patients died during the follow-up period (p-value = .3). COVID-19 was deemed the etiology of death in 5/13 cases in the vaccinated and 3/7 in the unvaccinated. DGF, rejection, CMV requiring treatment, and BK PCR >10 000 were comparable between groups. CONCLUSION: The incidence of COVID-19 was higher in unvaccinated than in vaccinated KTx. The two groups were not statistically different for other primary outcomes, including the need for hospital admissions (outpatient, general floor, ICU), length of hospital stay, death, and graft failure.

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.

DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback-Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.

Outcomes After Deceased Donor Kidney Transplantation Using Kidney Allografts With Marginal Perfusion Parameters.

BACKGROUND: This study examines outcomes of deceased donor kidney transplantation (DDKT) in recipients of kidney allografts with marginal perfusion parameters. METHODS: Allografts with marginal perfusion parameters (resistance index [RI] >0.4 and pump flow rate [F] <70 mL/min; MP group) were compared with those with good parameters (RI <0.4 and F >70 mL/min; GP group) for DDKT recipients between January 1996 and November 2017 after hypothermic pulsatile perfusion. Demographics, creatinine, cold ischemia times (CIT), delayed graft function (DGF), and recipient glomerular filtration rate at pre- and post-transplant were noted. The primary outcome was graft survival post-transplant. RESULTS: In the MP (n = 31) versus GP (n = 1281) group, the median recipient was aged 57 years versus 51 years; the median donor was aged 47 versus 37 years; terminal creatinine was 0.9 versus 0.9 mg/dL; CIT was 10.2 versus 13 hours, and the RI and flow were 0.46 and 60 mL/min versus 0.21 and 120 mL/min. The DGF rate was 19% (MP) versus 8% (GP). The graft survival in the MP versus GP group was 81% versus 90% (1 year), 65% versus 79% (3 years), 65% versus 73% (4 years), and 45% versus 68% (5 years). CONCLUSION: Carefully selected kidney allografts after comprehensive donor and recipient evaluation may allow for the use of these routinely discarded kidneys with marginal perfusion parameters.

Cytological and histopathological features of recurrent calcifying aponeurotic fibroma of neck in an adolescent male-rare presentation of a rare tumour.

Calcifying aponeurotic fibroma is a rare benign but locally aggressive soft tissue tumour. It is most commonly seen in distal extremities and very rarely seen in head and neck region. In this case report, we describe both cytological and histological features of this tumour in a young adolescent male.

Efficient Evolving Deep Ensemble Medical Image Captioning Network.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

Comparative performance assessment of deep learning based image steganography techniques.

Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study.

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

Artificial Intelligence-Based Cyber-Physical System for Severity Classification of Chikungunya Disease.

BACKGROUND: Artificial intelligence techniques are widely used in solving medical problems. Recently, researchers have used various deep learning techniques for the severity classification of Chikungunya disease. But these techniques suffer from overfitting and hyper-parameters tuning problems. METHODS: In this paper, an artificial intelligence-based cyber-physical system (CPS) is proposed for the severity classification of Chikungunya disease. In CPS system, the physical components are integrated with computational algorithms to provide better results. Random forest (RF) is used to design the severity classification model for Chikungunya disease. However, RF suffers from overfitting and poor computational speed problems due to complex architectures and large amounts of connection weights. Therefore, an evolving RF model is proposed using the adaptive crossover-based genetic algorithm (ACGA). RESULTS: ACGA can efficiently optimize the architecture of RF to achieve better results with better computational speed. Extensive experiments are performed by utilizing the Chikungunya disease dataset. CONCLUSION: Performance analysis demonstrates that ACGA-RF achieves higher performance as compared to the competitive models in terms of F-measure, accuracy, sensitivity, and specificity. The proposed CPS system can prevent users from visiting hospitals and can render services to patients living far away from hospitals.