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.

Probing the functional consequence and clinical relevance of CD320 p.E88del, a variant in the transcobalamin receptor gene.

The biological and clinical significance of the p.E88del variant in the transcobalamin receptor, CD320, is unknown. This allele is annotated in ClinVar as likely benign, pathogenic, and of uncertain significance. To determine functional consequence and clinical relevance of this allele, we employed cell culture and genetic association studies. Fibroblasts from 16 CD320 p.E88del homozygotes exhibited reduced binding and uptake of cobalamin. Complete ascertainment of newborns with transiently elevated C3 (propionylcarnitine) in New York State demonstrated that homozygosity for CD320 p.E88del was over-represented (7/348, p < 6 × 10-5 ). Using population data, we estimate that ~85% of the p.E88del homozygotes born in the same period did not have elevated C3, suggesting that cobalamin metabolism in the majority of these infants with this genotype is unaffected. Clinical follow-up of 4/9 homozygous individuals uncovered neuropsychological findings, mostly in speech and language development. None of these nine individuals exhibited perturbation of cobalamin metabolism beyond the newborn stage even during periods of acute illness. Newborns homozygous for this allele in the absence of other factors are at low risk of requiring clinical intervention, although more studies are required to clarify the natural history of various CD320 variants across patient populations.

Cross Channel Scripting and Code Injection Attacks on Web and Cloud-Based Applications: A Comprehensive Review.

Cross channel scripting (XCS) is a common web application vulnerability, which is a variant of a cross-site scripting (XSS) attack. An XCS attack vector can be injected through network protocol and smart devices that have web interfaces such as routers, photo frames, and cameras. In this attack scenario, the network devices allow the web administrator to carry out various functions related to accessing the web content from the server. After the injection of malicious code into web interfaces, XCS attack vectors can be exploited in the client browser. In addition, scripted content can be injected into the networked devices through various protocols, such as network file system, file transfer protocol (FTP), and simple mail transfer protocol. In this paper, various computational techniques deployed at the client and server sides for XCS detection and mitigation are analyzed. Various web application scanners have been discussed along with specific features. Various computational tools and approaches with their respective characteristics are also discussed. Finally, shortcomings and future directions related to the existing computational techniques for XCS are presented.

Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images.

Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.

ECG Data Analysis with Denoising Approach and Customized CNNs.

In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart's rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.

Densely connected convolutional networks-based COVID-19 screening model.

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks.

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Phytoestrogens Modulate Breast Cancer Resistance Protein Expression and Function at the Blood-Cerebrospinal Fluid Barrier.

PURPOSE: Breast cancer resistance protein (BCRP/ABCG2) is a drug efflux transporter expressed at the blood cerebrospinal fluid barrier (BCSFB), and influences distribution of drugs into the central nervous systems (CNS). Current inhibitors have failed clinically due to neurotoxicity. Novel approaches are needed to identify new modulators to enhance CNS delivery. This study examines 18 compounds (mainly phytoestrogens) as modulators of the expression/function of BCRP in an in vitro rat choroid plexus BCSFB model. METHODS: Modulators were initially subject to cytotoxicity (MTT) assessment to determine optimal non-toxic concentrations. Reverse-transcriptase PCR and confocal microscopy were used to identify the presence of BCRP in Z310 cells. Thereafter modulation of the intracellular accumulation of the fluorescent BCRP probe substrate Hoechst 33342 (H33342), changes in protein expression of BCRP (western blotting) and the functional activity of BCRP (membrane insert model) were assessed under modulator exposure. RESULTS: A 24 hour cytotoxicity assay (0.001 µM-1000 µM) demonstrated the majority of modulators possessed a cellular viability IC50 > 148 µM. Intracellular accumulation of H33342 was significantly increased in the presence of the known BCRP inhibitor Ko143 and, following a 24 hour pre-incubation, all modulators demonstrated statistically significant increases in H33342 accumulation (P < 0.001), when compared to control and Ko143. After a 24 hour pre-incubation with modulators alone, a 0.16-2.5 -fold change in BCRP expression was observed for test compounds. The functional consequences of this were confirmed in a permeable insert model of the BCSFB which demonstrated that 17-ß-estradiol, naringin and silymarin (down-regulators) and baicalin (up-regulator) can modulate BCRP-mediated transport function at the BCSFB. CONCLUSION: We have successfully confirmed the gene and protein expression of BCRP in Z310 cells and demonstrated the potential for phytoestrogen modulators to influence the functionality of BCRP at the BCSFB and thereby potentially allowing manipulation of CNS drug disposition.

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.

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.

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.

Mdm2 facilitates the association of p53 with the proteasome.

The ubiquitin ligase Mdm2 targets the p53 tumor suppressor protein for proteasomal degradation. Mutating phosphorylation sites in the central domain of Mdm2 prevents p53 degradation, although it is still ubiquitylated, indicating that Mdm2 has a post-ubiquitylation function for p53 degradation. We show that Mdm2 associates with several subunits of the 19S proteasome regulatory particle in a ubiquitylation-independent manner. Mdm2 furthermore promotes the formation of a ternary complex of itself, p53, and the proteasome. Replacing phosphorylation sites within the central domain with alanines reduced the formation of the ternary complex. The C-terminus of Mdm2 was sufficient for interaction with the proteasome despite an additional proteasome binding site in the Mdm2 N-terminus. In addition to binding to the proteasome, the C-terminus of Mdm2 bound to the central domain, possibly competing with, and therefore blocking, Mdm2/proteasome interaction. We propose that Mdm2 facilitates, or at least enhances, the association of p53 with the proteasome and that phosphorylation of the central domain of Mdm2 regulates this process.

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.

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.

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.

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.

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.