Cardiovascular and Thyroid Late Effects in Pediatric Patients With Hodgkin Lymphoma Treated With ABVD Protocol.

BACKGROUND: Hodgkin lymphoma (HL) survivors are at risk of developing a range of therapy-related complications. The goal of this study is to investigate therapy-related late-effects in HL survivors. MATERIALS AND METHODS: We performed a cross-sectional study on 208 HL survivors who were treated at the National Cancer Institute or at the Children Cancer Hospital Egypt with doxorubicin, bleomycin, vinblastine, and dacarbazine chemotherapy. RESULTS: Age at diagnosis ranged from 2.5 to 17.5 with a median of 8.7 years. The cumulative incidence of cardiac toxicity at 5 and 9 years were 18.7%±2.7% and 43.3%±4.4%, respectively. Preexisting cardiac abnormalities, cumulative anthracycline dose, and end of treatment cardiac status are strong predictors of late cardiotoxicity. Hypertension was observed in ~31% of patients. Young age and obesity at the time of treatment are important risk factors for hypertension. Thyroid abnormalities developed with a 5-year cumulative incidence of 2%±1%, whereas at 9 years the cumulative incidence was 27.9%±4.5%. Thyroid dysfunction was observed in 21.2% and thyroid tumors in 1.6% of cases. Subclinical hypothyroidism was the most common thyroid abnormality. CONCLUSIONS: Cardiotoxicity, hypertension, and thyroid dysfunction are frequent late effects after doxorubicin, bleomycin, vinblastine, and dacarbazine regimen, especially if combined with radiation therapy.

Conditional Most-Correlated Distribution-Based Load-Balancing Scheme for Hybrid LiFi/WiGig Network.

A hybrid network has recently been proposed as a framework for a high-speed wireless communication network. Basically, it integrates light fidelity (LiFi) with radio frequency wireless gigabit alliance (WiGig) networks that operate, simultaneously, in a completely different frequency band. To assign the best access point (AP) and provide enough resources for each user, an effective load-balancing (LB) strategy is needed. However, the traditional LB strategies involve sophisticated iterative computing procedures whenever the user distribution changes. Hence, the first contribution of this work is to offer a more adaptable, two-step, conditional, and most-correlated distribution (CMCD) algorithm. Thus, the low-complexity most-correlated distribution (MCD) LB scheme is applied, and the average data rates for all users are then calculated. If the results achieve the predefined performance threshold (PDT), the decisions will be confirmed; otherwise, the proposed scheme automatically switches to the more accurate, but more complex, consecutive assign WiGig first separate optimization algorithms (CAWFS) algorithm. The suggested algorithm provides a clear performance-complexity trade-off, which could be simply controlled by choosing the suitable performance tolerance factor. The second contribution of this paper is the correlation-weighted majority voting (CWMV) method, which attempts to benefit from as many prior decision votes as possible, instead of relying just on one vote. In the CWMV technique, the weight of each vote is calculated based on the correlation between the history distribution vectors and the new user distribution vector. A significant increase in the system performance is evident from the simulation results.

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas.

A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

Study on prevalence and bacterial etiology of mastitis, and effects of subclinical mastitis and stage of lactation on SCC in dairy goats in Egypt.

In Egypt, inadequate information on prevalence and epidemiology of caprine mastitis is available. This study was designed to investigate prevalence and etiological agents of caprine mastitis and assess the efficacy of somatic cell count (SCC) as marker of subclinical mastitis (SCM) in dairy goats. This study was carried out on 249 randomly selected lactating goats in different lactation stages and examined clinically. Of these animals, 477 milk samples were aseptically collected and screened for bacterial carriage. SCC was assessed in 234 apparently normal milk samples, and SCC ≥ 106 cells/ml was indicator for SCM. Prevalence of clinical mastitis (CM) was 33.73% and 16.87% at animal and udder-half levels, respectively. SCM was 52.56% in the apparently healthy halves. Culture results proved single infection in 49.69% of samples, mixed infection in 23.9% of samples, and 26.41% of samples were negative. Coagulase negative staphylococci (CNS) were the most predominant bacteria (58.75%), then Staphylococcus aureus (S. aureus) (24.375%), and Streptococci (1.875%) were the least. No significant difference was recorded between mean of SCC in bacteriologically positive and negative samples, neither in those with SCC ≤ 106 nor with SCC ≥ 106 cells/ml both in middle and late lactation stages. Besides, the percentage of animals harboring SCC ≥ 106 cells/ml and negative for bacteriology in late lactation stage was 3 times (28.57%) more than in midlactation (9.3%). We can assume that SCC is not proper indicator for intra-mammary inflammation (IMI) in goats, and bacteriological examination remains more efficient, despites being time consuming and expensive.

Energy and Spectrally Efficient Modulation Scheme for IoT Applications.

Due to the Internet of Things (IoT) requirements for a high-density network with low-cost and low-power physical (PHY) layer design, the low-power budget transceiver systems have drawn momentous attention lately owing to their superior performance enhancement in both energy efficiency and hardware complexity reduction. As the power budget of the classical transceivers is envisioned by using inefficient linear power amplifiers (PAs) at the transmitter (TX) side and by applying high-resolution analog to digital converters (ADCs) at the receiver (RX) side, the transceiver architectures with low-cost PHY layer design (i.e., nonlinear PA at the TX and one-bit ADC at the RX) are mandated to cope with the vast IoT applications. Therefore, in this paper, we propose the orthogonal shaping pulses minimum shift keying (OSP-MSK) as a multiple-input multiple-output (MIMO) modulation/demodulation scheme in order to design the low-cost transceiver architectures associated with the IoT devices. The OSP-MSK fulfills a low-power budget by using constant envelope modulation (CEM) techniques at the TX side, and by applying a low-resolution one-bit ADC at the RX side. Furthermore, the OSP-MSK provides a higher spectral efficiency compared to the recently introduced MIMO-CEM with the one-bit ADC. In this context, the orthogonality between the in-phase and quadrature-phase components of the OSP are exploited to increase the number of transmitted bits per symbol (bps) without the need for extra bandwidth. The performance of the proposed scheme is investigated analytically and via Monte Carlo simulations. For the mathematical analysis, we derive closed-form expressions for assessing the average bit error rate (ABER) performance of the OSP-MSK modulation in conjunction with Rayleigh and Nakagami-m fading channels. Moreover, a closed-form expression for evaluating the power spectral density (PSD) of the proposed scheme is obtained as well. The simulation results corroborate the potency of the conducted analysis by revealing a high consistency with the obtained analytical formulas.

Comparison of toxicity following different conditioning regimens (busulfan/melphalan and carboplatin/etoposide/melphalan) for advanced stage neuroblastoma: Experience of two transplant centers.

The outcome for advanced neuroblastoma has improved with combined modality therapy: induction chemotherapy, surgery, and consolidation with high-dose chemotherapy/autologous HSCT, followed by local radiation, cisretinoic acid, and recently antibody therapy. In the United States, the most common conditioning regimen is CEM, while in Europe/Middle East, Bu/Mel has been widely used; it remains unclear which regimen has the best outcome. Assess renal, hepatic, and infectious toxicity through Day+100 in 2 different regimens. Retrospective comparison between CEM-DFCHCC Boston and Bu/Mel- CCHE-57357. Thirty-five patients, median age 4, in Boston (2007-2011) and 38 patients, median age 3, in Cairo (2009-2011). Renal toxicity; creatinine was significantly higher in CEM than Bu/Mel: 57% (median day+90) vs. 29% (median>day+100), p = 0.004. One CEM patient died from renal dialysis at day+19. Hepatic toxicity was significantly higher in CEM than Bu/Mel: 80% (median day+26) vs. 58% (median day+60), p = 0.04. In infectious complications with CEM 14%, bacteremia (n = 4) and fungemia (n = 1), 3 had culture-negative sepsis requiring vasopressors. With Bu/Mel 18%, bacteremia (n = 7), none required pressors, p = 0.4. Bu/Mel was associated with less acute hepatic and renal toxicity and thus may be preferable for preserving organ functions.

Efficacy and toxicity of plasmonic photothermal therapy (PPTT) using gold nanorods (GNRs) against mammary tumors in dogs and cats.

Plasmonic photothermal therapy (PPTT) was introduced as a promising treatment of cancer. This work was conducted to evaluate the cytotoxic effect of intratumoral (IT) injection of 75µg gold nanorods (GNRs)/kg of body weight followed by direct exposure to 2 w/cm2 near infra-red laser light for 10min on ablation of mammary tumor in 10 dogs and 6 cats. Complete blood count (CBC), liver and kidney function were checked before the start of treatment and one month after injection of GNRs. Results showed that 62.5% (10/16), 25% (4/16) and 12.5% (2/16) of treated animals showed complete remission, partial remission and no response, respectively. Tumor was relapsed in 4 cases of initially responding animals (25%). Overall survival rate was extended to 315.5±20.5days. GNRs have no toxic effect on blood profile, liver or kidney functions. In conclusion, GNRs can be safely used for treatment of mammary tumors in dogs and cats.

Enhance the physicochemical properties of the citronella oil -SLNPs to improve the insecticidal activity against Spodoptera littoralis (Boisd) (Lep. Nactuidae).

The Nano-formulation of citronella essential oil (Cymbopogon nardus (L.) and their mixtures of three adjuvants (Provecta®, Top film®, and PEG600-dioleate) were studied to enhance physico-chemical properties of the Nano-formulation and improve the insecticidal effect against Spodoptera littoralis (Boisd). Characterisation of physicochemical properties of Nano-formulation was studied by calculating droplet size, polydispersity index (PDI), and Zeta-potential parameters. The results showed that the Solid Lipid Nanoparticles (SLNs) mixtures of three adjuvant mixtures had more toxic activity and stability than the Nano-formulation alone. Before storage the acidity, alkalinity (pH), and viscosity exhibited an acidic pH value in the range (4.11-5.34), whereas after three months of storage was recorded high pH, a shift in the pH on storage can indicate the instability of active substances or product under semi field-laboratory conditions. Nano-formulation of the citronella oil mixed with the three adjuvants increased the mortality percentage of S. littoralis larvae.

Design, and dynamic evaluation of a novel photovoltaic pumping system emulation with DS1104 hardware setup: Towards innovative in green energy systems.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose's challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator's ability to faithfully reproduce genuine solar panel characteristics.

Optimal controller design for reactor core power stabilization in a pressurized water reactor: Applications of gold rush algorithm.

Nuclear energy (NE) is seen as a reliable choice for ensuring the security of the world's energy supply, and it has only lately begun to be advocated as a strategy for reducing climate change in order to meet low-carbon energy transition goals. To achieve flexible operation across a wide operating range when it participates in peak regulation in the power systems, the pressurised water reactor (PWR) NE systems must overcome the nonlinearity problem induced by the substantial variation. In light of this viewpoint, the objective of this work is to evaluate the reactor core (main component) of the NE system via different recent optimization techniques. The PWR, which is the most common form, is the reactor under investigation. For controlling the movement of control rods that correspond with reactivity for power regulation the PWR, PID controller is employed. This study presents a dynamic model of the PWR, which includes the reactor core, the upper and lower plenums, and the piping that connects the reactor core to the steam alternator is analyzed and investigated. The PWR dynamic model is controlled by a PID controller optimized by the gold rush optimizer (GRO) built on the integration of the time-weighted square error performance indicator. Additionally, to exhibit the efficacy of the presented GRO, the dragonfly approach, Arithmetic algorithm, and planet optimization algorithm are used to adjust the PID controller parameters. Furthermore, a comparison among the optimized PID gains with the applied algorithms shows great accuracy, efficacy, and effectiveness of the proposed GRO. MATLAB\ Simulink program is used to model and simulate the system components and the applied algorithms. The simulation findings demonstrate that the suggested optimized PID control strategy has superior efficiency and resilience in terms of less overshoot and settling time.

Progress and trends in neurological disorders research based on deep learning.

In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.

Optimization and/or acclimatization of activated sludge process under heavy metals stress.

The present study aimed to overcome the toxicity of the heavy metals load, discharged with the industrial effluents into Alexandria sewerage network, on the activated sludge treatment system through effective acclimation for organic matter and heavy metals removal. Optimization and/or acclimatization of the activated sludge process in the presence of Cu, Cd, Co and Cr contaminating mixed domestic-industrial wastewater was investigated. Acclimatization process was performed through abrupt and stepwise addition of tested metals using sequencing batch reactors treatment approach and evaluated as microbial oxygen uptake rate (OUR), dehydrogenase activity (DHA), organic matter (COD) and heavy metals removal. Abrupt addition of metals adversely affected sludge bioactivity leading to decline in the removal efficiency of the targeted contaminants and loss of floc structure. Metals IC50 confirmed that copper possessed the highest toxicity towards the OUR, DHA activity and COD removal with orders Cu > Cd > Cr > Co; Cu > Cd > Co = Cr and Cu > Cd > Cr > Co, respectively. The highest metal removal was recorded for Cd followed by Co, Cu and finally Cr, most of which was retained in the dissolved influent. However, controlled stepwise application of the tested metals exhibited high sensitivity of DHA and OUR activities only at the highest metal concentrations although enhanced at the lowest concentrations while COD removal was not significantly affected. In conclusion, this approach resulted in adaptation of the system where sludge microbes acquired and developed natural resistance to such metals leading to remarkable enhancement of both organic matter and heavy metals removal.

African Swine Fever and Its Control Measures in Wild Boar: A "De Iure Condito" Analysis in the European Union.

Recently, the African swine fever (ASF) epizootic has been reported in domestic pigs and wild boars in several European Union Member States (EU MS) and epidemiological evidence has accumulated which indicates that wild boar play a key role in maintaining and spreading the disease. Thanks to the experience gained when managing ASF outbreaks in Sardinia (Italy) and Eastern Europe, Directive 2002/60 CE was issued. This directive represented an important step forward in controlling the disease, particularly the risk of spreading the virus to wild animals. Since 2021, according to Regulation (EU) 2016/429, which is also called "Animal Health Law-AHL", when the MS competent authority suspects or confirms ASF (a cat. A listed disease) in wild animals, it is mandatory to conduct surveillance in the wild boar population and implement the necessary control measures. Within AHL, Regulations (EU) 2020/687 and 2023/594 established special ASF control measures in kept and wild porcine animals, and their products and by-products, focusing on and underlying old and new responsibilities that vets (both public and private ones) have to accomplish under the new regulations. The new change in the legal framework deals with specific measures to be applied in the wild and represents a great challenge for MS veterinary services. Some of these measures have been well established in the last two decades, particularly those related to application in the farming system, while other measures are still new to veterinary health management and require a holistic approach in terms of intensity, considering all geographical, ecological, productive, cultural and social features of the involved EU MS. In this contribution, the authors intend to focus on specific measures which have been issued in order to limit or stop the spread of ASF in a wild, "boundless" ecosystem. These measures expand the field of competence of the official veterinary service to wild areas in addition to farm activity.

Phenolics from Nigella sativa L. straw: Characterization and insecticidal activity against Agrotis ipsilon (Hüfnagel).

The excessive production of food and agro-waste has become a significant problem for society, the economy, and the environment. To meet the growing demand for food free from harmful synthetic insecticides, a recent study has investigated the potential use of an ethanolic extract obtained from the straw of Nigella sativa L., a byproduct of seed collection, as a bioinsecticide. The study also evaluated its in-vitro and in-silico acetylcholinesterase (AChE) inhibitory potential against the Agrotis ipsilon (Hufnagel) moth species, which is known to cause damage to various crops and ornamental plants. The high-performance liquid chromatography examination revealed that the ethanolic N. sativa straw extract contained 18 phenolics, including 3 simple phenols, 8 phenolic acids, and 7 flavonoids. Catechol (330.14 µg/ml), chlorogenic (169.23 µg/ml), and gallic (110.93 µg/ml) acids were the predominant phenolics. On the other hand, catechin (94.07 µg/ml), naringenin (91.99 µg/ml), and rutin (78.16 µg/ml) were the major flavonoids identified in the extract. The insecticidal activity of the extract against the 4th larval instar of A. ipsilon was evaluated using four concentrations (1.25-10 %). The study found that higher extract concentrations led to increased mortality in the larvae. Specifically, the concentration of 10 % resulted in the highest mortality rate of 96.67 %. Lower concentrations of 5 %, 2.5 %, and 1.25 % resulted in mortality rates of 51.11 %, 18.89 %, and 9.17 %, respectively. The extract also showed higher activity against AChE in larval tissue, with an inhibition percentage of 65.2 % after 24 h of treatment. Docking experiments confirmed that ellagic acid and apigenin had higher binding affinity than the control (lanate). These results demonstrate the potential of utilizing agricultural waste like N. sativa straw to create innovative and sustainable bioinsecticides.

Multiple Site Dissimilarities of Herbaceous Species Due to Coal Fly Ash Dumping Based Soil Heavy Metal Toxication.

The present study analyzes the determinants and patterns of the regional, local, and differential plant diversity of two different sites with similar climatic but varied edaphic factors. This research was undertaken to study the plant diversity and population structure as a consequence of variation in the soil quality between two biotopes: Guru Ghasidas Vishwavidyalaya in Koni (site-I) and National Thermal Power Corporation in Sipat (site-II). The soil of site-I was found to be fertile and showed rich vegetation. On the other hand, the soil of site II was found to be contaminated with heavy metals, which impacts the flora of the region. The ecology of both sites was studied, and their quantitative and qualitative aspects were compared and contrasted. The abundance, density, and richness of the plants in site II were fairly lower than in site-I, which was confirmed by utilizing Simpson's and Shannon's diversity indices. Many of the species collected from site II were heavy metal accumulators and could also serve as indicators of heavy metal toxicity.

The Valorization of Spent Coffee Ground Extract as a Prospective Insecticidal Agent against Some Main Key Pests of Phaseolus vulgaris in the Laboratory and Field.

The exploitation of massive amounts of food and agro-waste represents a severe social, economic, and environmental issue. Under the growing demand for food products that are free of toxic synthetic insecticides, a methanolic extract of spent coffee grounds (SCGs), which represent the main byproduct of coffee production, was applied in the current study as a bioinsecticide against the main pests of the green bean: Spodoptera littoralis, Agrotis ipsilon, Bemisia tabaci, Empoasca fabae, and Aphis craccivora. A deterrent assay, contact bioassay, and lethal concentration analysis were performed to reveal the repellent, antifeedant, and oviposition deterrent effects. Parallel to the above-mentioned bioassays, the phytochemical composition of the methanolic SCG extract was investigated via a high-performance liquid chromatography (HPLC) analysis. Fourteen phenolic acids and five flavonoids, in addition to caffeine (alkaloid), were identified in the extract. Cinnamic, rosmarinic, and gallic acids were the predominant phenolics, while apigenin-7-glucoside was the main flavonoid, followed by naringin, catechin, and epicatechin. The extract of SCGs showed an insecticidal effect, with a mortality between 27.5 and 76% compared to the control (7.4%) and based on the concentration of the extract used. In the same trend, the oviposition efficiency revealed different batches of laid eggs (0.67, 2.33, 7.33, and 8.67 batches/jar) for 100, 50, and 25% of the SCG extract and the control. Finally, the major components of the SCG extract were docked into the insecticide acetylcholinesterase enzyme to explore their potential for inhibition, where apigenin-7-glucoside showed a higher binding affinity, followed by catechin, compared to the control (lannate). The obtained findings could be a starting point for developing novel bioinsecticides from SCGs.

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Impact of sustained virologic response in regression of portal hypertension in Egyptian patients with hepatitis C virus-associated cirrhosis and portal hypertension.

Background: Portal hypertension (PH) is a common consequence in hepatitis C virus cirrhotic patients. With interferon alpha-based therapy, SVR was linked to improved PH and fibrosis regression. SVR to oral antiviral regimens is linked to reduced portal pressure in patients with clinically significant portal hypertension (CSPH) at baseline. However, CSPH continues in most of the patients. This study aims to assess the reversibility and/or improvement of PH in Egyptian patients with HCV-related cirrhosis and CSPH after achieving SVR with DAAs. The second aim is to evaluate performance of the noninvasive markers of fibrosis in prediction of the presence and/or reversibility of the CSPH in correlation to radiological and endoscopic parameters. Subjects and methods: We evaluated noninvasive parameters, radiological and endoscopic signs of PH at baseline, and/or SVR 24 and SVR 48 post-DAA therapy in 40 patients with cirrhosis and CSPH (group A) and another 40 patients with cirrhosis only (group B). Results: In group A, the spleen diameter decreased from baseline (15.74 ± 1.53 cm), and SVR 24 (15.48 ± 1.51), to SVR 48 (15.35 ± 1.49 cm). No ascites detected at SVR 48 in 62.5%. Portal vein diameter and portal vein blood velocity reduced to 13.53 ± 1.07 mm and 14.14 ± 2.2 cm/s at SVR 48, with reversibility of hepatic vein waveform towards the triphasic pattern. Medium to large esophageal varices regressed from 52.5% to 2.5%, and up to 70% of patients showed no EVs at SVR 48. In group A, 24 patients showed complete reversibility of CSPH, and 16 patients showed improvement of CSPH. Child-Pugh score, FIB-4 index, King's score, and Lok index revealed higher significance for detection of the presence of PH. Child-Pugh score, PC/SD ratio, and Lok index revealed higher significance for detection of reversibility of PH. Conclusion: We concluded that CSPH improved after SVR with DAAs and completely regressed in some patients. Upon predicting the presence of PH, Child-Pugh score, FIB-4 index, King's score, and Lok index were the most significant noninvasive scores. While for predicting the reversibility of PH, Child-Pugh score, PC/SD ratio, and Lok index were the most significant scores.

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.