To assess the impact of spleen status on engraftment, and early morbidity and mortality after allogeneic hematopoietic cell transplantation (HCT), we analyzed 9,683 myeloablative allograft recipients ...from 1990 to 2006; 472 had prior splenectomy (SP), 300 splenic irradiation (SI), 1,471 with splenomegaly (SM), and 7,440 with normal spleen (NS). Median times to neutrophil engraftment (NE) and platelet engraftment (PE) were 15 vs 18 days and 22 vs 24 days for the SP and NS groups, respectively (P<0.001). Hematopoietic recovery at day +100 was not different across all groups, however the odds ratio of days +14 and +21 NE and day +28 PE were 3.26, 2.25 and 1.28 for SP, and 0.56, 0.55, and 0.82 for SM groups compared to NS (P<0.001), respectively. Among patients with SM, use of peripheral blood grafts improved NE at day +21, and CD34+ cell dose >5.7 × 10(6)/kg improved PE at day+28. After adjusting variables by Cox regression, the incidence of GVHD and OS were not different among groups. SM is associated with delayed engraftment, whereas SP prior to HCT facilitates early engraftment without having an impact on survival.
•The proposed method initially performs input image pre-processing to remove label noise and lighting problems.•To separate the ROI region, hesitant fuzzy linguistic bi-objective clustering is ...employed.•The INSST region to extract features using the segmented ROI region.•Skin cancer and healthy skin are distinguished using the Deep Siamese domain adaptation convolutional Neural Network.•The Honey Badger Algorithm is used to optimize the weight parameters of the DSDACNN.
Melanoma skin cancer poses significant challenges in terms of early detection and accurate diagnosis. It often goes unnoticed in its early stages, leading to advanced diagnoses with poorer treatment outcomes. Diagnosis is subjective and can vary among healthcare professionals, while limited accessibility to dermatologists further delays diagnosis. Addressing these challenges requires innovative solutions, leveraging technology and artificial intelligence, to improve early detection, enhance accuracy, and ultimately improve patient outcomes in melanoma skin cancer. In this paper, Skin cancer identification from dermoscopic images utilizing Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm is proposed. The proposed method initially performs input image pre-processing to remove label noise and lighting problems. The segmentation was then given the output of the pre-processing. To separate the ROI region, hesitant fuzzy linguistic bi-objective clustering is employed. The improved non-subsampled Shearlet transforms (INSST) region to extract features using the segmented ROI region. Skin cancer and healthy skin are distinguished using the Deep Siamese domain adaptation convolutional Neural Network (DSDACNN). The Honey Badger Algorithm is used to optimize the weight parameters of the DSDACNN. The proposed SKD-DSDACNN-HBA method is carry out in Python. The performance of the proposed SKD-DSDACNN-HBA method attains 15.64%, 20.07% and 25.5% higher F1-score, 24.35%, 29.33% and 35.29% higher Computational time, 24.72%, 29.32% and 36.66% higher AUC and 33.55%, 28.52% and 19.85% lower Error rate while compared with existing methods, like fuzzy k-means clustering (SKD-FKMC), handcrafted and non-handcrafted features (SKD-HNHF) and deep learning features with improved moth flame optimization (SKD-DLF-IMFO).
The imaging characteristics and modes of presentation of brain AVMs may vary with patient age. Our aim was to determine whether clinical and angioarchitectural features of brain AVMs differ between ...children and adults.
A prospectively collected institutional data base of all patients diagnosed with brain AVMs since 2001 was queried. Demographic, clinical, and angioarchitecture information was summarized and analyzed with univariable and multivariable models.
Results often differed when age was treated as a continuous variable as opposed to dividing subjects into children (18 years or younger; n = 203) versus adults (older than 18 years; n = 630). Children were more likely to present with AVM hemorrhage than adults (59% versus 41%, P < .001). Although AVMs with a larger nidus presented at younger ages (mean of 26.8 years for >6 cm compared with 37.1 years for <3 cm), this feature was not significantly different between children and adults (P = .069). Exclusively deep venous drainage was more common in younger subjects when age was treated continuously (P = .04) or dichotomized (P < .001). Venous ectasia was more common with increasing age (mean, 39.4 years with ectasia compared with 31.1 years without ectasia) and when adults were compared with children (52% versus 35%, P < .001). Patients with feeding artery aneurysms presented at a later average age (44.1 years) than those without such aneurysms (31.6 years); this observation persisted when comparing children with adults (13% versus 29%, P < .001).
Although children with brain AVMs were more likely to come to clinical attention due to hemorrhage than adults, venous ectasia and feeding artery aneurysms were under-represented in children, suggesting that these particular high-risk features take time to develop.
The study endeavors the anaerobic treatment of cyanide-containing effluents using the hybrid anaerobic reactor, with self-immobilized granules under high up-flow velocities. Comparison of one-year ...time-course analyses of HARs treating high strength effluents containing cyanide and control indicates the importance of wastewater characteristics in development and maintenance of microbiome. Efforts were directed towards associating process performance with microbial dynamics. Presence of cyanide results in the accumulation of intermediates paralleled with a drop in abundance of sensitive aceticlastic methanogens. HAR appear to have better resilience than other identified digesters because of shielding effects and enhanced granule-wastewater contact. The predominance of Methanobacteriales in the presence of cyanide can be linked to its tolerance. It was found that methane yield is positively correlated with abundance of aceticlastic guilds (R = 0.830, CI = 0.01). Tolerant bacterial groups were also identified. The study advances our knowledge related to less energy intensive technology with the focus on the development of efficient HAR.
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•Hybrid anaerobic reactor (HAR) had higher resilience than other high rate systems.•Temporal analysis of HAR demonstrated achievement of higher inhibition threshold.•Anaerobic microbiome structure was strongly linked with the process parameters.•Clostridiales and Methanobacteriales appeared as most tolerant microbial groups.•Higher up-flow velocities resulted in achievement of higher efficiencies.
The purpose of this systematic review and meta‐analysis was to examine clinical outcomes associated with convalescent plasma therapy in COVID‐19 patients. We performed a literature search on PubMed, ...medRxiv, Web of Science, and Scopus to identify studies published up to December 10th, 2020 that examined the efficacy of convalescent plasma treatment for COVID‐19. The primary endpoints were mortality, clinical improvement, and hospital length of stay. We screened 859 studies that met the search criteria, performed full‐text reviews of 56 articles, and identified 15 articles that fulfilled inclusion criteria for meta‐analysis. The odds of mortality were significantly lower in the convalescent plasma group compared to the control group (OR = 0.59 95% CI = 0.44; 0.78, P < .001), although results from two key randomized controlled trials did not support the mortality benefit. The odds of clinical improvement were significantly higher in the convalescent plasma group compared to the control group (OR = 2.02 95% CI = 1.54; 2.65, P < .001). There was no difference in hospital length of stay between the convalescent plasma group and the control group (MD = −0.49 days 95% CI = −3.11; 2.12, P = .713). In all, these data indicate that a mortality benefit with convalescent plasma is unclear, although there remain benefits with convalescent plasma therapy for COVID‐19.
Purpose
There is no clear consensus on how to assess low rectal anastomotic integrity and patency prior to reversal of de-functioning stoma. The aim of this systematic review was to assess the ...utility of contrast enema (CE) in this context and to clarify the natural history of radiological leaks.
Methods
Keyword search of electronic databases (Embase, MEDLINE, Cochrane Library, Google Scholar) and bibliographic cross-referencing were performed to identify appropriate studies. Data extraction and synthesis was performed with the primary outcomes being the sensitivity and specificity of CE for detecting clinically significant abnormalities. Statistical analysis was performed using Open Meta-Analyst software. Narrative review of outcomes including those of clinical and radiological leaks was also undertaken.
Results
A total of 1,142 CE from 11 articles were included in the final meta-analysis. CE had high specificity (95.4; 95 % confidence interval = 92.0–97.4) and negative predictive value (98.4; 97.4–99.1) and moderate sensitivity (79.9; 63.9–89.9) and positive predictive value (64.6; 55.5–72.9) for the detection of clinically significant anastomotic problems. There was a high degree of correlation between CE and clinical examination findings (96.7 %). Occult radiological leaks were seen in 5.7 % of CE, and all but one (97 %) eventually underwent successful reversal. Only three quarters of patients with clinical leak underwent successful reversal.
Conclusion
CE is effective at excluding clinically significant anastomotic problems, especially after clinical anastomotic leaks. However, false positive results can be observed in asymptomatic patients, and it is unclear how much additional information CE provides over clinical assessment in the low uncomplicated anastomosis.
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex ...nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.
Herein we report the high-temperature crystal chemistry of K2Ce(PO4)2 as observed from a joint in situ variable-temperature X-ray diffraction (XRD) and Raman spectroscopy as well as ab initio ...density functional theory (DFT) calculations. These studies revealed that the ambient-temperature monoclinic (P21 /n) phase reversibly transforms to a tetragonal (I41 /amd) structure at higher temperature. Also, from the experimental and theoretical calculations, a possible existence of an orthorhombic (Imma) structure with almost zero orthorhombicity is predicted which is closely related to tetragonal K2Ce(PO4)2. The high-temperature tetragonal phase reverts back to ambient monoclinic phase at much lower temperature in the cooling cycle compared to that observed at the heating cycle. XRD studies revealed the transition is accompanied by volume expansion of about 14.4%. The lower packing density of the high-temperature phase is reflected in its significantly lower thermal expansion coefficient (αV = 3.83 × 10–6 K–1) compared to that in ambient monoclinic phase (αV = 41.30 × 10–6 K–1). The coexistences of low- and high-temperature phases, large volume discontinuity in transition, and large hysteresis of transition temperature in heating and cooling cycles, as well as drastically different structural arrangement are in accordance with the first-order reconstructive nature of the transition. Temperature-dependent Raman spectra indicate significant changes around 783 K attributable to the phase transition. In situ low-temperature XRD, neutron diffraction, and Raman spectroscopic studies revealed no structural transition below ambient temperature. Raman mode frequencies, temperature coefficients, and reduced temperature coefficients for both monoclinic and tetragonal phases of K2Ce(PO4)2 have been obtained. Several lattice and external modes of rigid PO4 units are found to be strongly anharmonic. The observed phase transition and structures as well as vibrational properties of both ambient- and high-temperature phases were complimented by DFT calculations. The optical absorption studies on monoclinic phase indicated a band gap of about 2.46 eV. The electronic structure calculations on ambient-temperature monoclinic and high-temperature phases were also carried out.
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