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Method and application of stability prediction model for rock slope
A Prospective Observational Study on Renal Resistive Index as an Early Predictor of Hepatorenal Syndrome
Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations
Outcome of Patients with Life Threatening Acidosis in Lower Middle Income Country ICU
Improved aerobic capacity in a randomized controlled trial of noncombustible nicotine and tobacco products
Social Inequities and Organ Donation in Sydney\'s Indian Community. No Information, Complexities and Inaction
Novel mercerized Haloxylon salicornicum Sahara plants derived adsorbents for efficient removal of lead(II) from wastewater
Abstract In an effort to valorize the use of Egyptian Sahara plants as biomaterials for the heavy metals remediation from water, we present here a thorough investigation of the utilization of the locally accessible Haloxylon salicornicum (HS) Sahara plant as an adsorbent for Pb(II) remediation. The raw HS and the mercerized one (HSN) adsorbents were characterized by Boehm’s titration method, Fourier Transform infra-red (FTIR), scanning electron microscopy (SEM), Thermo-gravimetric analysis (TGA), and the pHPZC. Many experimental parameters that influence the Pb(II) adsorption onto these adsorbents were thoroughly studied, such as pH, adsorbent (HS and HSN) dose, initial concentration of metal ion, time of contact, and effect of various foreign ions. Due to the higher R2 values and the lower values of error functions, the adsorption of Pb(II) onto HS and HSN adsorbents was best described by pseudo-2nd-order and Langmuir isotherm model with adsorption capacities of 100.68 and 113.33 mg/g for HS and HSN, respectively. Thermodynamic studies revealed the spontaneous and endothermic nature of the adsorption of Pb(II). The mechanism of the adsorption of Pb(II) onto HS and HSN adsorbents was elucidated.
Is Ceftriaxone Sulbactum Edta (CSE) An Effective Alternative in Treatment of Carbapenem Resistant Acinetobactor (CRAB) as a Polymyxin Sparer?
An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks
Abstract Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). Transcriptomics is too complex and expensive for clinical implementation; therefore, an immunohistochemical method is needed. The prognostic impact of the immunohistochemistry-based four CMS-like subtypes remains unclear. Due to the complexity and costs associated with transcriptomics, we developed an immunohistochemistry (IHC)-based method supported by convolutional neural networks (CNNs) to define subgroups that resemble CMS biological characteristics. Building on previous IHC-classifiers and incorporating β-catenin to refine differentiation between CMS2- and CMS3-like profiles, we categorized CRC tumors in a cohort of 538 patients. Classification was successful in 89.4% and 15.9% of tumors were classified as CMS1-like, 35.1% as CMS2-like, 38.7% as CMS3-like, and 11.7% as CMS4-like. CMS2-like patients exhibited the best overall survival (p = 0.018), including when local and metastasized disease were analyzed separately. Our method offers an accessible and clinically feasible CMS-inspired classification, although it does not serve as a replacement for transcriptomic CMS classification.
Comparative Analysis of Nutrition Interventions in Organ Transplant Patients across Diverse Healthcare Settings: A Survey-based Study
Estimating motor symptom presence and severity in Parkinson’s disease from wrist accelerometer time series using ROCKET and InceptionTime
Abstract Parkinson’s disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monitoring for more targeted treatment. Classical time series classification and deep learning techniques have demonstrated limited efficacy in monitoring PD symptoms using wearable accelerometer data due to complex PD movement patterns and the small size of available datasets. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) as they are promising for PD symptom monitoring. InceptionTime’s high learning capacity is well-suited to modeling complex movement patterns, while ROCKET is suited to small datasets. With random search methodology, we identify the highest-scoring InceptionTime architecture and compare its performance to ROCKET with a ridge classifier and a multi-layer perceptron on wrist motion data from PD patients. Our findings indicate that all approaches can learn to estimate tremor severity and bradykinesia presence with moderate performance but encounter challenges in detecting dyskinesia. Among the presented approaches, ROCKET demonstrates higher scores in identifying dyskinesia, whereas InceptionTime exhibits slightly better performance in tremor and bradykinesia estimation. Notably, both methods outperform the multi-layer perceptron. In conclusion, InceptionTime can classify complex wrist motion time series and holds potential for continuous symptom monitoring in PD with further development.
Effectiveness of Medium Cut off Dializer Membrane (Theranova) for Rhabdomyolysis in ICU for Recovery of Renal Functions – Retrospective Case Series
Predicting photodegradation rate constants of water pollutants on TiO2 using graph neural network and combined experimental-graph features
Serum Ferritin as a Prognostic Marker of Sepsis in Critically Ill Patients – A Retrospective Study
A glimpse into Oomycota diversity in freshwater lakes and adjacent forests using a metabarcoding approach
Abstract Oomycota , a diverse group of fungus-like protists, play key ecological roles in aquatic and terrestrial ecosystems, yet their habitat-specific diversity and distribution remain poorly understood. This study investigates the diversity of two major Oomycota classes, Saprolegniomycetes and Peronosporomycetes , in two freshwater lakes and their adjacent forests in northeastern Germany. Using a combination of targeted metabarcoding and traditional isolation techniques, we analyzed samples from six habitats, including soil (forest), rotten leaves (forest and shoreline), sediments (shoreline), and surface waters (littoral and pelagic zones). Metabarcoding revealed 401 Oomycota OTUs, with Pythium , Globisporangium , and Saprolegnia as dominant genera. Culture-based methods identified 110 strains, predominantly from surface water and sediment, with Pythium sensu lato and Saprolegnia as the most frequent taxa. Alpha and beta diversity analyses highlighted distinct community structures influenced by lake and habitat type, with significant co-occurrence of Saprolegniomycetes and Peronosporomycetes across habitats. This study provides the first comprehensive metabarcoding-based exploration of Oomycota biodiversity in interconnected freshwater and terrestrial ecotones, uncovering previously unrecognized patterns of habitat-specific diversity.
Dexmedetomidine and Ketamine for Preventing Delirium in Elderly Patients in the Intensive Care Unit: A Comparative Study
Preparation and characteristics evaluation of chitosan-coated nanoliposomes containing ferrous sulfate
ORI for Detection Of Hyperoxemia and Titration of Oxygen Therapy in Intensive Care: A Prospective Observational Cohort Study
The impact of elective spine surgery in Canada for degenerative conditions on patient reported health-related quality of life outcomes
Abstract The impact of spine surgery on Health-Related Quality-of-Life (HRQoL) outcomes across common spinal degenerative diagnoses is not well characterised. A prospective observational study of patients enrolled in the Canadian Spine Outcomes and Research Network (CSORN) registry was performed. Baseline and 1-year post-operative Short Form-12 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores were collated and compared to normative values from the Canadian General Population (CGP). The percentage of patients achieving the PCS Minimum Clinically Important Difference (MCID) was quantified. 5049 patients were included in the analysis. The mean pre-operative SF-12 PCS was 29.5 and MCS was 44.1. This improved to a mean PCS of 40.5 (p < 0.001) and MCS of 49.3 (p < 0.0001) at 1-year post-operatively. The mean pre-operative PCS was over 2 standard deviations (SD) lower than the normative mean of the CGP; this improved to being close to 1-SD from the normative CGP mean at 1-year post-operatively. Findings were similar across age- and sex-stratified subgroups. Across all conditions, 70–75% of patients achieved the PCS MCID. Fewer patients with cervical myelopathy achieved the PCS MCID (59%). In a surgical cohort, patients with degenerative spinal conditions demonstrate a profound reduction in PCS compared to their peers in the CGP. Spinal surgery was impactful in improving physical function HRQoL outcomes in the majority, but not typically to average population norms.