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Pancreas segmentation using AI developed on the largest CT dataset with multi-institutional validation and implications for early cancer detection
Oxidative stress biomarkers for assessing the synergistic toxicity of emamectin benzoate and cyantraniliprole on liver function
Identification of liver proteins as biomarker for postmortem diagnosis of heat stroke through proteomics
Ammonia removal from simulated fish farms by metal organic framework ingrained by egg shell and fish bones
Abstract The current work focuses on the efficient removal of ammonia by calcium-based metal organic framework. Whereas, for the first time, Ca based metal organic framework was synthesized using either fish bones (FB) or eggshell (ES) as biogenic wastes to act as calcium precursors for synthesis of Ca-BDC, abbreviated as Ca-BDC(FB) & Ca-BDC(ES), respectively, to be sequentially applicable for removal of ammonia. The collected data revealed that; FB showed to be more preferable rather than ES as calcium precursor for preparation of highly efficient Ca-BDC. The obtained Ca-BDC (ES) and Ca-BDC (FB) were shown with orthorhombic crystals, while smaller crystal size was observed in case of Ca-BDC (FB) which is reflected in larger surface area (721.38 m2/g) and in turn higher absorptivity for more efficient removal of ammonia. The adsorption of ammonia followed the pseudo-second ordered reaction and Langmuir isotherm, also R2 values of second ordered and Langmuir models were estimated to be 0.98 & 0.99 for Ca-BDC (ES) & Ca-BDC (FB), respectively. The evaluated maximum adsorption capacities (Qm) onto ES & Ca-BDC (ES) were 86.99 mg/g and 308.16 mg/g, respectively. The maximum capacities of ammonia onto FB & Ca-BDC (FB) were 184.28 mg/g and 616.11 mg/g, respectively. Whereas, Ca-BDC (FB) (721.38 m2/g) was shown with significantly wider surface area by factor of 1.3 compared to Ca-BDC (ES) (563.16 m2/g). Overall, the study provides a promising trend for in designing MOFs with appropriate central metals for capturing of NH3.
A novel framework for sentiment classification employing Bi-GRU optimized by enhanced human evolutionary optimization algorithm
Investigation on thermal kinetic behavior of 5 aminotetrazole/sodium periodate gas generator
Dysregulation of MiRNAs in schizophrenia in an Egyptian patient population
Abstract Schizophrenia (SZ) is a complex neuropsychiatric disorder influenced by genetic, environmental, and epigenetic factors, including miRNA dysregulation. This study explored the diagnostic and therapeutic potential of miRNAs in SZ, focusing on seven key miRNAs: miR-137-3p, miR-34a-5p, miR-432-5p, miR-130b-3p, miR-346, miR-195-5p, and miR-103a-3p. Results revealed significant dysregulation of miR-137-3p, miR-195-5p, miR-346, and miR-103a-3p, highlighting their relevance to SZ pathology. Upregulation of miR-137-3p correlated with enhanced cognitive performance, as evidenced by improved scores on the Wisconsin Card Sorting Test (WCST) and Trail Making Test B (TMT-B). Conversely, miR-195-5p and miR-346 were strongly associated with cognitive processing speed, while miR-103a-3p downregulation was linked to reduced conceptual flexibility. Cluster analyses demonstrated that miRNA expression levels varied significantly based on antipsychotic treatment and receptor targeting, suggesting potential regulatory effects of medication. Importantly, miRNAs were measured in PBMCs, highlighting their feasibility as non-invasive biomarkers. The study underscores the diagnostic value of miRNAs, offering a promising avenue for early detection and personalized interventions in SZ. Future research should validate these findings across diverse cohorts and investigate miRNA-based therapeutic strategies. By integrating miRNA profiling into clinical practice, this study provides a foundation for advancing precision medicine in SZ management.
Two-dimensional biothermomechanical effects in a layer of skin tissue exposed to variable thermal loading using a fourth-order MGT model
Abstract A key consideration in medical procedures like thermal therapy is the danger of thermal harm to skin tissues from exposure to fluctuating thermal loads. To maximize treatment effectiveness while safeguarding healthy tissues, it is crucial to accurately anticipate and manage this damage, especially in hyperthermia therapy. The fourth-order Moore–Gibson–Thompson (4MGT) idea is employed in this study to lay a theoretical foundation for bioheat analysis. The purpose of this work is to clarify how skin tissues respond biothermally to varying thermal loading. The model developed makes it easier to anticipate the thermal reactions that occur in human skin and to estimate the efficiency of biothermal transfer in biological tissues. For the suggested model, a two-dimensional skin layer is utilized. The analytical results for tissue temperature are obtained using the normal mode approach. Both the impact of the duration of heat loading exposure and thermal damage are examined. Furthermore, the accuracy of the suggested model is evaluated by contrasting the obtained analytical results with accepted theories. The findings show that when the thermal relaxation time constant is included, the modified Moore-Gibson-Thomson biothermal model forecasts a decrease in temperature compared to the Pennes model.
Carbon dynamics under loss and restoration scenarios in the world’s largest seagrass meadow
Abstract Seagrass sediments accumulate high amounts of organic carbon, but they are threatened by human activities and their global extent continues to shrink. Simultaneously, there is interest in including seagrass carbon accumulation in countries’ Nationally Determined Contributions (NDCs). We used the InVEST Coastal Blue Carbon Model to estimate sediment organic carbon (SOC) accumulation over 100 years in seagrass of the Bahama Banks, the world’s largest seagrass meadow. Using seagrass maps and sediment core measurements, we modeled SOC accumulation in two scenarios: (1) 1% seagrass area loss per year, the Business As Usual scenario (BAU); (2) restoration of seagrass extent to that of 30 years prior by 2120, meeting the goals of the Kunming-Montreal global biodiversity framework. With a conservative initial seagrass extent, by 2120, the SOC accumulation was 90.6 Mt CO2 eq (24.0 autochthonous Mt CO2 eq) in the BAU and 703.7 Mt CO2 eq (186.5 autochthonous Mt CO2 eq) in the restoration scenario, and average additional SOC accumulation was 611.0 Mt CO2 eq (161.9 autochthonous Mt CO2 eq). Using a high estimate of initial seagrass extent, by 2120, the net SOC accumulation was 155.4 Mt CO2 eq (41.2 autochthonous Mt CO2 eq) in the BAU and 1058.2 Mt CO2 eq (280.4 autochthonous Mt CO2 eq) in the restoration scenario, and additional SOC accumulation was 902.8 Mt CO2 eq (239.2 autochthonous Mt CO2 eq). The potential for either SOC accumulation or losses to occur if seagrass extent continues to decline highlights uncertainty around whether Bahamian seagrass meadows will remain a net carbon sink. The additional accumulation of autochthonous carbon if seagrasses were restored was comparable in scale to the annual greenhouse gas emissions of The Bahamas, suggesting potential for seagrass restoration to contribute to the country’s NDCs and broader climate mitigation strategies.
Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification
Can AI help us talk to dolphins? The race is now on
Can NIH-funded research on racism and health survive Trump’s cuts?
Daily briefing: Meet the baby who received the world’s first personalized CRISPR therapy
Author Correction: The pan-cancer lncRNA PLANE regulates an alternative splicing program to promote cancer pathogenesis
Lightweight error-tolerant edge detection using memristor-enabled stochastic computing
Abstract The demand for efficient edge computer vision has spurred the development of stochastic computing for image processing. Memristors, by introducing their inherent switching stochasticity into computation, readily enable stochastic image processing. Here, we present a lightweight, error-tolerant edge detection approach based on memristor-enabled stochastic computing. By integrating memristors into compact logic circuits, we realise lightweight stochastic logics for stochastic number encoding and processing with well-regulated probabilities and correlations. This stochastic and probabilistic computational nature allows the stochastic logics to perform edge detection in edge visual scenarios characterised by high-level errors. As a demonstration, we implement a hardware edge detection operator using the stochastic logics, and prove its exceptional performance with 95% less energy consumption while withstanding 50% bit-flips. The results underscore the potential of our stochastic edge detection approach for developing efficient edge visual hardware for autonomous driving, virtual and augmented reality, medical imaging diagnosis, and beyond.
LipidIN: a comprehensive repository for flash platform-independent annotation and reverse lipidomics
Abstract Improving annotation accuracy, coverage, speed and depth of lipid profiles remains a significant challenge in traditional lipid annotation. We introduce LipidIN, an advanced framework designed for flash platform-independent annotation. LipidIN features a 168.5-million lipid fragmentation hierarchical library that encompasses all potential chain compositions and carbon-carbon double bond locations. The expeditious querying module achieves speeds exceeding one hundred billion queries per second across all mass spectral libraries. The lipid categories intelligence model is developed using three relative retention time rules, reducing false positive annotations and predicting unannotated lipids with a 5.7% estimated false discovery rate, covering 8923 lipids cross various species. More importantly, LipidIN integrates a Wide-spectrum Modeling Yield network for regenerating lipid fragment fingerprints to further improve accuracy and coverage with a 20% estimated recall boosting. We further demonstrate the utility of LipidIN in multiple tasks for lipid annotation and biomarker discovery in clinical cohorts.
Dynamics of a light-driven molecular rotary motor in an optical cavity
Tacrolimus dosing in liver transplant recipients using phenotypic personalized medicine: A phase 2 randomized clinical trial
Abstract Tacrolimus is the most commonly used immunosuppression drug after solid organ transplantation; however, its dosing is challenging due to substantial inter-individual variability, often resulting in blood levels that deviate from the target therapeutic range. We explored whether a dynamically customized, phenotypic-outcome-guided drug dosing method could improve maintenance of drug trough levels within pre-determined target ranges, focusing on tacrolimus immediately after liver transplantation. This single-center, partially blinded, completed clinical trial involved 62 adults undergoing liver transplantation, block randomized into parallel groups: standard-of-care (SOC) clinician-determined or Phenotypic Personalized Medicine (PPM)-guided tacrolimus dosing. The primary outcome was percentage of post-transplant days with large (>2 ng/mL) deviations from the target range. At trial completion, analysis found statistically significant improvement in the PPM group (n = 27): 24.2% of days showing large deviations compared to 38.4% in the SOC group (n = 29) (difference −14.2%, 95% CI: −26.7 to −1.5 %, P = 0.029) with no increase in adverse events. These results demonstrate that PPM-guided tacrolimus dosing more effectively maintains drug levels within the target range compared to SOC, suggesting a promising approach to improving drug dosing. The trial was registered at ClinicalTrials.gov with the identifier NCT03527238.