Browse Articles

Estrogen sulfotransferase SULT1E1 expression correlates with progression and prognosis of lung adenocarcinoma

Mortality causes and health spending by gender and health conditions in octogenarians, nonagenarians and centenarians in Colombia

An innovative complex-valued encoding black-winged kite algorithm for global optimization

A latent diffusion approach to visual attribution in medical imaging

Abstract Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue deployed in standard machine vision pipelines (which are less straightforwardly interpretable/explainable to clinicians). We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models, in order to generate normal counterparts of abnormal images. The discrepancy between the two hence gives rise to a mapping indicating the diagnostically-relevant image components. To achieve this, we deploy image priors in conjunction with appropriate conditioning mechanisms in order to control the image generative process, including natural language text prompts acquired from medical science and applied radiology. We perform experiments and quantitatively evaluate our results on the COVID-19 Radiography Database containing labelled chest X-rays with differing pathologies via the Frechet Inception Distance (FID), Structural Similarity (SSIM) and Multi Scale Structural Similarity Metric (MS-SSIM) metrics obtained between real and generated images. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction, which are evaluated with real examples from the cheXpert dataset.

The circadian clock gene period regulates the composition and daily bacterial load of the gut microbiome in Drosophila melanogaster

Investigating Seidel energies and thermodynamic properties of benzenoid hydrocarbons through regression models

Cell free supernatants of Bifidobacterium adolescentis and Bifidobacterium longum suppress the tumor growth in colorectal cancer organoid model

Interpretable machine learning for predicting sepsis risk in emergency triage patients

Integrated singlecell and bulk RNA-seq analysis identifies a prognostic signature related to inflammation in colorectal cancer

Associations between the conicity index and kidney stone disease prevalence and mortality in American adults

Investigations on the synthesis and characterization of silver-doped MoO3 thin films for photocatalytic applications

AbstractIn this study, we aimed to enhance the photocatalytic performance of molybdenum oxide (MoO3) thin films by doping with silver (Ag) via a spray pyrolysis technique. The primary objective for silver incorporation was intended to introduce additional energy levels into the band structure of MoO3, improving its efficiency. Structural, optical, and photocatalytic properties were analyzed using X-ray diffraction (XRD) and optical spectroscopy. XRD results confirmed an orthorhombic phase with a (040) preferential orientation for all samples. Optimal crystallinity was observed with 2% Ag doping, yielding an 84 nm crystallite size, while higher doping levels reduced crystallite size. Band gap energy narrowed from 3.07 eV (undoped) to 2.94 eV (2% Ag-doped), indicating electronic structure changes. Impedance spectroscopy revealed superior electrical properties at 4% Ag doping, enhancing charge transport. Photocatalytic performance, assessed via dye degradation, showed significant improvement with silver doping, the degradation rate peaking at 4% Ag. These results demonstrate that silver doping optimizes structural and electronic properties of MoO3 thin films, leading to enhanced photocatalytic activity.

Surrounding rock control for the roadway driven under the goafs of small collieries and heading for adjacent advancing face

Core-genome guided novel therapeutic targets identification and chimeric vaccine designing against Rickettsia rickettsii

Role of P2 X7 receptor during focused ultrasound induced blood brain barrier modulation

The homeobox family gene signature predicts the prognosis of osteosarcoma and correlates with immune invasion

Stability-indicating spectrophotometric quantification of safinamide in the presence of its possible degradation product

AbstractIn recent times, a truly exquisite pharmaceutical marvel has graced the world of medicine, known as Safinamide (SAF). This opulent creation has been specifically tailored to cater to the needs of individuals afflicted with Parkinson’s disease (PD), an esteemed neurological condition renowned for its regal ability to impede motor skills, coordination, and equilibrium. It is highly improbable that degradation products of pharmaceutical components would significantly compromise efficiency and safety of a drug during its shelf life. Pharmaceutical analysis requires a variety of stability tests to be conducted under distinct conditions. As a result, there was an increased need for the development of an analytical methodology capable of reliably separating and quantifying degradants and impurities that might be found in pharmaceuticals. In this study, we have developed two efficient and straightforward spectrophotometric methodologies for the concurrent estimation of SAF and its degradation product (SAF DEG), which is the main acid hydrolysis product. The confirmation of degradation product build-up by the use of several analytical techniques, including infrared spectroscopy (IR), and mass spectrometry (MS) investigations. The present methodologies have been validated for linearity within the concentration range of 5–30 µg/ml for SAF, and 5–15 µg/mL, 2–15 µg/ml for SAF DEG for fourier self-deconvolution (FSD) and dual wavelength (DW) methods, respectively. The originality of these techniques lies in their status as the first stability-indicating spectrophotometric procedures that are both environmentally friendly. Moreover, the process of obtaining pure SAF DEG offers substantial economic benefits by obviating the need to acquire a costly constituent. The use of intelligent techniques was employed to analyze the pharmaceutical dosage form, potentially offering significant advantages to the pharmaceutical industry.

Green-synthesized silver nanoparticle-enhanced nanofiltration mixed matrix membranes for high-performance water purification

A hybrid attention multi-scale fusion network for real-time semantic segmentation

AbstractIn semantic segmentation research, spatial information and receptive fields are essential. However, currently, most algorithms focus on acquiring semantic information and lose a significant amount of spatial information, leading to a significant decrease in accuracy despite improving real-time inference speed. This paper proposes a new method to address this issue. Specifically, we have designed a new module (HFRM) that combines channel attention and spatial attention to retrieve the spatial information lost during downsampling and enhance object classification accuracy. Regarding fusing spatial and semantic information, we have designed a new module (HFFM) to merge features of two different levels more effectively and capture a larger receptive field through an attention mechanism. Additionally, edge detection methods have been incorporated to enhance the extraction of boundary information. Experimental results demonstrate that for an input size of 512 × 1024, our proposed method achieves 73.6% mIoU at 176 frames per second (FPS) on the Cityscapes dataset and 70.0% mIoU at 146 FPS on Camvid. Compared to existing networks, our Model achieves faster inference speed while maintaining accuracy, enhancing its practicality.

Control of flow deflection angle around the corner using microjet array

Follow-up care needs and motivational factors for childhood cancer survivors and their parents in Germany

AbstractThis study aims to explore the long-term follow-up needs and motivations of childhood and adolescent cancer survivors and their parents to attend follow-up care in Germany, given the inconsistent adherence to national follow-up guidelines. We developed interview guidelines based on the Theory of Planned Behavior and the stereotype priming model to explore motivations and barriers related to follow-up care. We conducted a total of 36 episodic narrative interviews with adolescent (ages 13–17) and adult (ages 18–45) survivors of pediatric cancer, as well as their parents. We analyzed the transcripts qualitatively using thematic content analysis, while quantitative analysis through multiple regression models was used to support the qualitative findings and identify predictors of follow-up care attendance. We identified key themes across age groups, including a strong need to “return to normal life” and a desire for ongoing organizational and social support. Both survivors and parents highlighted specific needs, including timely, personalized health information and practical help with healthcare logistics. Adolescents particularly valued emotional support from their social circles, whereas adult survivors and parents expressed a need for more structured psychosocial and logistical assistance. The findings suggest that both age and the time elapsed since diagnosis play a role in affecting survivors’ perceived control to attend guideline-based follow-up care. Our findings suggest that a differentiated approach to follow-up care, with age-appropriate support structures, and tailored guidelines, may improve adherence among survivors and their parents. To enhance follow-up care adherence among cancer survivors, healthcare providers could offer tailored, age-specific information and practical assistance with healthcare logistics. Additionally, providing emotional and psychosocial support resources for both survivors and their parents can help address their unique needs at different stages of recovery.Trial registration: Registered at German Clinical Trial Register (ID DRKS00025960 and DRKS00026092).