Computational approach for counting of SISH amplification signals for HER2 status assessment.

The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions.

CO capture enhancement by metal oxides impregnated coal fly ash: a breakthrough adsorption study.

Coal fired power plants are significant contributors to CO emissions and produce solid waste in the form of coal fly ash, posing severe environmental challenges. This study explores the application of dry-impregnated coal fly ash for CO capture from gas stream. The modification of coal fly ash was achieved using alkaline earth metal oxides, specifically CaO and MgO, to alter its physical and chemical properties.

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques.

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection.

Correction: Jabbar et al. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images. 2022, , 535.

In the original publication [...

Uroprotective and Hepatoprotective Potential of against the Experimental Animal Model.

() belonging to the family is traditionally used for liver and kidney diseases. The aim of the study was to evaluate the uroprotective and hepatoprotective potentials of in cyclophosphamide-induced interstitial cystitis and paracetamol-induced hepatotoxicity rat model, respectively. Nociception, bladder weight, vesical vascular permeability, Gray's criteria for edema and hemorrhage, and levels of nitric oxide, catalase, and glutathione were estimated and studied in the cystitis model.

The Impact of Delayed Surgical Care on Patient Outcomes With Alimentary Tract Perforation: Insight From a Low-Middle Income Country.

Introduction In-patient delay is associated with increased mortality in patients with alimentary tract perforations. Access to surgical care is a glaring health issue in low-middle income countries (LMICs), where patient presentation is also delayed for a myriad of reasons, which can be broadly categorized as social/cultural, financial, and structural in their nature. The impact these delays have on surgical outcomes in low-middle income countries is not known.

Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images.

Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method.

Sunlight Exposure: A Protective Role in the Development of Myopia.

The study objective was to assess the correlation of time outdoors in sunlight hours with spherical equivalent refraction (SER) and other variables. This was a cross-sectional study carried out in the Fauji Foundation Hospital, Rawalpindi, between January and April 2019. A total of 110 Individuals were included via convenient sampling, with myopia defined as having at least -0.

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images.

Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases.

Texture based localization of a brain tumor from MR-images by using a machine learning approach.

In this paper, a machine learning approach was used for brain tumour localization on FLAIR scans of magnetic resonance images (MRI). The multi-modal brain images dataset (BraTs 2012) was used, that is a skull stripped and co-registered. In order to remove the noise, bilateral filtering is applied and then texton-map images are created by using the Gabor filter bank.