Anaplastic lymphoma kinase rearrangement-associated renal cell carcinoma: Rare subset case report.

Anaplastic lymphoma kinase rearrangement-associated renal cell carcinoma (ALK-RCC) is a rare subtype of renal cell carcinoma characterized by genetic rearrangements involving the ALK gene. Managing ALK-RCC is challenging due to its rarity and limited treatment options. Targeted therapies directed at the ALK gene have shown promise. ALK-RCC is a rare subtype of renal cell carcinoma with unique clinical and pathological features. ALK inhibitors may hold promise as a targeted therapy for ALK-RCC. Further research is needed to understand the behavior of ALK-RCC and develop effective treatment strategies.

Theoretical framework and inference for fitting extreme data through the modified Weibull distribution in a first-failure censored progressive approach.

The importance of biomedical physical data is underscored by its crucial role in advancing our comprehension of human health, unraveling the mechanisms underlying diseases, and facilitating the development of innovative medical treatments and interventions. This data serves as a fundamental resource, empowering researchers, healthcare professionals, and scientists to make informed decisions, pioneer research, and ultimately enhance global healthcare quality and individual well-being. It forms a cornerstone in the ongoing pursuit of medical progress and improved healthcare outcomes. This article aims to tackle challenges in estimating unknown parameters and reliability measures related to the modified Weibull distribution when applied to censored progressive biomedical data from the initial failure occurrence. In this context, the article proposes both classical and Bayesian techniques to derive estimates for unknown parameters, survival, and failure rate functions. Bayesian estimates are computed considering both asymmetric and symmetric loss functions. The Markov chain Monte Carlo method is employed to obtain these Bayesian estimates and their corresponding highest posterior density credible intervals. Due to the inherent complexity of these estimators, which cannot be theoretically compared, a simulation study is conducted to evaluate the performance of various estimation procedures. Additionally, a range of optimization criteria is utilized to identify the most effective progressive control strategies. Lastly, the article presents a medical application to illustrate the effectiveness of the proposed estimators. Numerical findings indicate that Bayesian estimates outperform other estimation methods by achieving minimal root mean square errors and narrower interval lengths.

The effect of SiO2 crystallization in enhancing the luminescence of Dy3+-Sm3+ co-doped glass ceramics for cool white light application.

The present work investigates the structural and luminescence behaviour of Dy3+-Sm3+ co-doped glass ceramics obtained through heat treatment of precursor glasses. The growth of SiO2 polycrystalline particles and evolution of these crystallites in the glass domain are witnessed via XRD and FESEM study. The presence of network vibrational bands, hydroxyl groups and the increased quantity of bridging oxygens (BOs) in glass ceramics are analysed through FTIR spectroscopy study. The absorption study (UV-Visible-NIR) showed the possible electronic transitions of Dy3+ and Sm3+ ions. The red shift in the absorption band edges and the lower bandgap values are obtained as a result of improved heat treatment in glass ceramics. Emission studies show the enhanced luminescence intensity of glass ceramics under 350 and 402 nm excitations. Decay measurement of glass ceramics showed the improved lifetimes of Dy3+ and Sm3+ ions to have appeared in microseconds (×10-6 s). The colour characteristics of glass ceramics analysed using CIE colour chromaticity diagram and correlated colour temperature (CCT) values suggest the neutral to cool white light emissions. Therefore, prepared glass ceramics with SiO2 polycrystalline phase are considered to be suitable materials in cool white LEDs applications.

Comprehensive study for radiation shielding features for Bi2O3-B2O3-ZnO composite using computational radioanalytical Phy-X/PSD, MCNP5, and SRIM software.

The current study uses zinc oxide doping nanoparticles to investigate the radiation shielding properties of bismuth borate glass. Fourier transform infrared (FTIR) and X-ray diffraction (XRD) examined the structural characteristics of the current samples. In contrast, the optical properties were determined based on the absorption spectrum for current samples. Appraisal studies are carried out depending on the simulation capabilities of Phy-X/PSD software in conjunction with MCNP5 to achieve this goal. In addition, the neutron and charged particle shielding properties were evaluated theoretically. All glasses are amorphous, as confirmed by the XRD data, and the FTIR data showed several vibration bands and functional groups. The density showed rising from 5.981 to 6.433 g/cm3 with adding ZnO. The band gap values reduced from 2.831 to 2.091 eV for direct and 3.024 to 2.218 eV for indirect with adding ZnO. The investigations' findings demonstrate a strong agreement between the theoretical and simulation-derived estimates of the mass attenuation coefficient. The relative difference of MAC results lie in the range 0.106-2.941% for BBZ0, 0.105-4.348% for BBZ1, 0.105-3.398% for BBZ2, and 0.105-2.032% for BBZ3. The study's findings are valuable insights from thoroughly examining these parameters, which can potentially improve the radiation protection abilities of Bi2O3-B2O3-ZnO glasses. This study represents a significant step in developing more efficient and safer materials for gamma radiation shielding applications.

Fine tuning deep learning models for breast tumor classification.

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Assessing the awareness, knowledge, and attitudes of adults in the general population in Saudi Arabia towards testicular torsion: A cross-sectional study.

Introduction: Testicular torsion (TT) is a rare but urgent condition; it leads to severe pain and swelling that must be diagnosed and treated immediately, where there is an expectant period of 6-8 h. It is an important prognostic factor in the arrival time to the emergency department. Raising awareness about TT is essential to ensure early diagnosis and timely treatment, which can prevent irreversible damage and loss of fertility. Aim: Our study aimed to measure the awareness, knowledge, and attitude toward testicular torsion (TT) among the general population in Saudi Arabia. Subject and Methods: This is a cross-sectional study conducted among the general population living in Saudi Arabia. A self-administered questionnaire was distributed among the population using an online survey. The questionnaire includes socio-demographic characteristics (i.e., age, gender, region, etc.), a questionnaire to assess the knowledge of testicular torsion, and questionnaires to assess the prevalence, management, and attitude toward testicular torsion. Results: Of the 473 participants, 56% were males, and 61% were aged between 18 to 30 years old. Unfortunately, One quarter of our population has a good knowledge level about testicular torsion. The overall mean knowledge about Testicular Torsion score was below average (3 out of 8 points). Factors associated with increased knowledge include younger age, male gender, living in the Central Region, working in the medical field, having heard of testicular torsion, and having a personal or family history of testicular torsion. Conclusion: In conclusion, there was a lack of testicular torsion knowledge among the general population. Further studies and social campaigns should be done to encourage the general population's understanding and increase their knowledge level.

Impact of carbimazole combined with vitamin E on testicular injury induced by experimental hyperthyroidism in adult albino rats: oxidative/inflammatory/apoptotic pathways.

Thyroid hormones play essential roles in spermatogenesis, but their effects on infertile males remain poorly understood. This study aimed to evaluate the impact of combining carbimazole (CBZ) with vitamin E (VE) on testicular injury induced by experimental hyperthyroidism in adult albino rats, focusing on oxidative, inflammatory, and apoptotic pathways. In this experimental study, 64 adult male albino Wistar rats were divided into eight groups: Group I (control-untreated), Group II (CBZ-control), Group III (VE-control), Group IV (CBZ + VE-control), Group V (levothyroxine-induced testicular injury), Group VI (levothyroxine + CBZ-treated), Group VII (levothyroxine + VE-treated), and Group VIII (levothyroxine + CBZ + VE-treated). The study was conducted in the Faculty of Medicine, Suez Canal University (Ismailia, Egypt). After cervical decapitation, both testes and epididymis were examined histopathologically and immunohistochemically. Significant differences were observed among groups concerning malondialdehyde (MDA), glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT; all P < 0.001). Polymerase chain reaction analysis showed significant differences in tumor necrosis factor-α (TNF- α ), interleukin-10 (IL-10), Bcl-2-associated X protein (BAX), B-cell lymphoma 2 protein (Bcl2), p53, Caspase-3, Caspase-8, Caspase-9, and nuclear factor-kappa B (NF- κ B) mRNA levels (all P < 0.001). Hyperthyroid group treated with CBZ alone (Group VI) exhibited testicular side effects, affecting seminiferous tubules and spermatogenesis. However, the Group VIII showed improved spermatogenesis and a decrease in testicular side effects. The addition of VE to the treatment of hyperthyroid rats with CBZ reduced testicular side effects and seminiferous tubular affection when potentially improving spermatogenesis. Further research is needed to elucidate the underlying mechanisms fully.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

BACKGROUND: The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. RESULTS: We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. CONCLUSIONS: This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

Quantitative assessment of dosimetric effect of using alternative OAR delineations in treatment planning as functions of delineations, setup uncertainty and planning techniques using an alternative truth assessment method.

PURPOSE: This study aims to quantify the variation in dose-volume histogram (DVH) and normal tissue complication probability(NTCP) metrics for head-and-neck (HN) cancer patients when alternative organ-at-risk(OAR) delineations are used for treatment planning and for treatment plan evaluation. We particularly focus on the effects of daily patient positioning/setup variations(SV) in relation to treatment technique and delineation variability. MATERIALS AND METHODS: We generated two-arc VMAT, 5-beam IMRT, and 9-beam IMRT treatment plans for a cohort of 209 HN patients. These plans incorporated five different OAR delineation sets, including manual and four automated algorithms. Each treatment plan was assessed under various simulated per-fraction patient setup uncertainties, evaluating the potential clinical impacts through DVH and NTCP metrics. RESULTS: The study demonstrates that increasing SV generally reduces differences in DVH metrics between alternative delineations. However, in contrast, differences in NTCP metrics tend to increase with higher setup variability. This pattern is observed consistently across different treatment plans and delineator combinations, illustrating the intricate relationship between SV and delineation accuracy. Additionally, the need for delineation accuracy in treatment planning is shown to be case-specific and dependent on factors beyond geometric variations. CONCLUSIONS: The findings highlight the necessity for comprehensive Quality Assurance programs in radiotherapy, incorporating both dosimetric impact analysis and geometric variation assessment to ensure optimal delineation quality. The study emphasizes the complex dynamics of treatment planning in radiotherapy, advocating for personalized, case-specific strategies in clinical practice to enhance patient care quality and efficacy in the face of varying SV and delineation accuracies.