Biochemical and immunological parameters as indicators of osteoarthritis subjects: role of OH-collagen in auto-antibodies generation

Osteoarthritis (OA) is characterized by inflammation of the knee joint, which is caused by accumulation of cytokines and C-reactive protein (CRP) in the extracellular matrix as an early immune response to infection. The articular cartilage destruction is discernible by elevated tumour necrosis factor-a (TNF-a). In this study, blood samples of knee osteoarthritis patients were analyzed for biochemical and physiological parameters based on the lipid profile, uric acid, total leukocyte count (TLC), hemoglobin percentage (Hb%) and absolute lymphocyte count (ALC). Furthermore, immunological parameters including TNF-a, interleukin-6 (IL-6) and CRP were analyzed. The presence of antibodies against hydroxyl radical modified collagen-II (•OH-collagen-II) was also investigated in arthritis patients using direct binding ELISA. The uric acid and lipid profiles changed extensively. Specifically, increased uric acid levels were associated with OA in both genders, as were enhanced immunological parameters. The TNF-a level also increased in both genders suffering from OA. Finally, auto-antibodies against OH-collagen II antigen were found in the sera of arthritis patients. These results indicated that immunological parameters are better predictors or indexes for diagnosis of OA than biochemical parameters

Seasonal variation of mixed layer depth in the north Arabian Sea

The Arabian Sea is unique due to the extremes in atmospheric forcing that lead to the semi-annual seasonal changes. The reversing winds of summer and winter monsoon induce the variation in the characteristics of mixed layer depth. The importance of mixed layer depth is recognized in studying the biological productivity in the ocean. In this paper variability of mixed layer depth in the north Arabian Sea have been discussed. The study is based on the data collected under North Arabian Sea Environment and Ecosystem Research (NASEER) program. The results of the study indicate that there is a significant variation in the mixed layer depth from summer to winter monsoon as well as coast to offshore

Seasonal effect and long-term nutritional status following exit from a Community-Based Management of Severe Acute Malnutrition program in Bihar, India.

BACKGROUND/OBJECTIVES: Children aged 6 months to 5 years completing treatment for severe acute malnutrition (SAM) in a Médecins Sans Frontières Community Management of Acute Malnutrition (CMAM) program in Bihar, India, showed high cure rates; however, the program suffered default rates of 38%. This report describes the nutritional status of 1956 children followed up between 3 and 18 months after exiting the program. SUBJECTS/METHODS: All children aged 6-59 months discharged as cured with mid-upper arm circumference (MUAC) ⩾120 mm or who defaulted from the program with MUAC <115 mm were traced at 3, 6, 9, 12 and 18 months (±10 days) before three exit reference dates: first at the end of the food insecure period, second after the 2-month food security and third after the 4-month food security. RESULTS: Overall, 68.7% (n=692) of defaulters and 76.2% (n=1264) of children discharged as cured were traced. Combined rates of non-recovery in children who defaulted with MUAC <115 mm were 41%, 30.1%, 9.9%, 6.1% and 3.6% at 3, 6, 9, 12 and 18 months following exit, respectively. Combined rates of relapse among cured cases (MUAC ⩾120 mm) were 9.1%, 2.9%, 2.1%, 2.8% and 0% at 3, 6, 9, 12 and 18 months following discharge, respectively. Prevalence of undernutrition increased substantially for both groups traced during low food security periods. Odds of death were much higher for children defaulting with MUAC <110 mm when compared with children discharged as cured, who shared the same mortality risk as those defaulting with MUAC 110-<115 mm. CONCLUSIONS: Seasonal food security predicted short-term nutritional status after exit, with relapse rates and non-recovery from SAM much higher during food insecurity. Mortality outcomes suggest that a MUAC of 110 mm may be considered an appropriate admission point for SAM treatment programs in this context

Synthesis & Vibrational Spectra of Dioxouranium(VI), Th(IV), Zr(IV) & Oxozirconium(IV) Chlorosulphates &Their Complexes with Some Organic Donors

1056-105

Cancellation of Contact Quenching : A Simple Concept for Selective Chemosensing of Basic Fluoride and Acetate Anions

A weakly fluorescent acid-base pair formed by reacting fluorescent acridine orange with the quencher picric acid is reported for the detection of basic fluoride and acetate anions. Deprotonation by these anions causes disengagement of the fluorescent acridine orange from the quencher, picric acid. This phenomenon cancels the quenching existing in the native probe, thereby allowing for the optical signalling of fluoride and acetate anions by color modulation as well fluorescence switch-on response. Anions such as Br-, I-, Cl-, NO3-, SCN-, HSO4-, and H2PO4- offer no detectable interferences even in excess concentrations

Power conversion techniques using multi-phase transformer: Configurations, applications, issues and recommendations

Recently, the superiority of multi-phase systems in comparison to three-phase energy systems has been demonstrated with regards to power generation, transmission, distribution, and utilization in particular. Generally, two techniques, specifically semiconductor converter and special transformers (static and passive transformation) have been commonly employed for power generation by utilizing multi-phase systems from the available three-phase power system. The generation of multi-phase power at a fixed frequency by utilizing the static transformation method presents certain advantages compared to semiconductor converters such as reliability, cost-effectiveness, efficiency, and lower total harmonics distortion (THD). Multi-phase transformers are essential to evaluate the parameters of a multi-phase motor, as they require a multi-phase signal that is pure sine wave in nature. However, multi-phase transformers are not suitable for variable frequency applications. Moreover, they have shortcomings with regard to impedance mismatching, the unequal number of turns which lead to inaccurate results in per phase equivalent circuits, which results in an imbalance output in phase voltages and currents. Therefore, this paper aims to investigate multi-phase power transformation from a three-phase system and examine the different static multiphase transformation techniques. In line with this matter, this study outlines various theories and configurations of transformers, including three-phase to five-, seven-, eleven-, and thirteen-phase transformers. Moreover, the review discusses impedance mismatching, voltage unbalance, and per phase equivalent circuit modeling and fault analysis in multi-phase systems. Moreover, various artificial intelligence-based optimization techniques such as particle swarm optimization (PSO) and the genetic algorithm (GA) are explored to address various existing issues. Finally, the review delivers effective future suggestions that would serve as valuable opportunities, guidelines, and directions for power engineers, industries, and decision-makers to further research on multi-phase transformer improvements towards sustainable operation and management.This work was supported by the Universiti Kebangsaan Malaysia under Grant Code GP-2021-K023221. This work also received partial financial support from Universiti Kebangsaan Malaysia under Grant Code GGPM-2020-006.Scopu

Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM

Background: Brain tumor categorization on MRI is a challenging but crucial task in medical imaging, requiring high resilience and accuracy for effective diagnostic applications. This study describe a unique multimodal scheme combining the capabilities of deep learning with ensemble learning approaches to overcome these issues. Methods: The system integrates three new modalities, spatial feature extraction using a pre-trained VGG19 network, sequential dependency learning using a Bidirectional LSTM, and classification efficiency through a LightGBM classifier. Results: The combination of both methods leverages the complementary strengths of convolutional neural networks and recurrent neural networks, thus enabling the model to achieve state-of-the-art performance scores. The outcomes confirm the efficacy of this multimodal approach, which achieves a total accuracy of 97%, an F1-score of 0.97, and a ROC AUC score of 0.997. Conclusion: With synergistic harnessing of spatial and sequential features, the model enhances classification rates and effectively deals with high-dimensional data, compared to traditional single-modal methods. The scalable methodology has the possibility of greatly augmenting brain tumor diagnosis and planning of treatment in medical imaging studies

Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping

Precise classification of lung cancer stages based on CT images remains a significant challenge in oncology. This is vitally necessary for determining prognosis and creating practical treatment plans. Traditional methods mainly rely on human interpretation, which can be inconsistent and prone to fluctuation. To overcome these limitations an automated deep learning model based on the EfficientNet-B0 based architecture is proposed. Explainable AI features enhanced through Gradient-weighted Class Activation Mapping (Grad-CAM) help further boost this model. Training of the model was conducted with 1,190 CT scans from the IQ-OTH/NCCD dataset. All the images fell into the benign, malignant, and normal categories. The suggested technique performs remarkably well, reaching 99% accuracy, 99% precision, and recall rates of 96% for benign cases, 99% for malignant cases, and 100% for normal occurrences. Grad-CAM makes the model more interpretable and transparent by providing visual explanations of its results. It identifies the most important regions in the scans that significantly contribute to the classification results. Apart from contributing to the field of medical image analysis, accurate precision and complete explanations also bring automated diagnosis systems credibility and reliability

Targeting Aberrant Replication and DNA Repair Events for Treating Breast Cancers

The major limitations of DNA-targeting chemotherapy drugs include life-threatening toxicity, acquired resistance and occurrence of secondary cancers. Here, we report a small molecule, Carbazole Blue (CB), that binds to DNA and inhibits cancer growth and metastasis by targeting DNA-related processes that tumor cells use but not the normal cells. We show that CB inhibits the expression of pro-tumorigenic genes that promote unchecked replication and aberrant DNA repair that cancer cells get addicted to survive. In contrast to chemotherapy drugs, systemic delivery of CB suppressed breast cancer growth and metastasis with no toxicity in pre-clinical mouse models. Using PDX and ex vivo explants from estrogen receptor (ER) positive, ER mutant and TNBC patients, we further demonstrated that CB effectively blocks therapy-sensitive and therapy-resistant breast cancer growth without affecting normal breast tissue. Our data provide a strong rationale to develop CB as a viable therapeutic for treating breast cancers