Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence.

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.

Implementation and Validation of Anisotropic Analytical Algorithm in Eclipse Treatment Planning System for Indigenous Telecobalt Machine (Bhabhatron II).

Background: The photon dose calculation model anisotropic analytical algorithm (AAA) available with eclipse integrated treatment planning system (TPS) (Varian) supports telecobalt dose calculation from Version 13.6 onward. Formerly, pencil beam convolution (PBC) was used for modeling telecobalt machines. Eclipse TPS no longer supports PBC dose calculation algorithm in v13.6 and above. The AAA dose calculation model is a three-dimensional PBC/superposition algorithm. Its configuration is based on Monte-Carlo-determined basic physical parameters that are tailored to measured clinical beam data. Aim: The study investigated the feasibility of clinical implementation of AAA in Eclipse TPS for Bhabhatron II. Materials and Methods: The indigenous telecobalt machine, Bhabhatron II, was configured as a generic machine because an inbuilt machine model for the same was not available in Varian Eclipse TPS algorithm library. In such a scenario, it was necessary to evaluate and validate dosimetric parameters of the TPS because improper tailoring would cause errors in dose calculations. Beam data measurements of the machine were carried out which were used for configuration of the algorithm. Result: After successful configuration, a variety of plans created in TPS were executed on the machine and subsequently evaluated. Conclusion: From this study, we concluded that AAA-based dose calculation in TPS is very well suited for accurate dose calculations for telecobalt machine and can be implemented for clinical use.

Branched chain amino acid profile in early chronic kidney disease.

The nutritional status in chronic kidney disease (CKD) patients is a predictor of prognosis during the first period of dialysis. Serum albumin is the most commonly used nutritional marker. Another index is plasma amino acid profile. Of these, the plasma levels of branched chain amino acids (BCAA), especially valine and leucine, correlate well with nutritional status. Plasma BCAAs were evaluated along with albumin and C-reactive protein in 15 patients of early stages of CKD and 15 age- and sex-matched healthy controls. A significant decrease in plasma valine, leucine and albumin levels was observed in CKD patients when compared with the controls (P <0.05). No significant difference in C-reactive protein (CRP) levels was observed between the two groups. Malnutrition seen in our CKD patients in the form of hypoalbuminemia and decreased concentrations of BCAA points to the need to evaluate the nutritional status in the early stages itself. Simple measures in the form of amino acid supplementation should be instituted early to decrease the morbidity and mortality before start of dialysis in these patients.

In silico targeted genome mining and comparative modelling reveals a putative protein similar to an Arabidopsis drought tolerance DNA binding transcription factor in Chromosome 6 of Sorghum bicolor genome.

Arabidopsis Thaliana HARDY (AtHRD) is a gene with an APETELA 2 / Ethylene Responsive Factor (AP2/ERF) domain linked to improved performance under drought in rice. We hypothesized that the sorghum genome could possess a similar gene product and were motivated to conduct a computational genome scale mining for the protein and analyse its structural and functional properties. AtHRD sequence was used as a query to BLAST against the sorghum genome dataset followed by multiple alignment analysis. A homology model of the target was built using a template detected based on the pair-wise comparison of hidden Markov models for alignments. DNA docking with a matrix of homologous interface contacts was done. Functional and structural analysis of the query and target was conducted using various online servers. A High-scoring segment pair from Chromosome 6 of the sorghum genome in the region between 54948120 and 54948668 had 68 amino acid similarities out of the 184 residues and was 1.4% above twilight zone threshold. The homology model showed 86.8% residues in most favoured regions. The target protein which had an AP2/ERF domain when docked with GCC box DNA motif had conserved residues involved in binding; it had a long unstructured region beyond the AP2 domain with several motifs for the recognition of serine/threonine protein kinase group. The protein model showed that it could bind to a GCC box which is present in several drought responsive genes. The presence of possible signalling domains and intrinsic disorder in the target protein suggest that this could play a role in drought tolerance which is an inherent character of sorghum. These results offer a jumpstart for validation experiments which could pave the way for cis/trans genic improvement of a range of crops.

PEG-SO(3)H catalyzed synthesis and cytotoxicity of α-aminophosphonates.

One pot three-component PEG-SO(3)H catalyzed reaction of 4-(Pyridin-4-yl)benzaldehyde and triethyl phosphite with various primary amines afforded α-aminophosphonates with high yields by the Kabachnik-Field's reaction. These new structurally diversified set of α-aminophosphonates (4a-j) were evaluated for their anti-tumor activity on human chronic myeloid leukemia cells (K 562), human colon carcinoma cells (Colo 205) along with non-cancerous human embryonic kidney cells (HEK 293). They showed moderate activity on both cancerous cells and non-cancerous cells.

Synthesis and bio-activity evaluation of tetraphenyl(phenylamino) methylene bisphosphonates as antioxidant agents and as potent inhibitors of osteoclasts in vitro.

A new series of tetraphenyl bisphosphonates have been elegantly synthesized by one-pot method and were characterized by elemental analysis, FTIR, 1H, 13C, 31P NMR, mass spectra and evaluated for their in vitro antibone resorptive activity by inhibiting growth of osteoclasts. Two bisphosphonates 3g and 3f showed marked inhibition ratio (8 µM and 10 µM) and emerged as lead compounds. All compounds were tested for their antioxidant (DPPH scavenging, reducing power and inhibition of lipid peroxidation). They exhibited potent in vitro antioxidant activity dose-dependently.