Push–pull organic molecules have aroused wide interest in photochemistry and photophysics research communities. In this report, we present the synthesis, characterisation, photophysical and nonlinear optical property studies of a new organic fluorophore (AA2) with a unique push–pull molecular configuration: acceptor–π-bridge–donor–π-bridge–acceptor (A–π–D–π–A) constituted with phenothiazine as the donor moiety. A thorough investigation to validate its structure was undertaken through ¹H, ¹³C NMR and MALDI-TOF mass spectrometry. A variety of studies were undertaken to unravel its optical properties both experimentally and theoretically. A systematic investigation probing its optical absorption and emission characteristics revealed its strong dependence on solvent polarity, which confirmed the crucial role played by the intra molecular charge transfer (ICT) process emerging from its unique molecular and electronic structure. Such a strong structure–property correlation could be exploited for various optoelectronic applications, which triggered us to explore AA2 for photonics applications driven by nonlinear optical responses. Our nonlinear optical investigations confirmed strong excited state absorption at 532 nm when excited in the nanosecond regime using the Z-scan technique. This opened up yet another avenue of exploring it for optical limiting applications. The estimated optical limiting threshold for an AA2 molecule is about 0.98 J cm⁻², which proved its high suitability for such photonics applications while outperforming many other organic fluorophores and benchmarked nanostructures under similar experimental evaluation. This is one of the very first reports on phenothiazine with an A–π–D–π–A configuration accompanied by excellent nonlinear optical properties.

Sub-micrometer-sized (0.33 μm) polycrystalline powders of calcium copper titanate (CCTO) were synthesized via the solgel method. Compacted calcined powders were sintered at 900 °C/2h to obtain porous (95% density) ceramics. X-ray structural investigations revealed the presence of ZnO or SnO₂ traces in the bulk of the CCTO ceramics. Scanning electron microscopy and energy dispersive spectroscopic studies confirmed the diffusion and the segregation of these oxides at the grain boundaries. The dielectric and varistor properties of the ZnO or SnO₂ diffused samples were found to be superior to that of pristine CCTO ceramics. For instance, the dielectric constant (ɛ′) of ZnO diffused ceramics exhibited a value as high as 2.4 × 10⁴ (1 kHz, at room temperature) and a dielectric loss (D) of 0.059. Similarly, SnO₂ diffused ceramics exhibited a dielectric constant of 2.7 × 10⁴ (1 kHz, at room temperature) associated with a dielectric loss of 0.047. The figure of merit of varistor performance, i.e., nonlinear coefficient (α = 10.6), of the SnO₂ diffused sample is significantly higher than that of ZnO diffused (α = 7.4) and pristine CCTO (α = 4.5) ceramics. The dielectric data obtained for both the pristine and ZnO or SnO₂ diffused ceramics were rationalized by invoking Cole–Cole analysis. The thermal activation energy was estimated from the temperature-dependent dielectric data besides current (I)–voltage (V) characteristics. Equivalent circuit modeling of the Nyquist plots demonstrated that the inclusion of ZnO and SnO₂ layers in CCTO ceramics remarkably improved the grain boundary resistance (R_gb) by 5-fold and 20-fold, respectively, which resulted in making CCTO a better dielectric. This methodology of fabricating ceramics via interfacial engineering could pave the way for obtaining superior CCTO ceramics associated with exotic functional properties.

Background Acetabular morphology has always been an area of interest in patients with slipped capital femoral epiphysis (SCFE). Acetabular retroversion in SCFE is proposed as a pre-disposing factor and also can predispose the hip to pincer impingement. But there is controversial data in literature regarding the acetabular morphology. All available data are from the West and we present the first study on acetabular morphology in Indian children with SCFE. Methods CT scans of 29 patients with SCFE were collected from our database and anteversion was measured as described by Dandachli et al. Lateral central edge angle (LCEA) was measured in the standard AP radiographs of the pelvis. There were 20 boys and 9 girls with a mean age of 13.5. Seven patients had right side SCFE, eighteen had left side SCFE, and four had bilateral involvement. Measurements were done in 33 affected hips and compared with 25 normal hips. Results The mean AVsup and AVcen of affected hips were 6.59° and 13.51°, respectively, and that in normal hips were 8.36° and 14.04° (p > 0.05). The mean LCEA was 23.05° on the affected hips and 25.45° on the normal hips (p > 0.05). The AVsup showed retroversion in 24.24% (8/33) of the affected hips and 20% (5/25) of the normal hips. Though the mean version was less in SCFE hips, it was not statistically significant. Conclusion Our study documents that 25% of hips with SCFE had acetabular retroversion. However, this was not significantly high compared to the contralateral hips.

Purpose: The retrograde urethrogram (RUG) has been a key diagnostic tool for over a century, remaining essential despite the availability of other imaging techniques for screening, diagnosis and follow up of Urethral strictures. However, interpretation of RUG images has to be done manually and needs experience on the part of the treating urologist, which calls for a common understanding of RUGs and presents a chance to improve stricture management in a practical way. Artificial intelligence (AI) algorithms present a novel way to prevent human discrepancy while concomitantly improving the accuracy of stricture identification and classification. Methods: Dataset: We have used a balanced dataset which includes RUGs of 168 strictured cases and 178 non-strictured(healthy) cases. Task#1: The primary requirement is to identify the Urethral region in any clinically obtained RUGs and detect the presence of stricture in it. We successfully deployed a Segmentation and Classification model to categorize the whole dataset as strictured or non-strictured RUGs. Task#2: On obtaining superior accuracy, we effectively went on to identify the type of stricture based on their location, which is of clinical importance. Results: With the above-mentioned available RUG dataset from 346 cases, we could train our Deep learning model and achieve a significant accuracy of 91.53% in detection and categorizing the type of stricture. At the end, a 10-fold cross-validation yielded an accuracy of about 86.66%. Conclusion: Our attempts have successfully validated that using Deep learning (DL) tools, one could readily (i) Detect the presence of stricture in a given RUG and (ii) ultimately locate and classify these strictures effectively. Thus, these Deep learning tools could be of great clinical assistance for Urinary stricture related disease management.