Defining Surgical Difficulty During Open Right Lobe Donor Hepatectomy and its Prediction Using Preoperative Donor Computed Tomography Morphometry.

Background: There is no accepted way to define difficult donor hepatectomy (DiffDH) during open right live donor hepatectomy (ORLDH). There are also no studies exploring association between DiffDH and early donor outcomes or reliable pre-operative predictors of DiffDH. Methods: Consecutive ORLDH performed over 18 months at a single center were included. Intraoperative parameters were used to develop an objective definition of DiffDH. The impact of DiffDH on early postoperative outcomes and achievement of textbook outcome (TO) was evaluated. Donor morphometry data on axial and coronal sections of donor computed tomography (CT) at the level of portal bifurcation were collected. Donor and graft factors predictive of DiffDH were evaluated using univariate and multivariate logistic regression. Results: One-hundred-eleven donors (male: 40.5%, age: 34 ± 9.5 years) underwent ORLDH during the study period. The difficulty score was constructed using five intraoperative parameters, i.e., operating time, transection time, estimated blood loss, need for intraoperative vasopressors, and need for Pringle maneuver. Donors were classified as DiffDH (score ≥ 2) or standard donor hepatectomy (StDH) (score <2). Twenty-nine donors (26%) were classified as DiffDH. DiffDH donors suffered greater all-cause morbidity (P = 0.004) but not major morbidity (Clavien-Dindo score >2; P = 0.651), more perioperative transfusion (P = 0.013), increased postoperative systemic inflammatory response syndrome (P = 0.034), delay in achieving full oral diet (P = 0.047), and a 70% reduced chance of achieving TO as compared to StDH (P = 0.007). On logistic regression analysis, increasing right lobe anteroposterior depth (RLdepth) was identified as an independent predictor of DiffDH (Odds ratio: 2.0 (95% confidence interval = 1.2, 3.3), P < 0.006). Receiver operating characteristic curve analysis identified an RLdepth of >14 cm as the best predictor of DiffDH (sensitivity:79%, specificity: 66%, area under curve = 0.803, P < 0.001). Conclusion: We report a novel definition of DiffDH and show that it is associated with worse postoperative outcomes, including a lesser chance of achieving TO. We also report that DiffDH can be predicted from readily available donor CT parameters.

UBE2D3 facilitates NHEJ by orchestrating ATM signalling through multi-level control of RNF168.

Maintenance of genome integrity requires tight control of DNA damage response (DDR) signalling and repair, with phosphorylation and ubiquitination representing key elements. How these events are coordinated to achieve productive DNA repair remains elusive. Here we identify the ubiquitin-conjugating enzyme UBE2D3 as a regulator of ATM kinase-induced DDR that promotes non-homologous end-joining (NHEJ) at telomeres. UBE2D3 contributes to DDR-induced chromatin ubiquitination and recruitment of the NHEJ-promoting factor 53BP1, both mediated by RNF168 upon ATM activation. Additionally, UBE2D3 promotes NHEJ by limiting RNF168 accumulation and facilitating ATM-mediated phosphorylation of KAP1-S824. Mechanistically, defective KAP1-S824 phosphorylation and telomeric NHEJ upon UBE2D3-deficiency are linked to RNF168 hyperaccumulation and aberrant PP2A phosphatase activity. Together, our results identify UBE2D3 as a multi-level regulator of NHEJ that orchestrates ATM and RNF168 activities. Moreover, they reveal a negative regulatory circuit in the DDR that is constrained by UBE2D3 and consists of RNF168- and phosphatase-mediated restriction of KAP1 phosphorylation.

Computational optical sensing and imaging: introduction to the feature issue.

This joint feature issue of Optics Express and Applied Optics showcases technical innovations by participants of the 2023 topical meeting on Computational Optical Sensing and Imaging and the computational imaging community. The articles included in the feature issue highlight advances in imaging science that emphasize synergistic activities in optics, signal processing and machine learning. The issue features 26 contributed articles that cover multiple themes including non line-of-sight imaging, imaging through scattering media, compressed sensing, lensless imaging, ptychography, computational microscopy, spectroscopy and optical metrology.

Managing Pigment Dispersion Glaucoma Postbilateral ICL Implantation in High Myopia: A Case Report on the Crucial Role of Gonioscopy in Correcting a Misdiagnosis.

Secondary open-angle glaucoma (SOAG) is a rare yet consequential complication following implantable collamer lens (ICL), also known as a phakic intraocular lens insertion, particularly in high myopia patients. This case report emphasizes the importance of recognizing SOAG and details the diagnostic complexities, reevaluation procedures, and successful long-term management of a 24-year-old bilateral high myopia (-7.00 D) patient who initially received an erroneous diagnosis of secondary angle-closure glaucoma (SACG) after ICL insertion at an external medical facility. Persistent visual issues prompted the patient to seek a second opinion, leading to a comprehensive reevaluation that eventually unveiled pigment dispersion syndrome (PDS) as the underlying cause, subsequently resulting in SOAG. This case not only highlights the diagnostic challenges but also elucidates the re-evaluation process and effective 5-year management strategies employed to restore the patient's visual health and quality of life. How to cite this article: Ramesh PV, Parthasarathi S, Azad A, et al. Managing Pigment Dispersion Glaucoma Postbilateral ICL Implantation in High Myopia: A Case Report on the Crucial Role of Gonioscopy in Correcting a Misdiagnosis. J Curr Glaucoma Pract 2024;18(1):31-36.

Child-adult speech diarization in naturalistic conditions of preschool classrooms using room-independent ResNet model and automatic speech recognition-based re-segmentation.

Speech and language development are early indicators of overall analytical and learning ability in children. The preschool classroom is a rich language environment for monitoring and ensuring growth in young children by measuring their vocal interactions with teachers and classmates. Early childhood researchers are naturally interested in analyzing naturalistic vs controlled lab recordings to measure both quality and quantity of such interactions. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to the diversity of acoustic events/conditions in such daylong audio streams, automated speaker diarization technology would need to be advanced to address this challenging domain for segmenting audio as well as information extraction. This study investigates alternate deep learning-based lightweight, knowledge-distilled, diarization solutions for segmenting classroom interactions of 3-5 years old children with teachers. In this context, the focus on speech-type diarization which classifies speech segments as being either from adults or children partitioned across multiple classrooms. Our lightest CNN model achieves a best F1-score of ∼76.0% on data from two classrooms, based on dev and test sets of each classroom. It is utilized with automatic speech recognition-based re-segmentation modules to perform child-adult diarization. Additionally, F1-scores are obtained for individual segments with corresponding speaker tags (e.g., adult vs child), which provide knowledge for educators on child engagement through naturalistic communications. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults. The resulting child communication metrics have been used for broad-based feedback for teachers with the help of visualizations.

Utility of the Growth Differentiation Factor-15 in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid on Cytopathologic and Histopathologic Samples.

Background Follicular-patterned lesions are a major gray zone in thyroid cytopathology. The recently introduced 2022 World Health Organization (WHO) classification emphasizes the importance of genetic alterations in thyroid neoplasms with the introduction of certain newer terminologies that are expected to cause remarkable changes in cytopathologic and histopathologic reporting. Although molecular assays such as the Afirma gene expression classifier and the ThyroSeq are already in use, there has been an ongoing search for further reliable molecular markers. The growth differentiation factor-15 (GDF-15) is one among them. This study aimed to determine the diagnostic utility of GDF-15 mRNA expression in frozen tissue and fine-needle aspiration (FNA) samples from follicular-patterned thyroid lesions and neoplasms. Methodology The real-time quantitative polymerase chain reaction was performed on 75 frozen tissue and FNA samples each from 19 cases of follicular thyroid hyperplasia (FTH), 10 nodular goiters (NGs), 17 follicular thyroid adenomas (FTAs), eight follicular thyroid carcinomas (FTCs), 12 follicular variant of papillary thyroid carcinomas (FVPTCs), and nine classic papillary thyroid carcinomas (CPTCs) that were diagnosed according to the 2017 WHO classification of thyroid neoplasms. The GDF-15 mRNA expression in all these cases was assessed and compared with the control thyroid tissue samples. One-way analysis of variance and the Kruskal-Wallis test were performed using GraphPad Prism 8 software to determine the significance of differences in the GDF-15 mRNA levels among various thyroid lesions. Results A higher GDF-15 mRNA expression was noted in the malignant thyroid neoplasms including FTC, FVPTC, and CPTC in comparison to FTA, with a fold change between the malignant and benign groups being more than 244.18 times. A difference in the fold change was noted between FTH and FTA with an increase in GDF-15 mRNA level in the latter, which was statistically not significant. Conclusions The fact that GDF-15 mRNA was studied both on fine-needle aspiration cytologic and the frozen tissue material and that the majority of the lesions studied were follicular-patterned establishes the GDF-15 as a potential marker not only for diagnosing malignant thyroid neoplasms of the follicular epithelium but also in distinguishing benign and malignant follicular-patterned neoplasms of the thyroid.

Metal peroxides as potential photocatalysts for environmental remediation.

Inorganic oxide materials such as TiO2 and ZnO have been extensively studied for environmental remediation, that operates through photo generated Reactive Oxygen Species (ROS) such as H2O2, ·OH and O2 - to decontaminate waste water. However, inorganic solid oxidants such as metal peroxides capable of generating ROS in aqueous solutions have not been studied for environmental remediation. Towards this objective, we have synthesized peroxides of Zn, Mg, and Ba and characterized these by powder X-ray diffraction, Transmission Electron Microscopy, UV-visible spectroscopy, and X-ray photoelectron spectroscopy. The photocatalytic activity of these wide band gap semiconductors has also been investigated. The novelty of the work is in the use of these peroxides as chemical sources of ROS in aqueous suspensions in addition to their photochemical generation. Hence, these peroxides, in particular Ba, exhibit high photocatalytic activity, better than the well-known ZnO. The mechanisms of ROS generation and subsequent dye degradation are elucidated. ROS has been estimated and is correlated to the photocatalytic activity. This work reports for the first time BaO2 as potential photocatalyst.

Holographic elysium of a 4D ophthalmic anatomical and pathological metaverse with extended reality/mixed reality.

Extended reality is one of the leading cutting-edge technologies, which has not yet fully set foot into the field of ophthalmology. The use of extended reality technology especially in ophthalmic education and counseling will revolutionize the face of teaching and counseling on a whole new level. We have used this novel technology and have created a holographic museum of various anatomical structures such as the eyeball, cerebral venous system, cerebral arterial system, cranial nerves, and various parts of the brain in fine detail. These four-dimensional (4D) ophthalmic holograms created by us (patent pending) are cost-effectively constructed with TrueColor confocal images to serve as a new-age immersive 4D pedagogical and counseling tool for gameful learning and counseling, respectively. According to our knowledge, this concept has not been reported in the literature before.

A comparison of explainable artificial intelligence methods in the phase classification of multi-principal element alloys.

We demonstrate the capabilities of two model-agnostic local post-hoc model interpretability methods, namely breakDown (BD) and shapley (SHAP), to explain the predictions of a black-box classification learning model that establishes a quantitative relationship between chemical composition and multi-principal element alloys (MPEA) phase formation. We trained an ensemble of support vector machines using a dataset with 1,821 instances, 12 features with low pair-wise correlation, and seven phase labels. Feature contributions to the model prediction are computed by BD and SHAP for each composition. The resulting BD and SHAP transformed data are then used as inputs to identify similar composition groups using k-means clustering. Explanation-of-clusters by features reveal that the results from SHAP agree more closely with the literature. Visualization of compositions within a cluster using Ceteris-Paribus (CP) profile plots show the functional dependencies between the feature values and predicted response. Despite the differences between BD and SHAP in variable attribution, only minor changes were observed in the CP profile plots. Explanation-of-clusters by examples show that the clusters that share a common phase label contain similar compositions, which clarifies the similar-looking CP profile trends. Two plausible reasons are identified to describe this observation: (1) In the limits of a dataset with independent and non-interacting features, BD and SHAP show promise in recognizing MPEA composition clusters with similar phase labels. (2) There is more than one explanation for the MPEA phase formation rules with respect to the set of features considered in this work.