Thiosemicarbazone complexes of the platinum metals. A story of variable coordination modes

Salicylaldehyde thiosemicarbazone (H2saltsc) reacts with [M(PPh3)3X2] (M = Ru, Os; X = Cl, Br) to afford complexes of type [M(PPh3)2(Hsaltsc)2], in which the salicylaldehyde thiosemicarbazone ligand is coordinated to the metal as a bidentate N,S-donor forming a four-membered chelate ring. Reaction of benzaldehyde thiosemicarbazones (Hbztsc-R) with [M(PPh3)3X2] also affords complexes of similar type, viz. [M(PPh3)2(bztsc-R)2], in which the benzaldehyde thiosemicarbazones have also been found to coordinate the metal as a bidentate N,S-donor forming a four-membered chelate ring as before. Reaction of the Hbztsc-R ligands has also been carried out with [M(bpy)2X2] (M = Ru, Os; X = Cl, Br), which has afforded complexes of type [M(bpy)2(bztsc-R)]+, which have been isolated as perchlorate salts. Coordination mode of bztsc-R has been found to be the same as before. Structure of the Hbztsc-OMe ligand has been determined and some molecular modelling studies have been carried out determine the reason for the observed mode of coordination. Reaction of acetone thiosemicarbazone (Hactsc) has then been carried out with [M(bpy)2X2] to afford the [M(bpy)2(actsc)]ClO4complexes, in which the actsc ligand coordinates the metal as a bidentate N,S-donorformingafive-membered chelate ring. Reaction of H2saltsc has been carried out with [Ru(bpy)2Cl2] to prepare the [Ru(bpy)2(Hsaltsc)]ClO4 complex, which has then been reacted with one equivalent of nickel perchlorate to afford an octanuclear complex of type [Ru(bpy)2(saltsc-H)4Ni4](ClO4)4

Isolated Ventricular Noncompaction Syndrome in a Nigerian Male: Case Report and Review of the Literature

Isolated ventricular non-compaction cardiomyopathy (IVNC) is a rare, morphologically distinct primary genetic cardiomyopathy, which is now gaining prominence as an important differential diagnosis in patients presenting with cardiac failure. We describe a case report of a Nigerian male with facial dysmorphism presenting with cardiac failure. This is followed by a review of the literature with focus on the diagnosis of this condition, which may be difficult especially in non-Caucasian populations

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Can patient decision aids reduce decisional conflict in a de-escalation of breast radiotherapy clinical trial? The PRIMETIME Study Within a Trial implemented using a cluster stepped-wedge trial design.

BackgroundFor patients with early breast cancer considered at very-low risk of local relapse, risks of radiotherapy may outweigh the benefits. Decisions regarding treatment omission can lead to patient uncertainty (decisional conflict), which may be lessened with patient decision aids (PDA). PRIMETIME (ISRCTN 41579286) is a UK-led biomarker-directed study evaluating omission of adjuvant radiotherapy in breast cancer; an embedded Study Within A Trial (SWAT) investigated whether PDA reduces decisional conflict using a cluster stepped-wedge trial design.MethodsPDA diagrams and a video explaining risks and benefits of radiotherapy were developed in close collaboration between patient advocates and PRIMETIME trialists. The SWAT used a cluster stepped-wedge trial design, where each cluster represented the radiotherapy centre and referring peripheral centres. All clusters began in the standard information group (patient information and diagrams) and were randomised to cross-over to the enhanced information group (standard information plus video) at 2, 4 or 6 months. Primary endpoint was the decisional conflict scale (0-100, higher scores indicating greater conflict) which was assessed on an individual participant level. Multilevel mixed effects models used a random effect for cluster and a fixed effect for each step to adjust for calendar time and clustering. Robust standard errors were also adjusted for the clustering effect.ResultsFive hundred twenty-one evaluable questionnaires were returned from 809 eligible patients (64%) in 24 clusters between April 2018 and October 2019. Mean decisional conflict scores in the standard group (N = 184) were 10.88 (SD 11.82) and 8.99 (SD 11.82) in the enhanced group (N = 337), with no statistically significant difference [mean difference - 1.78, 95%CI - 3.82-0.25, p = 0.09]. Compliance with patient information and diagrams was high in both groups although in the enhanced group only 121/337 (36%) reported watching the video.ConclusionThe low levels of decisional conflict in PRIMETIME are reassuring and may reflect the high-quality information provision, such that not everyone required the video. This reinforces the importance of working with patients as partners in clinical trials especially in the development of patient-centred information and decision aids

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License