Clonal hematopoiesis of indeterminate potential in patients with immunoglobulin light-chain AL amyloidosis.

ABSTRACT: Immunoglobulin light-chain (AL) amyloidosis is characterized by the deposition of misfolded monoclonal free light chains, with cardiac complications accounting for patient mortality. Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with worse cardiovascular outcomes in the general population. Its significance in AL amyloidosis remains unclear. We collected clinical information and outcome data on 76 patients with a diagnosis of AL amyloidosis who underwent deep targeted sequencing for myeloid neoplasia-associated mutations between April 2018 and August 2023. Variant allele frequency was set at 2% to call CHIP-associated mutations. CHIP mutations were present in patients with AL amyloidosis at a higher frequency compared with age-matched control individuals. Sixteen patients (21%) had at least 1 CHIP mutation. DNMT3A was the most frequent mutation (7/16; 44%). Compared with patients without CHIP, patients with CHIP had a higher prevalence of t(11;14) translocation (69% vs 25%, respectively; P = .004). Furthermore, among patients with renal involvement, those with CHIP had a lower Palladini renal stage (P = .001). At a median follow-up of 32.5 months, the presence of CHIP was not associated with worse overall survival or major organ dysfunction progression-free survival. Larger studies and longer follow-up are needed to better define the impact of CHIP in patients with AL amyloidosis.

Manchester Intermittent Diet in Gestational Diabetes Acceptability Study (MIDDAS-GDM): a two-arm randomised feasibility protocol trial of an intermittent low-energy diet (ILED) in women with gestational diabetes and obesity in Greater Manchester.

INTRODUCTION: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy eating and physical activity which is only moderately effective for achieving glycaemic targets. Approximately 30% of women require medication with metformin and/or insulin. There is currently no strong evidence base for any particular dietary regimen to improve outcomes in GDM. Intermittent low-energy diets (ILEDs) are associated with improved glycaemic control and reduced insulin resistance in type 2 diabetes and could be a viable option in the management of GDM. This study aims to test the safety, feasibility and acceptability of an ILED intervention among women with GDM compared with best National Health Service (NHS) care. METHOD AND ANALYSIS: We aim to recruit 48 women with GDM diagnosed between 24 and 30 weeks gestation from antenatal clinics at Wythenshawe and St Mary's hospitals, Manchester Foundation Trust, over 13 months starting in November 2022. Participants will be randomised (1:1) to ILED (2 low-energy diet days/week of 1000 kcal and 5 days/week of the best NHS care healthy diet and physical activity advice) or best NHS care 7 days/week until delivery of their baby. Primary outcomes include uptake and retention of participants to the trial and adherence to both dietary interventions. Safety outcomes will include birth weight, gestational age at delivery, neonatal hypoglycaemic episodes requiring intervention, neonatal hyperbilirubinaemia, admission to special care baby unit or neonatal intensive care unit, stillbirths, the percentage of women with hypoglycaemic episodes requiring third-party assistance, and significant maternal ketonaemia (defined as ≥1.0 mmol/L). Secondary outcomes will assess the fidelity of delivery of the interventions, and qualitative analysis of participant and healthcare professionals' experiences of the diet. Exploratory outcomes include the number of women requiring metformin and/or insulin. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Cambridge East Research Ethics Committee (22/EE/0119). Findings will be disseminated via publication in peer-reviewed journals, conference presentations and shared with diabetes charitable bodies and organisations in the UK, such as Diabetes UK and the Association of British Clinical Diabetologists. TRIAL REGISTRATION NUMBER: NCT05344066.

Clarifying status of DNNs as models of human vision.

On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.

Accurate staging of chick embryonic tissues via deep learning of salient features.

Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitating precise and unbiased staging. Here, we investigated whether we could train a deep convolutional neural network to sub-stage HH10 chick brains using a small dataset of 151 expertly labelled images. By augmenting our images with biologically informed transformations and data-driven preprocessing steps, we successfully trained a classifier to sub-stage HH10 brains to 87.1% test accuracy. To determine whether our classifier could be generally applied, we re-trained it using images (269) of randomised control and experimental chick wings, and obtained similarly high test accuracy (86.1%). Saliency analyses revealed that biologically relevant features are used for classification. Our strategy enables training of image classifiers for various applications in developmental biology with limited microscopy data.

Characterizing dry mass and volume changes in human multiple myeloma cells upon treatment with proteotoxic and genotoxic drugs.

Multiple myeloma (MM) is a cancer of terminally differentiated plasma cells. MM remains incurable, but overall survival of patients has progressively increased over the past two decades largely due to novel agents such as proteasome inhibitors (PI) and the immunomodulatory agents. While these therapies are highly effective, MM patients can be de novo resistant and acquired resistance with prolonged treatment is inevitable. There is growing interest in early, accurate identification of responsive versus non-responsive patients; however, limited sample availability and need for rapid assays are limiting factors. Here, we test dry mass and volume as label-free biomarkers to monitor early response of MM cells to treatment with bortezomib, doxorubicin, and ultraviolet light. For the dry mass measurement, we use two types of phase-sensitive optical microscopy techniques: digital holographic tomography and computationally enhanced quantitative phase microscopy. We show that human MM cell lines (RPMI8226, MM.1S, KMS20, and AMO1) increase dry mass upon bortezomib treatment. This dry mass increase after bortezomib treatment occurs as early as 1 h for sensitive cells and 4 h for all tested cells. We further confirm this observation using primary multiple myeloma cells derived from patients and show that a correlation exists between increase in dry mass and sensitivity to bortezomib, supporting the use of dry mass as a biomarker. The volume measurement using Coulter counter shows a more complex behavior; RPMI8226 cells increase the volume at an early stage of apoptosis, but MM.1S cells show the volume decrease typically observed with apoptotic cells. Altogether, this cell study presents complex kinetics of dry mass and volume at an early stage of apoptosis, which may serve as a basis for the detection and treatment of MM cells.

Acinetobacter baumannii from grass: novel but non-resistant clones.

Acinetobacter baumannii is one the most worrisome nosocomial pathogens, which has long been considered almost mainly as a hospital-associated bacterium. There have been some studies about animal and environmental isolates over the last decade. However, little effort has been made to determine if this pathogen dwells in the grass. Here, we aim to determine the evolutionary relationships and antibiotic resistance of clones of A. baumannii sampled from grass to the major human international clones and animal clones. Two hundred and forty genomes were considered in total from four different sources for this study. Our core and accessory genomic epidemiology analyses showed that grass isolates cluster in seven groups well differentiated from one another and from the major human and animal isolates. Furthermore, we found new sequence types under both multilocus sequence typing schemes: two under the Pasteur scheme and seven for the Oxford scheme. The grass isolates contained fewer antibiotic-resistance genes and were not resistant to the antibiotics tested. Our results demonstrate that these novel clones appear to have limited antibiotic resistance potential. Given our findings, we propose that genomic epidemiology and surveillance of A. baumannii should go beyond the hospital settings and consider the environment in an explicit One Health approach.

The Acinetobacter baumannii website (Ab-web): a multidisciplinary knowledge hub, communication platform, and workspace.

Acinetobacter baumannii is a Gram-negative bacterium increasingly implicated in hospital-acquired infections and outbreaks. Effective prevention and control of such infections are commonly challenged by the frequent emergence of multidrug-resistant strains. Here we introduce Ab-web (https://www.acinetobacterbaumannii.no), the first online platform for sharing expertise on A. baumannii. Ab-web is a species-centric knowledge hub, initially with 10 articles organized into two main sections, 'Overview' and 'Topics', and three themes, 'epidemiology', 'antibiotic resistance', and 'virulence'. The 'workspace' section provides a spot for colleagues to collaborate, build, and manage joint projects. Ab-web is a community-driven initiative amenable to constructive feedback and new ideas.

Knowledge, attitudes and behaviour of Egyptians towards antibiotic use in the community: can we do better?

BACKGROUND: Infectious diseases are among the leading causes of death worldwide. This is concerning because of the increasing capacity of the pathogens to develop antibiotic resistance. Antibiotic overuse and misuse remain the main drivers of resistance development. In the USA and Europe, annual campaigns raise awareness of antibiotic misuse hazards and promote their judicial use. Similar efforts are lacking in Egypt. This study assessed the knowledge of the public in Alexandria, Egypt of antibiotic misuse risks and their habits towards antibiotic use, in addition to conducting a campaign to increase awareness of the safe use of antibiotics. METHODS: A questionnaire assessing knowledge, attitudes and behaviour towards antibiotics was used to collect responses from study participants at various sports clubs in Alexandria in 2019. An awareness campaign to correct misconceptions and a post awareness survey followed. RESULTS: Most of the participants were well-educated (85%), in their middle age (51%) and took antibiotics last year (80%). 22% would take an antibiotic for common cold. This dropped to 7% following the awareness. There was a 1.6 time increase in participants who would start an antibiotic on a healthcare professional's advice following the campaign. A 1.3 time increase in participants who would finish an antibiotic regimen was also observed. The campaign made all participants recognize that unwise antibiotic use is harmful to them or others; and 1.5 more participants would spread the word about antibiotic resistance. Despite learning of the risks of antibiotic use, there was no change in how often participants thought they should take antibiotics. CONCLUSIONS: Although awareness of antibiotic resistance is rising, some wrong perceptions hold fast. This highlights the need for patient and healthcare-tailored awareness sessions as part of a structured and national public health program directed to the Egyptian population.

Bounded strategic reasoning explains crisis emergence in multi-agent market games.

The efficient market hypothesis (EMH), based on rational expectations and market equilibrium, is the dominant perspective for modelling economic markets. However, the most notable critique of the EMH is the inability to model periods of out-of-equilibrium dynamics without significant external news. When such dynamics emerge endogenously, the traditional economic frameworks prove insufficient. This work offers an alternate perspective explaining the endogenous emergence of punctuated out-of-equilibrium dynamics based on bounded rational agents. In a concise market entrance game, we show how boundedly rational strategic reasoning can lead to endogenously emerging crises, exhibiting fat tails in returns. We also show how other common stylized facts, such as clustered volatility, arise due to agent diversity (or lack thereof) and the varying learning updates across the agents. This work explains various stylized facts and crisis emergence in economic markets, in the absence of any external news, based on agent interactions and bounded rational reasoning.

The role of capacity constraints in Convolutional Neural Networks for learning random versus natural data.

Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks is extremely limited, even with few stimuli. We argue that the performance difference is due to CNNs' overcapacity and introduce biologically inspired mechanisms to constrain it, while retaining the good test set generalisation to structured images as characteristic of CNNs. We investigate the efficacy of adding noise to hidden units' activations, restricting early convolutional layers with a bottleneck, and using a bounded activation function. Internal noise was the most potent intervention and the only one which, by itself, could reduce random data performance in the tested models to chance levels. We also investigated whether networks with biologically inspired capacity constraints show improved generalisation to out-of-distribution stimuli, however little benefit was observed. Our results suggest that constraining networks with biologically motivated mechanisms paves the way for closer correspondence between network and human performance, but the few manipulations we have tested are only a small step towards that goal.

Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations.

The incidence of cutaneous T-cell lymphoma (CTCL) increases with age, and blood involvement portends a worse prognosis. To advance our understanding of the development of CTCL and identify potential therapeutic targets, we performed integrative analyses of paired single-cell RNA and T-cell receptor (TCR) sequencing of peripheral blood CD4+ T cells from patients with CTCL to reveal disease-unifying features. The malignant CD4+ T cells of CTCL showed highly diverse transcriptomic profiles across patients, with most displaying a mature Th2 differentiation and T-cell exhaustion phenotype. TCR-CDR3 peptide prediction analysis suggested limited diversity between CTCL samples, consistent with a role for a common antigenic stimulus. Potential of heat diffusion for affinity-based trajectory embedding transition analysis identified putative precancerous circulating populations characterized by an intermediate stage of gene expression and mutation level between the normal CD4+ T cells and malignant CTCL cells. We further revealed the therapeutic potential of targeting CD82 and JAK that endow the malignant CTCL cells with survival and proliferation advantages.

Dissecting molecular mechanisms of immune microenvironment dysfunction in multiple myeloma and precursor conditions.

Multiple myeloma (MM) is a disease of clonally differentiated plasma cells. MM is almost always preceded by precursor conditions, monoclonal gammopathy of unknown significance (MGUS), and smoldering MM (SMM) through largely unknown molecular events. Genetic alterations of the malignant plasma cells play a critical role in patient clinical outcomes. Del(17p), t(4;14), and additional chromosomal alterations such as del(1p32), gain(1q) and MYC translocations are involved in active MM evolution. Interestingly, these genetic alterations appear strikingly similar in transformed plasma cell (PC) clones from MGUS, SMM, and MM stages. Recent studies show that effectors of the innate and adaptive immune response show marked dysfunction and skewing towards a tolerant environment that favors disease progression. The MM myeloid compartment is characterized by myeloid-derived suppressor cells (MDSCs), dendritic cells as well as M2-like phenotype macrophages that promote immune evasion. Major deregulations are found in the lymphoid compartment as well, with skewing towards immune tolerant Th17 and Treg and inhibition of CD8+ cytotoxic and CD4+ activated effector T cells. In summary, this review will provide an overview of the complex cross-talk between MM plasma cells and immune cells in the microenvironment and the molecular mechanisms promoting progression from precursor states to full-blown myeloma.

Deep problems with neural network models of human vision.

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

OXA-66 structure and oligomerisation of OXAAb enzymes.

The OXA ß-lactamases are responsible for hydrolysing ß-lactam antibiotics and contribute to the multidrug-resistant phenotype of several major human pathogens. The OXAAb enzymes are intrinsic to Acinetobacter baumannii and can confer resistance to carbapenem antibiotics. Here we determined the structure of the most prevalent OXAAb enzyme, OXA-66. The structure of OXA-66 was solved at a resolution of 2.1 Å and found to be very similar to the structure of OXA-51, the only other OXAAb enzyme that has had its structure solved. Our data contained one molecule per asymmetric unit, and analysis of positions responsible for dimer formation in other OXA enzymes suggest that OXA-66 likely exists as a monomer.

Arms race and fluctuating selection dynamics in Pseudomonas aeruginosa bacteria coevolving with phage OMKO1.

Experimental evolution studies have examined coevolutionary dynamics between bacteria and lytic phages, where two models for antagonistic coevolution dominate: arms-race dynamics (ARD) and fluctuating-selection dynamics (FSD). Here, we tested the ability for Pseudomonas aeruginosa to coevolve with phage OMKO1 during 10 passages in the laboratory, whether ARD versus FSD coevolution occurred, and how coevolution affected a predicted phenotypic trade-off between phage resistance and antibiotic sensitivity. We used a unique "deep" sampling design, where 96 bacterial clones per passage were obtained from the three replicate coevolving communities. Next, we examined phenotypic changes in growth ability, susceptibility to phage infection and resistance to antibiotics. Results confirmed that the bacteria and phages coexisted throughout the study with one community undergoing ARD, whereas the other two showed evidence for FSD. Surprisingly, only the ARD bacteria demonstrated the anticipated trade-off. Whole genome sequencing revealed that treatment populations of bacteria accrued more de novo mutations, relative to a control bacterial population. Additionally, coevolved bacteria presented mutations in genes for biosynthesis of flagella, type-IV pilus and lipopolysaccharide, with three mutations fixing contemporaneously with the occurrence of the phenotypic trade-off in the ARD-coevolved bacteria. Our study demonstrates that both ARD and FSD coevolution outcomes are possible in a single interacting bacteria-phage system and that occurrence of predicted phage-driven evolutionary trade-offs may depend on the genetics underlying evolution of phage resistance in bacteria. These results are relevant for the ongoing development of lytic phages, such as OMKO1, in personalized treatment of human patients, as an alternative to antibiotics.

Risk factors for the development of orthostatic hypotension during autologous stem cell transplant in patients with multiple myeloma.

Orthostatic hypotension (OH) is a well-recognized phenomenon occurring in multiple myeloma (MM) patients undergoing autologous stem cell transplant (ASCT), and is associated with significant morbidity and mortality. A retrospective analysis of patients admitted for first ASCT between June 2012 and April 2014 found that 161/222 (73%) patients were diagnosed with OH during the course of ASCT, including 51 patients who were found to have OH on the day of first orthostatic vitals check. Excluding these 51 patients, 110/171 (64%) patients developed OH during the peri-transplant period, at a median of 7 days post ASCT (95% CI: 6.5-8.5). OH did not significantly impact length of hospitalization, progression free and overall survival. Multivariable analysis revealed four risk factors (i.e. ≥0.5% weight loss/day, white race, gabapentin, antihypertensives) and two protective factors (i.e. antihistamine, proton pump inhibitor) associated with the development of peri-transplant OH.

Acinetobacter baumannii Sampled from Cattle and Pigs Represent Novel Clones.

Acinetobacter baumannii is a very important human pathogen. Nonetheless, we know very little about nonhuman isolates of A. baumannii. Here, we determine the genomic identity of 15 Scottish cattle and pig isolates, as well as their antibiotic and virulence genetic determinants, and compare them with 148 genomes from the main human clinical international clones. Our results demonstrate that cattle and pig isolates represent novel clones well separated from the major international clones. Furthermore, these new clones showed fewer antibiotic resistance genes and may have fewer virulence genes than human clinical isolates. IMPORTANCE Over the last decades, huge amounts of information have been obtained for clinical isolates of A. baumannii and the clones they belong to. In contrast, very little is known about the genomic identity and the genomic basis for virulence and resistance of animal isolates. To fulfil this gap, we conducted a genomic epidemiology study of 15 Scottish cattle and pig isolates in the context of almost 150 genomes belonging to the main international clones of A. baumannii. Our findings show that these animal isolates represent novel clones clearly different from the major international clones. Furthermore, these new clones are distinct in nature considering both antibiotic resistance and virulence when compared with their human clinical counterparts.

Genomic Diversity of Bacteriophages Infecting the Genus Acinetobacter.

The number of sequenced Acinetobacter phage genomes in the International Nucleotide Sequence Database Collaboration has increased significantly in recent years, from 37 in 2017 to a total of 139 as of January 2021 with genome sizes ranging from 31 to 378 kb. Here, we explored the genetic diversity of the Acinetobacter phages using comparative genomics approaches that included assessment of nucleotide similarity, shared gene content, single gene phylogeny, and the network-based classification tool vConTACT2. Phages infecting Acinetobacter sp. are genetically diverse and can be grouped into 8 clusters (subfamilies) and 46 sub-clusters (genera), of which 8 represent genomic singletons (additional genera). We propose the creation of five new subfamilies and suggest a reorganisation of the genus Obolenskvirus. These results provide an updated view of the viruses infecting Acinetobacter species, providing insights into their diversity.

Biological convolutions improve DNN robustness to noise and generalisation.

Deep Convolutional Neural Networks (DNNs) have achieved superhuman accuracy on standard image classification benchmarks. Their success has reignited significant interest in their use as models of the primate visual system, bolstered by claims of their architectural and representational similarities. However, closer scrutiny of these models suggests that they rely on various forms of shortcut learning to achieve their impressive performance, such as using texture rather than shape information. Such superficial solutions to image recognition have been shown to make DNNs brittle in the face of more challenging tests such as noise-perturbed or out-of-distribution images, casting doubt on their similarity to their biological counterparts. In the present work, we demonstrate that adding fixed biological filter banks, in particular banks of Gabor filters, helps to constrain the networks to avoid reliance on shortcuts, making them develop more structured internal representations and more tolerance to noise. Importantly, they also gained around 20-35% improved accuracy when generalising to our novel out-of-distribution test image sets over standard end-to-end trained architectures. We take these findings to suggest that these properties of the primate visual system should be incorporated into DNNs to make them more able to cope with real-world vision and better capture some of the more impressive aspects of human visual perception such as generalisation.