SPIGELIAN HERNIA VARIOUS PRESENTATION AND MANAGEMENT- A CASE SERIES

BACKGROUND: Spigelian hernia is rare and requires high index of suspicion. Given the case of inconsistent ndings and signs the diagnosis of spigelian hernia presents with great difculties than its treatment. Incidence of spigelian hernia ranges from 0.1% to 2% of all abdominal hernias AIM: Aim of this case series is to analyze epidemiological aspects, clinical presentation, challenges in diagnosis, surgical technique characteristics, morbidities and hospital stay MATERIALS AND METHODS: A total number of 4 patients who had undergone surgery for spigelian hernia for a period of 6 months from the month of Jan to June 2021 in the dept of general surgery in KMCH were studied. All 4 cases have been analyzed in this study period and followed up until discharge from the hospital. RESULTS: This case series consisted of 4 cases. All cases presented with pain. Only 2 among 4 cases presented with swelling. Denitive diagnosis of all cases could be done only by CT. Out of these 1 was taken up as emergency.Intraoperatively 3 cases were interstitial and 1 was subcutaneous type. All 4 cases were treated by open surgical technique and by placing mesh at different planes CONCLUSION: Diagnosis of spigelian hernia presents with great difculties and hence one must be aware of the clinical presentation. Pain is the consistent feature of all hernias and only occasionally swelling is present which may mislead easily. Computed tomography of abdomen and pelvis still prevails as the denitive radiological investigation in the diagnosis of spigelian hernia. There are many planes at which mesh can be placed for repair where the operative techniques vary. Though the outcome was good in all four cases discussed here, the post operative complication and duration of stay was considerably less when the mesh was placed in the intermuscular plane.

Deep Learning for Latent Space Data Assimilation LSDA in Subsurface Flow Systems

We present a deep learning architecture for efficient reduced-order implementation of ensemble data assimilation. Specifically, deep learning is used to improve two important aspects of data assimilation workflows: (i) low-rank representation of complex reservoir property distributions for geologically consistent feature-based model updating, and (ii) efficient prediction of the statistical information that are required for model updating. The proposed method uses deep convolutional autoencoders to nonlinearly map the original complex and high-dimensional parameters onto a low-dimensional parameter latent space that compactly represents the original parameters. In addition, a low-dimensional data latent space is constructed to predict the observable response of each model parameter realization, which can be used to compute the statistical information needed for the data assimilation step. The two mappings are developed as a joint deep learning architecture with two autoencoders that are connected and trained together. The training uses an ensemble of model parameters and their corresponding production response predictions as needed in implementing the standard ensemble-based data assimilation frameworks. Simultaneous training of the two mappings leads to a joint data-parameter manifold that captures the most salient information in the two spaces for a more effective data assimilation, where only relevant data and parameter features are included. Moreover, the parameter-to-data mapping provides a fast forecast model that can be used to increase the ensemble size for a more accurate data assimilation, without a major computational overhead. We implement the developed approach to a series of numerical experiments, including a 3D example based on the Volve field in the North Sea. For data assimilation methods that involve iterative schemes, such as ensemble smoothers with multiple data assimilation or iterative forms of ensemble Kalman filter, the proposed approach offers a computationally competitive alternative. Our results show that a fully low-dimensional implementation of ensemble data assimilation using deep learning architectures offers several advantages compared to standard algorithms, including joint data-parameter reduction that respects the salient features in each space, geologically consistent feature-based updates, increased ensemble sizes to improve the accuracy and computational efficiency of the calculated statistics for the update step.

Classification of Functional Movement Disorders with Resting State fMRI

Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not recognize as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier via support vector machine (SVM) to accurately classify 61 FMD patients from 59 healthy controls using features derived from resting state functional MRI (rs-fMRI). First, we selected 66 seed regions based on prior related studies, then calculated the full correlation matrix between them, before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. We identified 29 features of interest that were highly predictive of FMD condition, classifying patients from controls with 80% accuracy. The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward and sensorimotor integration, potentially mediating relationships between regions associated with motor function, attention and executive function. Exploratory machine learning was able to identify this distinctive, abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients.

Infection-related immunoglobulin A nephropathy or IgA-dominant postinfectious glomerulonephritis; what is in a name?

Immunoglobulin A nephropathy (IgAN) is the most common glomerulonephritis worldwide. However, its incidence and prevalence vary depending on racial and geographical factors. IgAN is a highly heterogeneous disease with wide clinical and pathological variability. The defining and consistent feature of IgAN is the dominance or co-dominance of IgA deposits in the glomeruli on immunofluorescence (IF) microscopy. However, recent reports suggest that a number of post-infectious glomerulonephritis (PIGN) cases also exhibit dominance or co-dominance of IgA deposits on IF microscopy. Therefore, a debate has arisen on labeling these cases either as infection-related IgAN (a form of secondary IgAN) or IgA-dominant PIGN. Although the majority favors the later nosology, this issue has remained unresolved, as is the issue of labelling this condition as PIGN when, in fact, the infection is often intercurrent, and no latent period is found in this condition. This brief narrative review aims to discuss the salient features of this condition and issues related to its nomenclature.

Sex-specific ornament evolution is a consistent feature of climatic adaptation across space and time in dragonflies

Adaptation to different climates fuels the origins and maintenance of biodiversity. Detailing how organisms optimize fitness for their local climates is therefore an essential goal in biology. Although we increasingly understand how survival-related traits evolve as organisms adapt to climatic conditions, it is unclear whether organisms also optimize traits that coordinate mating between the sexes. Here, we show that dragonflies consistently adapt to warmer climates across space and time by evolving less male melanin ornamentation—a mating-related trait that also absorbs solar radiation and heats individuals above ambient temperatures. Continent-wide macroevolutionary analyses reveal that species inhabiting warmer climates evolve less male ornamentation. Community-science observations across 10 species indicate that populations adapt to warmer parts of species’ ranges through microevolution of smaller male ornaments. Observations from 2005 to 2019 detail that contemporary selective pressures oppose male ornaments in warmer years; and our climate-warming projections predict further decreases by 2070. Conversely, our analyses show that female ornamentation responds idiosyncratically to temperature across space and time, indicating the sexes evolve in different ways to meet the demands of the local climate. Overall, these macro- and microevolutionary findings demonstrate that organisms predictably optimize their mating-related traits for the climate just as they do their survival-related traits.

DNA damage as a mechanism of neurodegeneration in ALS and a contributor to astrocyte toxicity

Increasing evidence supports the involvement of DNA damage in several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Elevated levels of DNA damage are consistently observed in both sporadic and familial forms of ALS and may also play a role in Western Pacific ALS, which is thought to have an environmental cause. The cause of DNA damage in ALS remains unclear but likely differs between genetic subgroups. Repeat expansion in the C9ORF72 gene is the most common genetic cause of familial ALS and responsible for about 10% of sporadic cases. These genetic mutations are known to cause R-loops, thus increasing genomic instability and DNA damage, and generate dipeptide repeat proteins, which have been shown to lead to DNA damage and impairment of the DNA damage response. Similarly, several genes associated with ALS including TARDBP, FUS, NEK1, SQSTM1 and SETX are known to play a role in DNA repair and the DNA damage response, and thus may contribute to neuronal death via these pathways. Another consistent feature present in both sporadic and familial ALS is the ability of astrocytes to induce motor neuron death, although the factors causing this toxicity remain largely unknown. In this review, we summarise the evidence for DNA damage playing a causative or secondary role in the pathogenesis of ALS as well as discuss the possible mechanisms involved in different genetic subtypes with particular focus on the role of astrocytes initiating or perpetuating DNA damage in neurons.

MAN1B1-CDG: novel patients and novel variant

Objectives Congenital disorders of glycosylation (CDGs) are a group of genetic disorders due to hypoglycosylation of proteins and lipids. A type I pattern is associated with defects in glycan assembly and transfer (CDG-I; cytosol; and endoplasmic reticulum defects), a type II pattern is seen in processing defects of the Golgi apparatus. MAN1B1-CDG is an autosomal recessive CDG-II due to mutations in the α 1,2-mannosidase gene (MAN1B1), mainly characterized by psychomotor disability, facial dysmorphism, truncal obesity, and hypotonia. Case presentation Three patients (two males and one female), with MAN1B1-CDG who had elevated transaminase levels are presented. All patients had presented due to dysmorphic and neurological findings and hypertransaminasemia was remarkable. A type 2 pattern was found on serum transferrin isoelectrofocusing analysis of the presented cases. MAN1B1-CDG was confirmed by genetic analysis. Conclusions Although the cause of the increased serum transaminase levels in the present patients is not clear, no evidence for an infection or underlying liver pathology could be identified. In order to know if this is a consistent feature, we suggest measuring serum transaminase levels regularly in MAN1B1-CDG patients.

Upregulation of Local Hepcidin Contributes to Iron Accumulation in Alzheimer’s Disease Brains

Background: Accumulation of iron is a consistent feature of Alzheimer’s disease (AD) brains. The underlying cause, however, remains debatable. Objective: To explore whether local hepcidin synthesized by brain cells contributes to iron accumulation in AD brains. Methods: Brain tissue from the cingulate cortex of 33 cases of AD pre-assigned to Braak stage I-VI, 6 cases of non-dementia, and 15 cases of non-AD dementia were analyzed for transcriptional upregulation of hepcidin by RT-qPCR and RT-PCR. Change in the expression of ferritin, ferroportin (Fpn), microglial activation marker Iba1, IL-6, and TGFβ2 was determined by western blotting. Total tissue iron was determined by colorimetry. Results: Significant transcriptional upregulation of hepcidin was observed in Braak stage III-VI relative to Braak stage I and II, non-AD dementia, and non-dementia samples. Ferritin was increased in Braak stage V, and a significant increase in tissue iron was evident in Braak stage III-VI. The expression of Iba1 and IL-6 was also increased in Braak stage III-VI relative to Braak stage I and II and non-AD dementia samples. Amyloid-β plaques were absent in most Braak stage I and II samples, and present in Braak stage III-VI samples with few exceptions. Conclusion: These observations suggest that upregulation of brain hepcidin is mediated by IL-6, a known transcriptional activator of hepcidin. The consequent downregulation of Fpn on neuronal and other cells results in accumulation of iron in AD brains. The increase in hepcidin is disease-specific, and increases with disease progression, implicating AD-specific pathology in the accumulation of iron.

Region-Based Static Video Stitching for Reduction of Parallax Distortion

In this paper, we propose a semantic segmentation-based static video stitching method to reduce parallax and misalignment distortion for sports stadium scenes with dynamic foreground objects. First, video frame pairs for stitching are divided into segments of different classes through semantic segmentation. Region-based stitching is performed on matched segment pairs, assuming that segments of the same semantic class are on the same plane. Second, to prevent degradation of the stitching quality of plain or noisy videos, the homography for each matched segment pair is estimated using the temporally consistent feature points. Finally, the stitched video frame is synthesized by stacking the stitched matched segment pairs and the foreground segments to the reference frame plane by descending order of the area. The performance of the proposed method is evaluated by comparing the subjective quality, geometric distortion, and pixel distortion of video sequences stitched using the proposed and conventional methods. The proposed method is shown to reduce parallax and misalignment distortion in segments with plain texture or large parallax, and significantly improve geometric distortion and pixel distortion compared to conventional methods.

From Unspeakability to Inequality Talk: Why Conversations about Inequalities May Not Lead to Change

This article draws on eighteen qualitative in-depth interviews with female, early-career classical musicians to investigate if, and if so in which ways, recent discourse around the lack of diversity in the classical music profession has affected how young musicians talk about inequalities in the field of classical music. The article demonstrates that the research participants were aware of ongoing inequalities and discussed them openly. This marks an important shift from previously conducted research, which highlighted the ‘unspeakability’ of inequalities in the classical music profession and the cultural and creative industries. By drawing on discursive psychology, this article explores the rhetorical and ideological work that such ‘inequality talk’ performs, arguing that conversations about inequalities do not necessarily lead to political change. Divided into three analytical sections, the article demonstrates that inequality talk can become an end in itself, rather than a means to an end (such as political change); that a fatalist sentiment can characterise discussions of inequalities, presenting structural change as unachievable; and that acknowledgement and recognition of privilege, crucial to overcoming inequalities, is not a consistent feature of inequality talk, which in turn risks reinforcing the normativity of whiteness and middle-classness in the field of classical music. Overall, the article provides a detailed analysis of recently collected empirical data to caution against overly optimistic accounts of the shift towards a more open discussion of inequalities in the classical music profession and beyond.