Splenectomy for Children and Adults with Persistent and Chronic Immune Thrombocytopenia: Long term results.

BACKGROUND : Although immune thrombocytopenia (ITP) is a common cause of thrombocytopenia among both children and adults, information regarding prognostic determinants of outcome of splenectomy and the management of patients who fail to respond to splenectomy is limited. AIMS AND OBJECTIVES : To analyze the response to splenectomy and to assess the response to treatment of refractory ITP post splenectomy, in children and adults with persistent and chronic ITP in our institution. METHODOLOGY : All patients with persistent and chronic ITP seen in the Department of Haematology between 1995 and 2009 who underwent splenectomy and whose data could be retrieved were analyzed. RESULTS : Of the 167 adults and 87 children, 82.6% of adults and 88.5% of children were in response at 2 months post splenectomy. After a median follow up of 18.6 months (range:1-170) in adults and 12 months (range: 1-173) in children post splenectomy, 115 (68.9%) adults and 63 (72.4%) children showed a response, while 48 (28.7%) adults & 23 (26.4%) children were refractory to splenectomy. The overall mortality was 6 (3.6%) in adults and 1 (1.2%) in children. Among those who were refractory to splenectomy, subsequent drug treatment resulted in response in additional 25 (15%) of adults and 16 (17.2%) of children. The 5 & 10 year event free survival of total cases were 75.2 ± 3.7% & 71.5 ± 4.5% in adults and 79.1 ± 4.6% & 70 ± 7.5% in children respectively. Among adults, females and those who had higher platelet count at splenectomy had a higher chance of response to splenectomy. While in children, persistent phase of the disease was associated with response. The median time to post splenectomy response was shorter and the median peak platelet count was higher among those who showed sustained response in both groups. CONCLUSION : In this first large series form India, comprehensively analyzing outcome of patients with ITP undergoing splenectomy, the response to treatment is similar to those reported in the literature in both adults and children from other population. Further analysis needs to be done to ascertain the predictive value of the variables associated with response

Association of Cerebral Ischemia With Corneal Nerve Loss and Brain Atrophy in MCI and Dementia

IntroductionThis study assessed the association of cerebral ischemia with neurodegeneration in mild cognitive impairment (MCI) and dementia.MethodsSubjects with MCI, dementia and controls underwent assessment of cognitive function, severity of brain ischemia, MRI brain volumetry and corneal confocal microscopy.ResultsOf 63 subjects with MCI (n = 44) and dementia (n = 19), 11 had no ischemia, 32 had subcortical ischemia and 20 had both subcortical and cortical ischemia. Brain volume and corneal nerve measures were comparable between subjects with subcortical ischemia and no ischemia. However, subjects with subcortical and cortical ischemia had a lower hippocampal volume (P < 0.01), corneal nerve fiber length (P < 0.05) and larger ventricular volume (P < 0.05) compared to those with subcortical ischemia and lower corneal nerve fiber density (P < 0.05) compared to those without ischemia.DiscussionCerebral ischemia was associated with cognitive impairment, brain atrophy and corneal nerve loss in MCI and dementia

Artificial Intelligence Based Analysis of Corneal Confocal Microscopy Images for Diagnosing Peripheral Neuropathy: A Binary Classification Model

Diabetic peripheral neuropathy (DPN) is the leading cause of neuropathy worldwide resulting in excess morbidity and mortality. We aimed to develop an artificial intelligence deep learning algorithm to classify the presence or absence of peripheral neuropathy (PN) in participants with diabetes or pre-diabetes using corneal confocal microscopy (CCM) images of the sub-basal nerve plexus. A modified ResNet-50 model was trained to perform the binary classification of PN (PN+) versus no PN (PN-) based on the Toronto consensus criteria. A dataset of 279 participants (149 PN-, 130 PN+) was used to train (n = 200), validate (n = 18), and test (n = 61) the algorithm, utilizing one image per participant. The dataset consisted of participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141), and pre-diabetes (n = 50). The algorithm was evaluated using diagnostic performance metrics and attribution-based methods (gradient-weighted class activation mapping (Grad-CAM) and Guided Grad-CAM). In detecting PN+, the AI-based DLA achieved a sensitivity of 0.91 (95%CI: 0.79-1.0), a specificity of 0.93 (95%CI: 0.83-1.0), and an area under the curve (AUC) of 0.95 (95%CI: 0.83-0.99). Our deep learning algorithm demonstrates excellent results for the diagnosis of PN using CCM. A large-scale prospective real-world study is required to validate its diagnostic efficacy prior to implementation in screening and diagnostic programmes

Corneal nerve and brain imaging in mild cognitive impairment and dementia

Background: Visual rating of medial temporal lobe atrophy (MTA) is an accepted structural neuroimaging marker of Alzheimer's disease. Corneal confocal microscopy (CCM) is a non-invasive ophthalmic technique that detects neuronal loss in peripheral and central neurodegenerative disorders. Objective: To determine the diagnostic accuracy of CCM for mild cognitive impairment (MCI) and dementia compared to medial temporal lobe atrophy (MTA) rating on MRI. Methods: Subjects aged 60-85 with no cognitive impairment (NCI), MCI, and dementia based on the ICD-10 criteria were recruited. Subjects underwent cognitive screening, CCM, and MTA rating on MRI. Results: 182 subjects with NCI (n = 36), MCI (n = 80), and dementia (n = 66), including AD (n = 19, 28.8%), VaD (n = 13, 19.7%), and mixed AD (n = 34, 51.5%) were studied. CCM showed a progressive reduction in corneal nerve fiber density (CNFD, fibers/mm2) (32.0±7.5 versus 24.5±9.6 and 20.8±9.3, p < 0.0001), branch density (CNBD, branches/mm2) (90.9±46.5 versus 59.3±35.7 and 53.9±38.7, p < 0.0001), and fiber length (CNFL, mm/mm2) (22.9±6.1 versus 17.2±6.5 and 15.8±7.4, p < 0.0001) in subjects with MCI and dementia compared to NCI. The area under the ROC curve (95% CI) for the diagnostic accuracy of CNFD, CNBD, CNFL compared to MTA-right and MTA-left for MCI was 78% (67-90%), 82% (72-92%), 86% (77-95%) versus 53% (36-69%) and 40% (25-55%), respectively, and for dementia it was 85% (76-94%), 84% (75-93%), 85% (76-94%) versus 86% (76-96%) and 82% (72-92%), respectively. Conclusion: The diagnostic accuracy of CCM, a non-invasive ophthalmic biomarker of neurodegeneration, was high and comparable with MTA rating for dementia but was superior to MTA rating for MCI

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of Machine Learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. ML has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management

Advancing sepsis clinical research: harnessing transcriptomics for an omics-based strategy - a comprehensive scoping review

Sepsis continues to be recognized as a significant global health challenge across all ages and is characterized by a complex pathophysiology. In this scoping review, PRISMA-ScR guidelines were adhered to, and a transcriptomic methodology was adopted, with the protocol registered on the Open Science Framework. We hypothesized that gene expression analysis could provide a foundation for establishing a clinical research framework for sepsis. A comprehensive search of the PubMed database was conducted with a particular focus on original research and systematic reviews of transcriptomic sepsis studies published between 2012 and 2022. Both coding and non-coding gene expression studies have been included in this review. An effort was made to enhance the understanding of sepsis at the mRNA gene expression level by applying a systems biology approach through transcriptomic analysis. Seven crucial components related to sepsis research were addressed in this study: endotyping (n = 64), biomarker (n = 409), definition (n = 0), diagnosis (n = 1098), progression (n = 124), severity (n = 451), and benchmark (n = 62). These components were classified into two groups, with one focusing on Biomarkers and Endotypes and the other oriented towards clinical aspects. Our review of the selected studies revealed a compelling association between gene transcripts and clinical sepsis, reinforcing the proposed research framework. Nevertheless, challenges have arisen from the lack of consensus in the sepsis terminology employed in research studies and the absence of a comprehensive definition of sepsis. There is a gap in the alignment between the notion of sepsis as a clinical phenomenon and that of laboratory indicators. It is potentially responsible for the variable number of patients within each category. Ideally, future studies should incorporate a transcriptomic perspective. The integration of transcriptomic data with clinical endpoints holds significant potential for advancing sepsis research, facilitating a consensus-driven approach, and enabling the precision management of sepsis

A Transcriptomic Appreciation of Childhood Meningococcal and Polymicrobial Sepsis from a Pro-Inflammatory and Trajectorial Perspective, a Role for Vascular Endothelial Growth Factor A and B Modulation?

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)–VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis

Diabetic cardiomyopathy

Diabetic cardiomyopathy is a distinct primary disease process, independent of coronary artery disease, which leads to heart failure in diabetic patients. Epidemiological and clinical trial data have confirmed the greater incidence and prevalence of heart failure in diabetes. Novel echocardiographic and MR (magnetic resonance) techniques have enabled a more accurate means of phenotyping diabetic cardiomyopathy. Experimental models of diabetes have provided a range of novel molecular targets for this condition, but none have been substantiated in humans. Similarly, although ultrastructural pathology of the microvessels and cardiomyocytes is well described in animal models, studies in humans are small and limited to light microscopy. With regard to treatment, recent data with thiazoledinediones has generated much controversy in terms of the cardiac safety of both these and other drugs currently in use and under development. Clinical trials are urgently required to establish the efficacy of currently available agents for heart failure, as well as novel therapies in patients specifically with diabetic cardiomyopathy