Heavy metals are constituents of the natural environment and are of great importance to both natural and artificial processes. But in recent times the indiscriminate use of heavy metals especially for human purposes has caused an imbalance in natural geochemical cycles. This imbalance has caused contamination of heavy metals into natural resources and such as soil and a marine ecosystem. Long exposure and higher accumulation of given heavy metals are known to impose detrimental and even lethal effects on humans. Conventional remediation techniques of heavy metals provide good results but have negative side effects on surrounding environment. The role played by microbes in bioremediation of heavy metals is well reported in the literature and understanding the role of molecules in the process of metal accumulation its reduction and transformation into less hazardous state, has myriads of biotechnological implications for bioremediation of metal-contaminated sites. The current review presents the implications of heavy metals on human health and marine ecosystems, conventional methods of heavy metal removal and their side effects on the environment. Bioremediation approaches have been discussed as well in this review, proving to be a more sustainable and eco-friendly approach towards remediation of heavy metals. 

Understanding heavy metal toxicity: Implications on human health, marine ecosystems and bioremediation strategies

Abstract Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study’s goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups. 

RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

The role of radiography in diagnosing, monitoring and prognosing juvenile idiopathic arthritis.

Abstract Rheumatoid arthritis (RA) is an autoimmune disease involving T and B lymphocytes. Autoantibodies contribute to joint deterioration and worsening symptoms. Adenosine deaminase (ADA), an enzyme in purine metabolism, influences adenosine levels and joint inflammation. Inhibiting ADA could impact RA progression. Intracellular ATP breakdown generates adenosine, which increases in hypoxic and inflammatory conditions. Lymphocytes with ADA play a role in RA. Inhibiting lymphocytic ADA activity has an immune-regulatory effect. Synovial fluid levels of ADA are closely associated with the disease’s systemic activity, making it a useful parameter for evaluating joint inflammation. Flavonoids, such as quercetin (QUE), are natural substances that can inhibit ADA activity. QUE demonstrates immune-regulatory effects and restores T-cell homeostasis, making it a promising candidate for RA therapy. In this review, we will explore the impact of QUE in suppressing ADA and reducing produced the inflammation in RA, including preclinical investigations and clinical trials. Graphical Abstract 

Mechanistic role of quercetin as inhibitor for adenosine deaminase enzyme in rheumatoid arthritis: systematic review

"Leveraging CT Imaging to Detect COPD and Concomitant Chronic Diseases." American Journal of Respiratory and Critical Care Medicine, 0(ja), pp. 

Leveraging CT Imaging to Detect COPD and Concomitant Chronic Diseases

BackgroundMachine learning and deep learning models have been increasingly used to predict long-term disease progression in patients with chronic obstructive pulmonary disease (COPD). We aimed to summarise the performance of such prognostic models for COPD, compare their relative performances, and identify key research gaps.MethodsWe conducted a systematic review and meta-analysis to compare the performance of machine learning and deep learning prognostic models and identify pathways for future research. We searched PubMed, Embase, the Cochrane Library, ProQuest, Scopus, and Web of Science from database inception to April 6, 2023, for studies in English using machine learning or deep learning to predict patient outcomes at least 6 months after initial clinical presentation in those with COPD. We included studies comprising human adults aged 18–90 years and allowed for any input modalities. We reported area under the receiver operator characteristic curve (AUC) with 95% CI for predictions of mortality, exacerbation, and decline in forced expiratory volume in 1 s (FEV1). We reported the degree of interstudy heterogeneity using Cochran's Q test (significant heterogeneity was defined as p≤0·10 or I2>50%). Reporting quality was assessed using the TRIPOD checklist and a risk-of-bias assessment was done using the PROBAST checklist. This study was registered with PROSPERO (CRD42022323052).FindingsWe identified 3620 studies in the initial search. 18 studies were eligible, and, of these, 12 used conventional machine learning and six used deep learning models. Seven models analysed exacerbation risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·69–0·85]) and there was significant heterogeneity (I2 97%, p<0·0001). 11 models analysed mortality risk, with only six reporting AUC and 95% CI on internal validation datasets (pooled AUC 0·77 [95% CI 0·74–0·80]) with significant degrees of heterogeneity (I2 60%, p=0·027). Two studies assessed decline in lung function and were unable to be pooled. Machine learning and deep learning models did not show significant improvement over pre-existing disease severity scores in predicting exacerbations (p=0·24). Three studies directly compared machine learning models against pre-existing severity scores for predicting mortality and pooled performance did not differ (p=0·57). Of the five studies that performed external validation, performance was worse than or equal to regression models. Incorrect handling of missing data, not reporting model uncertainty, and use of datasets that were too small relative to the number of predictive features included provided the largest risks of bias.InterpretationThere is limited evidence that conventional machine learning and deep learning prognostic models demonstrate superior performance to pre-existing disease severity scores. More rigorous adherence to reporting guidelines would reduce the risk of bias in future studies and aid study reproducibility.FundingNone. 

Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis. 

/pdf/artificial-intelligence-and-the-future-of-radiographic-3omml7sm.pdf

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint

Abstract Objective In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years. Methods A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors. Results The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment. Conclusion In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately. 

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores

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Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores