IM- LTS: An Integrated Model for Lung Tumor Segmentation using Neural Networks and IoMT

In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT). The model integrates two architectures, MobileNetV2 and U-NET, for classifying the input lung data. The input CT lung images are pre-processed using Z-score Normalization. The semantic features of lung images are extracted based on texture, intensity, and shape to provide information to the training network.•In this work, the transfer learning technique is incorporated, and the pre-trained NN was used as an encoder for the U-NET model for segmentation. Furthermore, Support Vector Machine is used here to classify input lung data as benign and malignant.•The results are measured based on the metrics such as, specificity, sensitivity, precision, accuracy and F-Score, using the data from benchmark datasets. Compared to the existing lung tumor segmentation and classification models, the proposed model provides better results and evidence for earlier disease diagnosis.

Spectral computed tomography iodine concentration spatial distribution and multi-parameter quantitative analysis in the differential diagnosis of central small cell lung cancer from squamous cell carcinoma

Background

Central small cell lung cancer (SCLC) and squamous cell carcinoma (SCC) are distinct types of lung cancer that require different treatment approaches and have varying prognoses. Accurate differentiation is crucial for clinical decision-making. However, traditional computed tomography (CT) may face challenges when their imaging features overlap. Spectral CT provides additional spectral images, offering more objective information for differentiation. This study investigated the application values of iodine concentration spatial distribution and multi-parameter quantitative analysis using a novel double-layer detector energy spectrum CT in the differential diagnosis of SCLC and SCC.

Methods

Sixty-six patients with central lung cancer (including 31 patients with SCLC and 35 patients with SCC) were examined with CT spectral imaging during arterial phase (AP) and venous phase (VP). The mean iodine concentration (IC), mean effective atomic number (Eff-Z), normalized iodine concentration (NIC), normalized iodine concentration in the proximal region (NICpro), normalized iodine concentration in the distal region (NICdis), and the differences in NIC between the proximal and distal regions in the AP and VP were calculated. The differences in parameters between central SCLC and SCC were compared, and receiver operating characteristic (ROC) curves were generated to explore the value of these parameters in distinguishing between central SCLC and SCC.

Results

The Eff-Z, IC, and NIC of SCLC were higher than SCC in the AP and VP. In terms of the spatial distribution differences in iodine concentration, both NICpro and NICdis during AP and VP were higher in SCLC compared to SCC. Moreover, ROC analysis showed that the NICdis in the VP had highest diagnostic performance in differentiating SCLC from SCC with an area under the receiver operating characteristic curve of 0.897, sensitivity of 96.80%, and a critical threshold of 0.175.

Conclusions

CT spectral imaging proves beneficial in diagnosing and differentiating between central SCLC and SCC, demonstrating significant clinical application value. Particularly, the spatial distribution of iodine concentration emerges as a valuable factor capable of enhancing diagnostic efficiency, showcasing undeniable potential.

Genomic, socio-environmental, and sequencing capability patterns in the surveillance of SARS-CoV-2 in Latin America and the Caribbean up to 2023

Latin America and the Caribbean (LAC) have been one of the most strongly impacted regions by the Coronavirus disease 2019 (COVID-19) emergency, with more than 83 million reported infections and 1.7 million deaths until December 2023, intensifying economic and social inequalities. This study aimed to provide information to the regional SARS-CoV-2 surveillance programs by determining genomic, socio-environmental, and sequencing capacity patterns associated with the circulation of the virus in Latin America up to 2023. Data from 24 countries in LAC were analyzed using public databases up to December 2023. A sampling of SARS-CoV-2 sequences from cases in the region enabled a phylogenomic study to elucidate the temporal distribution of various variants of concern (VOCs), mutations, and recombinants. Also, we identified differences in sequencing capabilities in LAC. Additionally, correlation and generalized linear model (GLM) analyses were conducted to explore potential associations between 89 socio-environmental variables and five COVID-19 indicators at the country level. The phylogenomic analyses revealed a diversity of variants with the predominance of some during specific periods, mainly VOCs and some recombinant cases, and a mutation rate of 8.39 × 10-4 substitutions per site per year, which are in line with other regions of the world. Besides, a low sequencing rate in LAC (on average 0.7% of cases) and incomplete databases in several countries were identified. In the analysis of indicators, correlations between 9 socio-environmental indicators and four COVID-19 variables associated with cases, deaths, and diagnostic tests related to the virus in the region, although not for sequencing percentages. This study provides information about the development of COVID-19 disease in LAC in terms of the viral genome, sequencing capabilities, and the region's complex socio-environmental conditions. Therefore, emphasis must be placed on implementing an integrated epidemiological surveillance approach to strengthen public health infrastructure and improve cooperation and preparedness for future infections affecting this region.

Characteristics of cortical thickness in treated HIV-infected individuals with and without cognitive impairment

BACKGROUND

HIV can alter the brain structure and function in the early stage of infection. This study investigated the differences in cortical thickness patterns between healthy controls (HCs) and people living with HIV (PLWH) with asymptomatic neurocognitive impairment (ANI) or cognitive integrity (CI).

METHODS

Twenty-one ANI, 25 CI, and 24 HCs were recruited and underwent high-resolution T1-weighted magnetic resonance images. Cortical thickness was analyzed using the Computational Anatomy Toolbox, and the correlation analysis was conducted between cortical thickness and clinical and neuropsychological variables.

RESULTS

Both CI and ANI exhibited decreased cortical thickness, primarily in the left frontal cortices and bilateral limbic system. ANI demonstrated a more pronounced and widespread pattern of cortical thinning. Lower CD4+ counts and higher peak plasma viral load were associated with decreased cortical thickness of the right pericallosal sulcus and middle-posterior part of cingulate gyrus and sulcus in ANI. Conversely, compared to HCs, both ANI and CI showed cortical thickening in the left insula cortex, and ANI tended to have a thicker cortex. Moreover, the increased thickness of left insula cortex in both CI and ANI were positively correlated with attention and working memory.

CONCLUSIONS

The cortical thickness thinning was observed in the frontal and limbic systems in both ANI and CI. Meanwhile, the thickening of the insular cortex may represent mild neuroinflammation or a transient compensatory mechanism. This study provides new insights into the neural mechanisms underlying HIV-related cognitive impairment and highlights the importance of cortical thickness alteration patterns when assessing cognitive function of PLWH.

Differentiation of inflammatory pseudotumors and malignant pulmonary nodules using the time-to-peak in first-pass dual-input volume computed tomography-perfusion

Background

Inflammatory pseudotumors (IPTs) are often misdiagnosed as malignant solitary pulmonary nodules (SPNs) because their imaging features overlap with those of malignant SPNs, leading to overtreatment. The purpose of this article was to investigate the value of first-pass enhanced time-to-peak (TTP) in distinguishing IPTs from malignant SPNs using dual-input volume computed tomography perfusion (DI-CTP).

Methods

This retrospective study included consecutive patients from No. 82 Group Hospital of Chinese People's Liberation Army with IPTs or malignant SPNs by surgery or biopsy from June 2016 to October 2021. Pulmonary artery flow (PF), bronchial artery flow (BF), perfusion index (PI), time-density curve, total perfusion (TLP), and first-pass TTP for all SPNs were determined by DI-CTP. The receiver operating characteristic (ROC) curve was used to analyse the values of TTP and other perfusion parameters in the differential diagnosis of IPTs and malignant SPNs.

Results

Ninety-eight patients were enrolled, including 25 with IPTs and 73 with malignant SPNs. The intraclass correlation coefficient (ICC) for inter-observer and intra-observer reliability of all parameters was perfect (all ICCs >0.90, P<0.01). Compared with IPTs, the malignant SPNs showed significantly higher PF, TLP, and TTP (all P<0.01), without significant differences in BF and PI (all P>0.05). The area under the curve of TTP was 0.987, which was higher than those of the other perfusion parameters (0.987 vs. 0.752 vs. 0.609 vs. 0.728 vs. 0.628). With a cut-off TTP of 18.10 s to distinguish IPNs from malignant SPNs, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92.0%, 97.3%, 97.3%, 96.0%, and 96.9%, respectively. The sensitivity, specificity, and accuracy of CT plain scan combined with DI-CTP in diagnosing pulmonary nodules were 100%, 96.83%, and 98.97%, respectively.

Conclusions

Compared with other perfusion parameters, TTP may be a more valuable parameter to differentiate IPTs from malignant SPNs.

Artificial intelligence in emergency neuroradiology: Current applications and perspectives

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brain, head and neck, and spine. This narrative review aims at providing an up-to-date discussion about the state of the art of applications of artificial intelligence in emergency neuroradiology, which have substantially expanded in depth and scope in the past few years. A detailed analysis of machine learning and deep learning algorithms in several tasks related to acute ischemic stroke involving various imaging modalities, including a description of existing commercial products, is provided. The applications of artificial intelligence in acute intracranial hemorrhage and other vascular pathologies such as intracranial aneurysm and arteriovenous malformation are discussed. Other areas of emergency neuroradiology including infection, fracture, cord compression, and pediatric imaging are further discussed in turn. Based on these discussions, this article offers insight into practical considerations regarding the applications of artificial intelligence in emergency neuroradiology, calling for more development driven by clinical needs, attention to pediatric neuroimaging, and analysis of real-world performance.

Improving differentiation of hemorrhagic brain metastases from non-neoplastic hematomas using radiomics and clinical feature fusion

OBJECTIVES

This study aimed to develop and validate a fusion model combining multi-sequence MRI radiomics and clinico-radiological features to distinguish hemorrhagic brain metastasis covered by hematoma (HBM.cbh) from non-neoplastic intracranial hematomas (nn-ICH).

METHODS

The data of 146 patients with pathologically or clinically proven HBM.cbh (n = 55) and nn-ICH (n = 91) were collected from two clinical institutions. Radiomics features were extracted from various regions (hemorrhage and/or edema) based on T2-weighted, T1-weighted, fluid-attenuated inversion-recovery, and T1 contrast-enhanced imaging. Synthetic minority over-sampling technique (SMOTE) was performed to balance the minority group (HBM.cbh). Logistic regression (LR) and k-nearest neighbors (KNN) were utilized to construct the models based on clinico-radiological factors (clinical model), radiomic features from various modalities of MRI (radiomics model), and their combination (fusion model). The area under the curve (AUC) values of different models on the external dataset were compared using DeLong's test.

RESULTS

The 4-sequence radiomics model based on the entire region performed the best in all radiomics models, with or without SMOTE, where the AUCs were 0.83 and 0.84, respectively. The AUC of clinical mode was 0.71 with SMOTE, and 0.62 without SMOTE. The fusion model demonstrated excellent predictive value with or without SMOTE (AUC: 0.93 and 0.90, respectively), outperforming both the radiomics and clinical model (0.93 vs. 0.83, 0.71, p < 0.05 and 0.90 vs. 0.84, 0.62, p < 0.05, respectively).

CONCLUSIONS

The multi-sequence radiomics model is an effective method for differentiating HBM.cbh from nn-ICH. It can yield the best diagnostic performance prediction model when combined with clinico-radiological features.

Graph theory analysis reveals functional brain network alterations in HIV-associated asymptomatic neurocognitive impairment in virally suppressed homosexual males

BACKGROUND

This study aimed to investigate the global and nodal functional network alterations, abnormal connections of brain regions, and potential imaging biomarkers in virally suppressed people living with HIV (PLH) with asymptomatic neurocognitive impairment (ANI) using graph theory analysis.

METHODS

The study included 64 men with ANI (mean age 32.45 years) and 64 healthy controls (HC) (mean age 31.31 years). The functional network was established through the graph theory method and Automated Anatomic Labeling (AAL) 90 atlas, which provides a cerebrum parcellation framework. Moreover, hub regions were identified based on betweenness centrality (Bc). Functional connectivity (FC) differences were investigated between the two groups, these connections were located in the resting-state network (RSN). Neuropsychological (NP) tests were performed, and relationships between graph theory measures, clinical data, and NP tests were analyzed. Multiple comparisons were used to correct for false-positive findings.

RESULTS

On the global level, small-worldness, global efficiency (Eg), and local efficiency (Eloc) were significantly decreased in ANI subjects. On a nodal level, brain regions in the frontal and subcortical regions showed significantly decreased nodal measures, while regions in the parietal, temporal, and occipital lobes showed increased nodal measures. Increased FCs were found between brain regions in the visual, frontoparietal, and somatomotor networks. Hub regions overlapped highly between the two groups. Age was negatively correlated with graph theory measures.

CONCLUSION

Our findings demonstrate the global and nodal alterations in the functional network of virally suppressed homosexual males in the ANI stage. Frontal and subcortical brain regions may be important for finding the imaging biomarkers for HIV-associated neurocognitive disorder.

Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes

Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82-85%, AUC of 0.87-0.9, and Cohen's κ of 0.45-0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6-12.5% and R2 of 0.26-0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. Conclusions: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist's assessment. It may improve diagnostic and foster personalized treatment.

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severity requires a radiologist with significant expertise. This study introduces a pioneering utilization of deep learning (DL) for evaluate COVID-19 severity using lung CT images, presenting a novel and effective method for assessing the severity of pulmonary manifestations in COVID-19 patients. Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. Inception-ResNet incorporates the dilated mechanism into its ResNet component, enhancing its ability to accurately classify lung involvement stages. This study demonstrates that dilated residual networks (dResNet) outperform their non-dilated counterparts in image classification tasks, as their architectural designs allow the systems to acquire comprehensive global data by expanding their receptive fields. Our study utilized an initial dataset of 1548 human thoracic CT scans, meticulously annotated by two experienced specialists. Lung involvement was determined by calculating a percentage based on observations made at each scan. The hybrid methodology successfully distinguished the ten distinct severity levels associated with COVID-19, achieving a maximum accuracy of 96.40%. This system demonstrates its effectiveness as a diagnostic framework for assessing lung involvement in COVID-19-affected individuals, facilitating disease progression tracking.

Altered angular gyrus activation during the digit symbol substitution test in people living with HIV: beyond information processing speed deficits

Speed-of-information processing (SIP) is often impaired in people living with HIV (PLWH), typically assessed through tests such as the digit symbol (DS) and symbol search, which also rely on motor and executive functions. This study aims to disentangle SIP deficits from other cognitive impairments in PLWH using an MRI-adapted digit symbol substitution test (mDSST). Fifty-seven PLWH (34.7 ± 11.2 years) and 50 age-matched people living without HIV (PLWoH, 31.8 ± 9.9 years) completed standardized neuropsychological tests and the mDSST. Behavioral performances and brain activations were compared, with correlations drawn between group-differentiating brain activations and clinical ratings of cognitive domains. Results showed that PLWH performed worse in DS and symbol search, made fewer responses, and was slower in mDSST, with performances correlating to SIP and motor ratings. Notably, PLWH showed greater deficits in attention compared to PLWoH, rather than in SIP or motor. PLWH also exhibited greater primary motor cortex activation and reduced right angular gyrus activation. These findings suggest that slower performances on SIP-related tests in PLWH may be partially linked to abnormal visuospatial attention, as reflected by reduced angular gyrus activation, with higher motor cortex activation potentially serving as a compensatory mechanism. Future studies should explore whether prefrontal regions implicated in SIP are impaired in more severely affected PLWH.

Neuroimaging advances in neurocognitive disorders among HIV-infected individuals

Although combination antiretroviral therapy (cART) has been widely applied and effectively extends the lifespan of patients infected with human immunodeficiency virus (HIV), these patients remain at a substantially increased risk of developing neurocognitive impairment, commonly referred to as HIV-associated neurocognitive disorders (HAND). Magnetic resonance imaging (MRI) has emerged as an indispensable tool for characterizing the brain function and structure. In this review, we focus on the applications of various MRI-based neuroimaging techniques in individuals infected with HIV. Functional MRI, structural MRI, diffusion MRI, and quantitative MRI have all contributed to advancing our comprehension of the neurological alterations caused by HIV. It is hoped that more reliable evidence can be achieved to fully determine the driving factors of cognitive impairment in HIV through the combination of multi-modal MRI and the utilization of more advanced neuroimaging analysis methods.

Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source-destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor-critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

Refining COVID-19 Lesion Segmentation in Lung CT Scans Using Swarm Intelligence and Evolutionary Algorithms

BACKGROUND

Accurately identifying lung lesions in CT (Computed Tomography) scans remains crucial during the Coronavirus Disease 2019 (COVID-19) pandemic. Swarm intelligence algorithms offer promising tools for this purpose.

METHODS

This study compares four swarm intelligence algorithms Gravitational Search Algorithm (GSA), Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for segmenting COVID-19 lung lesions.

RESULTS

GA, GSA, and BFOA achieved accuracies exceeding 90.5%, while the PSO algorithm further improved segmentation accuracy, reaching 91.45%, with an exceptional F1 score of 95.54%. Overall, the approach achieved up to 99% segmentation accuracy.

CONCLUSIONS

The findings demonstrate the effectiveness of swarm and evolutionary algorithms in segmenting COVID-19 lesions, contributing to enhanced diagnostic accuracy and treatment efficiency.

Prognostic implications of regional lung impairment evaluation in quantitative computed tomography imaging of COVID-19

AIM

To enhance the understanding of COVID-19 regional lung damage pattern by analyzing the organ in subregions, beyond the typical lobe segmentation.

MATERIALS AND METHODS

This study used semiautomatic computed tomography (CT) imaging segmentation and quantification to investigate regional lung impairments in patients with COVID-19. Each lung was divided into 12 regions, and the anatomical impairments obtained from the CT image (emphysema, ground glass opacity, and collapsed tissue) were quantified. Then, the results for every region were correlated with clinical outcomes. This research encompassed 333 individuals, both COVID positive (n = 190) and COVID negative (n = 143), whose medical reports were checked for the need for ventilatory support and outcome (cure or deceased).

RESULTS

Findings indicate a strong association between the extent of lung damage and COVID-19 diagnosis, the level of ventilatory assistance required, and patient survival rates. Notably, the medial posterior lung region exhibited increased opacities and collapse in COVID-positive patients (p < 0.05), particularly those requiring invasive ventilation or who succumbed to the illness.

CONCLUSION

The results expand the knowledge of COVID-19 regional impact beyond typical lobe segmentation and indicate that COVID-19 impairments in the lungs are localized. The most affected region identified was the medial posterior of both right and left lungs. Early detection of quantifiable lung damage can serve as a valuable prognostic tool, helping to pinpoint patients at heightened risk of severe complications or mortality.

Quantitative assessment of lung opacities from CT of pulmonary artery imaging data in COVID-19 patients: artificial intelligence versus radiologist

Objectives

Artificial intelligence (AI) deep learning algorithms trained on non-contrast CT scans effectively detect and quantify acute COVID-19 lung involvement. Our study explored whether radiological contrast affects the accuracy of AI-measured lung opacities, potentially impacting clinical decisions. We compared lung opacity measurements from AI software with visual assessments by radiologists using CT pulmonary angiography (CTPA) images of early-stage COVID-19 patients.

Methods

This prospective single-centre study included 18 COVID-19 patients who underwent CTPA due to suspected pulmonary embolism. Patient demographics, clinical data, and 30-day and 90-day mortality were recorded. AI tool (Pulmonary Density Plug-in, AI-Rad Companion Chest CT, SyngoVia; Siemens Healthineers, Forchheim, Germany) was used to estimate the quantity of opacities. Visual quantitative assessments were performed independently by 2 radiologists.

Results

There was a positive correlation between radiologist estimations (r 2 = 0.57) and between the AI data and the mean of the radiologists' estimations (r 2 = 0.70). Bland-Altman plot analysis showed a mean bias of +3.06% between radiologists and -1.32% between the mean radiologist vs AI, with no outliers outside 2×SD for respective comparison.

The AI protocol facilitated a quantitative assessment of lung opacities and showed a strong correlation with data obtained from 2 independent radiologists, demonstrating its potential as a complementary tool in clinical practice.

Conclusion

In assessing COVID-19 lung opacities in CTPA images, AI tools trained on non-contrast images, provide comparable results to visual assessments by radiologists.

Advances in knowledge

The Pulmonary Density Plug-in enables quantitative analysis of lung opacities in COVID-19 patients using contrast-enhanced CT images, potentially streamlining clinical workflows and supporting timely decision-making.

Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans

BACKGROUND/OBJECTIVES

According to the World Health Organization, the gold standard for diagnosing COVID-19 is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, to confirm the diagnosis in patients who have negative results but still show symptoms, imaging tests, especially computed tomography (CT), are used. In this study, using convolutional neural networks, we compared the following topics using manual and automatic lung segmentation methods: (1) the performance of an automatic segmentation of COVID-19 areas using two strategies for data partitioning, CT scans, and slice strategies; (2) the performance of an automatic segmentation method of COVID-19 when there was interobserver agreement between two groups of radiologists; and (3) the performance of the area affected by COVID-19.

METHODS

Two datasets and two deep neural network architectures are used to evaluate the automatic segmentation of lungs and COVID-19 areas. The performance of the U-Net architecture is compared with the performance of a new architecture proposed by the research group.

RESULTS

With automatic lung segmentation, the Dice metrics for the segmentation of the COVID-19 area were 73.01 ± 9.47% and 84.66 ± 5.41% for the CT-scan strategy and slice strategy, respectively. With manual lung segmentation, the Dice metrics for the automatic segmentation of COVID-19 were 74.47 ± 9.94% and 85.35 ± 5.41% for the CT-scan and the slice strategy, respectively.

CONCLUSIONS

The main conclusions were as follows: COVID-19 segmentation was slightly better for the slice strategy than for the CT-scan strategy; a comparison of the performance of the automatic COVID-19 segmentation and the interobserver agreement, in a group of 7 CT scans, revealed that there was no statistically significant difference between any metric.

Dynamics of peripheral T cell exhaustion and monocyte subpopulations in neurocognitive impairment and brain atrophy in chronic HIV infection

BACKGROUND

HIV-associated neurocognitive disorders (HAND) is hypothesized to be a result of myeloid cell-induced neuro-inflammation in the central nervous system that may be initiated in the periphery, but the contribution of peripheral T cells in HAND pathogenesis remains poorly understood.

METHODS

We assessed markers of T cell activation (HLA-DR + CD38+), immunosenescence (CD57 + CD28-), and immune-exhaustion (TIM-3, PD-1 and TIGIT) as well as monocyte subsets (classical, intermediate, and non-classical) by flow cytometry in peripheral blood derived from individuals with HIV on long-term stable anti-retroviral therapy (ART). Additionally, normalized neuropsychological (NP) composite test z-scores were obtained and regional brain volumes were assessed by magnetic resonance imaging (MRI). Relationships between proportions of immune phenotypes (of T-cells and monocytes), NP z-scores, and brain volumes were analyzed using Pearson correlations and multiple linear regression models.

RESULTS

Of N = 51 participants, 84.3% were male, 86.3% had undetectable HIV RNA < 50 copies/ml, median age was 52 [47, 57] years and median CD4 T cell count was 479 [376, 717] cells/uL. Higher CD4 T cells expressing PD-1 + and/or TIM-3 + were associated with lower executive function and working memory and higher CD8 T cells expressing PD-1+ and/or TIM-3+ were associated with reduced brain volumes in multiple regions (putamen, nucleus accumbens, cerebellar cortex, and subcortical gray matter). Furthermore, higher single or dual frequencies of PD-1 + and TIM-3 + expressing CD4 and CD8 T-cells correlated with higher CD16 + monocyte numbers.

CONCLUSIONS

This study reinforces evidence that T cells, particularly those with immune exhaustion phenotypes, are associated with neurocognitive impairment and brain atrophy in people living with HIV on ART. Relationships revealed between T-cell immune exhaustion and inflammatory in CD16+ monocytes uncover interrelated cellular processes likely involved in the immunopathogenesis of HAND.

An Exploratory Study: Can Native T1 Mapping Differentiate Sarcoma from Benign Soft Tissue Tumors at 1.5 T and 3 T?

Background/Objectives: T1 relaxation time has been shown to be valuable in detecting and characterizing tumors in various organs. This study aims to determine whether native T1 relaxation time can serve as a useful tool in distinguishing sarcomas from benign tumors. Methods: In this retrospective study, patients with histologically confirmed soft tissue sarcomas and benign tumors were included. Only patients who had not undergone prior treatment or surgery and whose magnetic resonance imaging (MRI) included native T1 mapping were considered. Images were acquired using both 1.5 T and 3 T MRI scanners. T1 histogram parameters were measured in regions of interest encompassing the entire tumor volume, as well as in healthy muscle tissue. Results: Out of 316 cases, 16 sarcoma cases and 9 benign tumor cases were eligible. The T1 values observed in sarcoma did not significantly differ from those in benign lesions in both 1.5 T and 3 T MRIs (p1.5T = 0.260 and p3T = 0.119). However, T1 values were found to be lower in healthy tissues compared to sarcoma at 3 T (p = 0.020), although this difference did not reach statistical significance at 1.5 T (p = 0.063). At both 1.5 T and 3 T, no significant difference between healthy muscle measured in sarcoma cases or benign tumor cases was observed (p1.5T = 0.472 and p3T = 0.226). Conclusions: T1 mapping has the potential to serve as a promising tool for differentiating sarcomas from benign tumors in baseline assessments. However, the standardization of imaging protocols and further improvements in T1 mapping techniques are necessary to fully realize its potential.

T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study

BACKGROUND

T1 mapping can quantify the longitudinal relaxation time of tissues. This study aimed to investigate the repeatability and reproducibility of T1 mapping radiomics features of lung cancer and the feasibility of T1 mapping-based radiomics model to predict its pathological types.

METHODS

The chest T1 mapping images and clinical characteristics of 112 lung cancer patients (54 with adenocarcinoma and 58 with other types of lung cancer) were collected retrospectively. 54 patients underwent twice short-term T1 mapping scans. Regions of interest were manually delineated on T1 mapping pseudo-color images to measure the mean native T1 values of lung cancer, and radiomics features were extracted using the semi-automatic segmentation method by two independent observers. The patients were randomly divided into training group (77 cases) and validation group (35 cases) with the ratio of 7:3. Interclass correlation coefficients (ICCs), Student's t-test or Mann-Whitney U tests and least absolute shrinkage and selection operator (LASSO) were used for feature selection. The optimum features were selected to establish a logistic regression (LR) radiomics model. Independent sample t-test, Mann Whitney U-test or chi square test were used to compare the differences of clinical characteristics and T1 values. Performance was compared by the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

In the training group, smoking history, lesion type and native T1 values were different between adenocarcinoma and non-adenocarcinoma patients (P = 0.004-0.038). There were 1035 (54.30%) radiomics features meet the intra-and inter-observer, and test-retest reproducibility with ICC > 0.80. After feature dimensionality reduction and model construction, the AUC of T1 mapping-based radiomics model for predicting the pathological types of lung cancer was 0.833 and 0.843, respectively, in the training and validation cohorts. The AUCs of T1 value and clinical model (including smoking history and lesion type) were 0.657 and 0.692 in the training group, and 0.722 and 0.686 in the validation group. Combined with T1 mapping radiomics, clinical model and T1 value to establish a combined model, the prediction efficiency was further improved to 0.895 and 0.915 in the training and validation groups.

CONCLUSIONS

About 50% of the T1 mapping-based radiomics features displayed relatively poor repeatability and reproducibility. While T1 mapping-based radiomics model is valuable in identification of histological types of lung cancer despite the measurement variability. Combination of T1 mapping radiomics model, clinical characteristics and native T1 value can improve the predictive value of pathological types of lung cancer.

Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model

Objective

By extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO2/FiO2), thereby developed an Artificial Intelligence (AI) model based on radiomic features to predict the deterioration of oxygenation function in COVID-19 patients.

Methods

This retrospective study included 384 patients with COVID-19, whose baseline information, laboratory indicators, oxygenation-related parameters, and non-enhanced chest CT images were collected. Utilizing the PaO2/FiO2 stratification proposed by the Berlin criteria, patients were divided into 4 groups, and differences in laboratory indicators among these groups were compared. Radiomic features were extracted, and their correlations with laboratory indicators and the PaO2/FiO2 were analyzed, respectively. Finally, an AI model was developed using the PaO2/FiO2 threshold of less than 200 mmHg as the label, and the model's performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Group datas comparison was analyzed using SPSS software, and radiomic features were extracted using Python-based Pyradiomics.

Results

There were no statistically significant differences in baseline characteristics among the groups. Radiomic features showed differences in all 4 groups, while the differences in laboratory indicators were inconsistent, with some PaO2/FiO2 groups showed differences and others not. Regardless of whether laboratory indicators demonstrated differences across different PaO2/FiO2 groups, they could all be captured by radiomic features. Consequently, we chose radiomic features as variables to establish an AI model based on chest CT radiomic features. On the training set, the model achieved an AUC of 0.8137 (95% CI [0.7631-0.8612]), accuracy of 0.7249, sensitivity of 0.6626 and specificity of 0.8208. On the validation set, the model achieved an AUC of 0.8273 (95% CI [0.7475-0.9005]), accuracy of 0.7739, sensitivity of 0.7429 and specificity of 0.8222.

Conclusion

This study found that the early chest CT radiomic features of COVID-19 patients are strongly associated not only with early laboratory indicators but also with the lowest PaO2/FiO2. Consequently, we developed an AI model based on CT radiomic features to predict deterioration in oxygenation function, which can provide a reliable basis for further clinical management and treatment.

How AI Could Help Us in the Epidemiology and Diagnosis of Acute Respiratory Infections?

Acute respiratory infections (ARIs) represent a significant global health burden, contributing to high morbidity and mortality rates, particularly in vulnerable populations. Traditional methods for diagnosing and tracking ARIs often face limitations in terms of speed, accuracy, and scalability. The advent of artificial intelligence (AI) has the potential to revolutionize these processes by enhancing early detection, precise diagnosis, and effective epidemiological tracking. This review explores the integration of AI in the epidemiology and diagnosis of ARIs, highlighting its capabilities, current applications, and future prospects. By examining recent advancements and existing studies, this paper provides a comprehensive understanding of how AI can improve ARI management, offering insights into its practical applications and the challenges that must be addressed to realize its full potential.

Central artery pulse pressure, not central arterial stiffness impact on all-cause mortality in patients with viral pneumonia infection

OBJECTIVES

COVID-19 viral pneumonia can result in increased arterial stiffness, along with cardiac and systemic inflammatory responses. This study aimed to investigate the association between arterial stiffness, inflammation severity, and all-cause mortality in patients with COVID-19.

METHODS

In this study, anthropometric data, pneumonia infection severity, and blood tests were analyzed. Arterial stiffness was assessed using the non-invasive assessment indices, including arterial velocity pulse index (AVI) and central arterial pulse pressure (CAPP). Infection volumes and percentages for the whole lungs, most lobes, and most segments were extracted from CT images using artificial intelligence-based quantitative analysis software. The relationship between arterial stiffness, central hemodynamics, and all-cause mortality was investigated.

RESULTS

In multivariable Cox regression analysis, high CAPP was significantly associated with all-cause mortality (hazard ratio: 0.263, 95% CI, 0.073-0.945, p = 0.041). Whole lung infection percentages were independently associated with high CAPP, with an area under the curve (AUC) of 0.662 and a specificity of 89.09%.

CONCLUSIONS

High CAPP, but not high AVI, demonstrated independent prognostic value for all-cause mortality in patients due to COVID-19 pneumonia infection. Evaluating this parameter could help in risk assessment and improve diagnostic and therapeutic strategies in viral pneumonia infections.

Interleukin-1 Beta rs16944 and rs1143634 and Interleukin-6 Receptor rs12083537 Single Nucleotide Polymorphisms as Potential Predictors of COVID-19 Severity

Host genetic variation has been recognized as a key predictor of diverse clinical sequelae among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. Insights into the link between the Interleukin-6 receptor (IL-6R) and Interleukin-1 beta (IL-1β) genetic variation and severe coronavirus disease 2019 (COVID-19) are crucial for developing new predictors and therapeutic targets. We aimed to investigate the association of IL-6R rs12083537, IL-1β rs16944, and IL-1β rs1143634 SNPs with the severity of COVID-19. Our study was conducted on 300 COVID-19-negative individuals (control group) and 299 COVID-19-positive cases, classified into mild, moderate, and severe subgroups. Analyses of IL-1β (rs16944, rs1143634) and IL-6R (rs12083537) SNPs' genotypes were performed using qPCR genotyping assays. The IL-1β (rs16944) CC genotype and IL-6R (rs12083537) GG genotype were substantially related to COVID-19 severity, which was also associated with comorbidities and some laboratory parameters (p < 0.001). The IL-1β (rs1143634) TT genotype was found to be protective. Likewise, the IL-1β (rs16944) CC genotype was associated with increased mortality. IL-1β rs16944 and IL-6R rs12083537 SNPs are promising potential predictors of SARS-CoV-2 disease severity. Meanwhile, the rs1143634 SNP T allele was protective against severity and mortality risk.

Predicting the severity of mycoplasma pneumoniae pneumonia in pediatric and adult patients: a multicenter study

The purpose of this study is to develop a nomogram model for early prediction of the severe mycoplasma pneumoniae pneumonia (SMPP) in Pediatric and Adult Patients. A retrospective analysis was conducted on patients with MPP, classifying them into SMPP and non-severe MPP (NSMPP) groups. A total of 550 patients (NSMPP 374 and SMPP 176) were enrolled in the study and allocated to training, validation cohorts. 278 patients (NSMPP 224 and SMPP 54) were retrospectively collected from two institutions and allocated to testing cohort. The risk factors for SMPP were identified using univariate analysis. For radiomic feature selection, Spearman's correlation and the least absolute shrinkage and selection operator (LASSO) were utilized. Logistic regression was used to build different models, including clinical, imaging, radiomics, and integrated models (combining clinical, imaging, and radiomics features selected). The model's discrimination was evaluated using a receiver operating characteristic curve, its calibration with a calibration curve, and the results were visualized using the Hosmer-Lemeshow goodness-of-fit test. Thirteen clinical features and fourteen imaging features were selected for constructing the clinical and imaging models. Simultaneously, a set of twenty-five radiomics features were utilized to build the radiomics model. The integrated model demonstrated good calibration and discrimination in the training cohorts (AUC, 0.922; 95% CI: 0.900, 0.942), validation cohorts (AUC, 0.879; 95% CI: 0.806, 0.920), and testing cohorts (AUC, 0.877; 95% CI: 0.836, 0.916). The discriminatory and predictive efficacy of the clinical model in testing cohorts increased further after clinical and radiological features were incorporated (AUC, 0.849 vs. 0.922, P = 0.002). The model demonstrated exemplary predictive efficacy for SMPP by leveraging a comprehensive set of inputs, encompassing clinical data, quantitative and qualitative radiological features, along with radiomics features. The integration of these three aspects in the predictive model further enhanced the performance of the clinical model, indicating the potential for extensive clinical applications.

Radiofrequency ablation: mechanisms and clinical applications

Radiofrequency ablation (RFA), a form of thermal ablation, employs localized heat to induce protein denaturation in tissue cells, resulting in cell death. It has emerged as a viable treatment option for patients who are ineligible for surgery in various diseases, particularly liver cancer and other tumor-related conditions. In addition to directly eliminating tumor cells, RFA also induces alterations in the infiltrating cells within the tumor microenvironment (TME), which can significantly impact treatment outcomes. Moreover, incomplete RFA (iRFA) may lead to tumor recurrence and metastasis. The current challenge is to enhance the efficacy of RFA by elucidating its underlying mechanisms. This review discusses the clinical applications of RFA in treating various diseases and the mechanisms that contribute to the survival and invasion of tumor cells following iRFA, including the roles of heat shock proteins, hypoxia, and autophagy. Additionally, we analyze‌ the changes occurring in infiltrating cells within the TME after iRFA. Finally, we provide a comprehensive summary of clinical trials involving RFA in conjunction with other treatment modalities in the field of cancer therapy, aiming to offer novel insights and references for improving the effectiveness of RFA.

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses

RATIONALE AND OBJECTIVES

Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses.

MATERIALS AND METHODS

A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05).

CONCLUSION

The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease

Background

Coronavirus disease 2019 (COVID-19) still poses a threat to people's physical and mental health. We proposed a new semi-quantitative visual classification method for COVID-19, and this study aimed to evaluate the clinical usefulness and feasibility of lung field-based severity score (LFSS).

Methods

This retrospective study included 794 COVID-19 patients from two hospitals in China between December 2022 and January 2023. Six lung fields on the axial computed tomography (CT) were defined. LFSS and eighteen clinical characteristics were evaluated. LFSS was based on summing up the parenchymal opacification involving each lung field, which was scored as 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), or 4 (75-100%), respectively (range of LFSS from 0 to 24). Total pneumonia burden (TPB) was calculated using the U-net model. The correlation between LFSS and TPB was analyzed. After performing logistic regression analysis, an LFSS-based model, clinical-based model and combined model were developed. Receiver operating characteristic curves were used to evaluate and compare the performance of three models.

Results

LFSS, age, chronic liver disease, chronic kidney disease, white blood cell, neutrophils, lymphocytes and C-reactive protein differed significantly between the non-critical and critical group (all P<0.05). There was a strong positive correlation of LFSS and TPB (Pearson correlation coefficient =0.767, P<0.001). The area under curves of LFSS-based model, clinical-based model and combined model were 0.799 [95% confidence interval (CI): 0.770-0.827], 0.758 (95% CI: 0.727-0.788), and 0.848 (95% CI: 0.821-0.872), respectively.

Conclusions

The LFSS derived from chest CT may be a potential new tool to help identify COVID-19 patients at high risk of progressing to critical disease.

COVID-19 vaccine uptake in individuals with functional difficulty, disability, and comorbid conditions: insights from a national survey in Bangladesh

BACKGROUND

COVID-19 vaccine uptake among individuals with disabilities is crucial for safeguarding their health and well-being. However, the extent of vaccine uptake among this group remains largely unknown in low- and middle-income countries. This study aims to assess the COVID-19 vaccine uptake among persons with functional difficulty, disability and/or comorbidity in Bangladesh and their associated factors.

METHODS

Data from 9,370 respondents extracted from the 2021 National Household Survey on Persons with Disability were analysed. The outcome variable was the uptake of at least one dose of the COVID-19 vaccine (yes, no). Key explanatory variables included the presence of disability (yes, no), comorbidity (yes, no), and both comorbidity and disability (yes, no) among persons with functional difficulty. The relationship between the outcome and explanatory variables was determined using mixed-effects multilevel logistic regressions adjusted for covariates.

RESULTS

The overall uptake of at least one dose of the COVID-19 vaccine among persons with functional difficulty was 57.37%, among persons with functional difficulty and disability was 48.63% and among persons with functional difficulty and single (57.85%) or multi-comorbidity (60.37%). Compared to the respondents with functional difficulty only, the adjusted odds ratio (aOR) of not receiving any dose of the COVID-19 vaccine for individuals with both functional difficulty and disability was 1.37 (95% CI, 1.22-1.53), and for individuals with functional difficulty, disability and one or more comorbid conditions was 1.30 (95% CI, 1.15-1.47). The aOR of receiving at least one dose of the COVID-19 vaccine among individuals with functional difficulty and one or more comorbid conditions was significantly higher than among those with functional difficulty only.

CONCLUSION

In Bangladesh, COVID-19 vaccine uptake was relatively low among individuals with disabilities. The existing COVID-19 vaccine rollout programs and similar future programs should prioritise individuals with disabilities and include targeted strategies to reach them.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Antiviral Effectiveness, Clinical Outcomes, and Artificial Intelligence Imaging Analysis for Hospitalized COVID-19 Patients Receiving Antivirals

INTRODUCTION

There is still a lack of clinical evidence comprehensively evaluating the effectiveness of antiviral treatments for COVID-19 hospitalized patients.

METHODS

A retrospective cohort study was conducted at Beijing You'An Hospital, focusing on patients treated with nirmatrelvir/ritonavir or azvudine. The study employed a tripartite analysis-viral dynamics, survival curve analysis, and AI-based radiological analysis of pulmonary CT images-aiming to assess the severity of pneumonia.

RESULTS

Of 370 patients treated with either nirmatrelvir/ritonavir or azvudine as monotherapy, those in the nirmatrelvir/ritonavir group experienced faster viral clearance than those treated with azvudine (5.4 days vs. 8.4 days, p < 0.001). No significant differences were observed in the survival curves between the two drug groups. AI-based radiological analysis revealed that patients in the nirmatrelvir group had more severe pneumonia conditions (infection ratio is 11.1 vs. 5.35, p = 0.007). Patients with an infection ratio higher than 9.2 had nearly three times the mortality rate compared to those with an infection ratio lower than 9.2.

CONCLUSIONS

Our study suggests that in real-world studies regarding hospitalized patients with COVID-19 pneumonia, the antiviral effect of nirmatrelvir/ritonavir is significantly superior to azvudine, but the choice of antiviral agents is not necessarily linked to clinical outcomes; the severity of pneumonia at admission is the most important factor to determine prognosis. Additionally, our findings indicate that pulmonary AI imaging analysis can be a powerful tool for predicting patient prognosis and guiding clinical decision-making.

Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19

Introduction

Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients.

Methods

This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People's Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models.

Results

The median dialysis duration was 48 (interquartile range, 24-96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes.

Conclusion

This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.

Laboratory data and broncho-alveolar lavage on Covid-19 patients with no intensive care unit admission: Correlation with chest CT features and clinical outcomes

Broncho-alveolar lavage (BAL) is indicated in cases of uncertain diagnosis but high suspicion of Sars-Cov-2 infection allowing to collect material for microbiological culture to define the presence of coinfection or super-infection. This prospective study investigated the correlation between chest computed tomography (CT) findings, Covid-19 Reporting and Data System score, and clinical outcomes in Coronavirus disease 2019 (Covid-19) patients who underwent BAL with the aim of predicting outcomes such as lung coinfection, respiratory failure, and hospitalization length based on chest CT abnormalities. Study population included 34 patients (range 38-90 years old; 20 males, 14 females) with a positive nucleic acid amplification test for Covid-19 infection, suitable BAL examination, and good quality chest CT scan in the absence of lung cancer history. Pulmonary coinfections were found in 20.6% of patients, predominantly caused by bacteria. Specific correlations were found between right middle lobe involvement and pulmonary co-infections. Severe lung injury (PaO2/FiO2 ratio of 100-200) was associated with substantial involvement of right middle, right upper, and left lower lobes. No significant correlation was found between chest CT findings and inflammatory markers (C-reactive protein, procalcitonin) or hospitalization length of stay. Specific chest CT patterns, especially in right middle lobe, could serve as indicators for the presence of co-infections and disease severity in noncritically ill Covid-19 patients, aiding clinicians in timely interventions and personalized treatment strategies.

Seasonal human coronaviruses OC43, 229E, and NL63 induce cell surface modulation of entry receptors and display host cell-specific viral replication kinetics

The emergence of the COVID-19 pandemic prompted an increased interest in seasonal human coronaviruses. OC43, 229E, NL63, and HKU1 are endemic seasonal coronaviruses that cause the common cold and are associated with generally mild respiratory symptoms. In this study, we identified cell lines that exhibited cytopathic effects (CPE) upon infection by three of these coronaviruses and characterized their viral replication kinetics and the effect of infection on host surface receptor expression. We found that NL63 produced CPE in LLC-MK2 cells, while OC43 produced CPE in MRC-5, HCT-8, and WI-38 cell lines, while 229E produced CPE in MRC-5 and WI-38 by day 3 post-infection. We observed a sharp increase in nucleocapsid and spike viral RNA (vRNA) from day 3 to day 5 post-infection for all viruses; however, the abundance and the proportion of vRNA copies measured in the supernatants and cell lysates of infected cells varied considerably depending on the virus-host cell pair. Importantly, we observed modulation of coronavirus entry and attachment receptors upon infection. Infection with 229E and OC43 led to a downregulation of CD13 and GD3, respectively. In contrast, infection with NL63 and OC43 leads to an increase in ACE2 expression. Attempts to block entry of NL63 using either soluble ACE2 or anti-ACE2 monoclonal antibodies demonstrated the potential of these strategies to greatly reduce infection. Overall, our results enable a better understanding of seasonal coronaviruses infection kinetics in permissive cell lines and reveal entry receptor modulation that may have implications in facilitating co-infections with multiple coronaviruses in humans.IMPORTANCESeasonal human coronavirus is an important cause of the common cold associated with generally mild upper respiratory tract infections that can result in respiratory complications for some individuals. There are no vaccines available for these viruses, with only limited antiviral therapeutic options to treat the most severe cases. A better understanding of how these viruses interact with host cells is essential to identify new strategies to prevent infection-related complications. By analyzing viral replication kinetics in different permissive cell lines, we find that cell-dependent host factors influence how viral genes are expressed and virus particles released. We also analyzed entry receptor expression on infected cells and found that these can be up- or down-modulated depending on the infecting coronavirus. Our findings raise concerns over the possibility of infection enhancement upon co-infection by some coronaviruses, which may facilitate genetic recombination and the emergence of new variants and strains.

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.

Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing

Synthesis of unavailable imaging modalities from available ones can generate modality-specific complementary information and enable multi-modality based medical images diagnosis or treatment. Existing generative methods for medical image synthesis are usually based on cross-modal translation between acquired and missing modalities. These methods are usually dedicated to specific missing modality and perform synthesis in one shot, which cannot deal with varying number of missing modalities flexibly and construct the mapping across modalities effectively. To address the above issues, in this paper, we propose a unified Multi-modal Modality-masked Diffusion Network (M2DN), tackling multi-modal synthesis from the perspective of "progressive whole-modality inpainting", instead of "cross-modal translation". Specifically, our M2DN considers the missing modalities as random noise and takes all the modalities as a unity in each reverse diffusion step. The proposed joint synthesis scheme performs synthesis for the missing modalities and self-reconstruction for the available ones, which not only enables synthesis for arbitrary missing scenarios, but also facilitates the construction of common latent space and enhances the model representation ability. Besides, we introduce a modality-mask scheme to encode availability status of each incoming modality explicitly in a binary mask, which is adopted as condition for the diffusion model to further enhance the synthesis performance of our M2DN for arbitrary missing scenarios. We carry out experiments on two public brain MRI datasets for synthesis and downstream segmentation tasks. Experimental results demonstrate that our M2DN outperforms the state-of-the-art models significantly and shows great generalizability for arbitrary missing modalities.

Nanomaterials-based electrochemical biosensors for diagnosis of COVID-19

Since the outbreak of corona virus disease 2019 (COVID-19), this pandemic has caused severe death and infection worldwide. Owing to its strong infectivity, long incubation period, and nonspecific symptoms, the early diagnosis is essential to reduce risk of the severe illness. The electrochemical biosensor, as a fast and sensitive technique for quantitative analysis of body fluids, has been widely studied to diagnose different biomarkers caused at different infective stages of COVID-19 virus (SARS-CoV-2). Recently, many reports have proved that nanomaterials with special architectures and size effects can effectively promote the biosensing performance on the COVID-19 diagnosis, there are few comprehensive summary reports yet. Therefore, in this review, we will pay efforts on recent progress of advanced nanomaterials-facilitated electrochemical biosensors for the COVID-19 detections. The process of SARS-CoV-2 infection in humans will be briefly described, as well as summarizing the types of sensors that should be designed for different infection processes. Emphasis will be supplied to various functional nanomaterials which dominate the biosensing performance for comparison, expecting to provide a rational guidance on the material selection of biosensor construction for people. Finally, we will conclude the perspective on the design of superior nanomaterials-based biosensors facing the unknown virus in future.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Immunization against Medically Important Human Coronaviruses of Public Health Concern

SARS-CoV-2 is a virus that affects the human immune system. It was observed to be on the rise since the beginning of 2020 and turned into a life-threatening pandemic. Scientists have tried to develop a possible preventive and therapeutic drug against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and other related coronaviruses by assessing COVID-19-recovered persons' immunity. This study aims to review immunization against SARS-CoV-2, along with exploring the interventions that have been developed for the prevention of SARS-CoV-2. This study also highlighted the role of phototherapy in treating SARS-CoV infection. The study adopted a review approach to gathering the information available and the progress that has been made in the treatment and prevention of COVID-19. Various vaccinations, including nucleotide, subunit, and vector-based vaccines, as well as attenuated and inactivated forms that have already been shown to have prophylactic efficacy against the Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV, have been summarized. Neutralizing and non-neutralizing antibodies are all associated with viral infections. Because there is no specific antiviral vaccine or therapies for coronaviruses, the main treatment strategy is supportive care, which is reinforced by combining broad-spectrum antivirals, convalescent plasma, and corticosteroids. COVID-19 has been a challenge to keep reconsidering the usual approaches to regulatory evaluation as a result of getting mixed and complicated findings on the vaccines, as well as licensing procedures. However, it is observed that medicinal herbs also play an important role in treating infection of the upper respiratory tract, the principal symptom of SARS-CoV due to their natural bioactive composite. However, some Traditional Chinese Medicines contain mutagens and nephrotoxins and the toxicological properties of the majority of Chinese herbal remedies are unknown. Therefore, to treat the COVID-19 infection along with conventional treatment, it is recommended that herb-drug interaction be examined thoroughly.

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening

The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies using zebrafish, the microscopic images and videos that illustrate the developmental stages, phenotypic morphologies, and animal behaviors possess great potential to facilitate rapid hazard assessment and dissection of the toxicity mechanism of environmental pollutants. However, the traditional manual observation approach is both labor-intensive and time-consuming. In this Perspective, we aim to summarize the current AI-enabled image and video analysis tools to realize the full potential of AI. For image analysis, AI-based tools allow fast and objective determination of morphological features and extraction of quantitative information from images of various sorts. The advantages of providing accurate and reproducible results while avoiding human intervention play a critical role in speeding up the screening process. For video analysis, AI-based tools enable the tracking of dynamic changes in both microscopic cellular events and macroscopic animal behaviors. The subtle changes revealed by video analysis could serve as sensitive indicators of adverse outcomes. With AI-based toxicity analysis in its infancy, exciting developments and applications are expected to appear in the years to come.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia

Purpose

This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.

Materials and methods

In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively.

Results

For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35.

Conclusion

The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Association Between Artificial Intelligence Based Chest Computed Tomography and Clinical/Laboratory Characteristics with Severity and Mortality in COVID-19 Hospitalized Patients

Background

Some patients with COVID-19 rapidly develop respiratory failure or mortality, underscoring the necessity for early identification of those prone to severe illness. Numerous studies focus on clinical and lab traits, but only few attend to chest computed tomography. The current study seeks to numerically quantify pulmonary lesions using early-phase CT scans calculated through artificial intelligence algorithms in conjunction with clinical and laboratory helps clinicians to early identify the development of severe illness and death in a group of COVID-19 patients.

Methods

From December 15, 2022, to January 30, 2023, 191 confirmed COVID-19 patients admitted to Xinhua Hospital Affiliated with Shanghai Jiao Tong University School of Medicine were consecutively enrolled. All patients underwent chest CT scans and serum tests within 48 hours prior to admission. Variables significantly linked to critical illness or mortality in univariate analysis were subjected to multivariate logistic regression models post collinearity assessment. Adjusted odds ratio, 95% confidence intervals, sensitivity, specificity, Youden index, receiver-operator-characteristics (ROC) curves, and area under the curve (AUC) were computed for predicting severity and in-hospital mortality.

Results

Multivariate logistic analysis revealed that myoglobin (OR = 1.003, 95% CI 1.001-1.005), APACHE II score (OR = 1.387, 95% CI 1.216-1.583), and the infected CT region percentage (OR = 113.897, 95% CI 4.939-2626.496) independently correlated with in-hospital COVID-19 mortality. Prealbumin stood as an independent safeguarding factor (OR = 0.965, 95% CI 0.947-0.984). Neutrophil counts (OR = 1.529, 95% CI 1.131-2.068), urea nitrogen (OR = 1.587, 95% CI 1.222-2.062), SOFA score(OR = 3.333, 95% CI 1.476-7.522), qSOFA score(OR = 15.197, 95% CI 3.281-70.384), PSI score(OR = 1.053, 95% CI 1.018-1.090), and the infected CT region percentage (OR = 548.221, 95% CI 2.615-114,953.586) independently linked to COVID-19 patient severity.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days

Objectives

This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT).

Methods

From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results.

Results

Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful.

Conclusion

The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Validation of single reference variable flip angle (SR-VFA) dynamic T1 mapping with T2 * correction using a novel rotating phantom

PURPOSE

To validate single reference variable flip angle (SR-VFA) dynamic T1 mapping with and without T2 * correction against inversion recovery (IR) T1 measurements.

METHODS

A custom cylindrical phantom with three concentric compartments was filled with variably doped agar to produce a smooth spatial gradient of the T1 relaxation rate as a function of angle across each compartment. IR T1 , VFA T1 , and B1 + measurements were made on the phantom before rotation, and multi-echo stack-of-radial dynamic images were acquired during rotation via an MRI-compatible motor. B1 + -corrected SR-VFA and SR-VFA-T2 * T1 maps were computed from the sliding window reconstructed images and compared against rotationally registered IR and VFA T1 maps to determine the percentage error.

RESULTS

Both VFA and SR-VFA-T2 * T1 maps fell within 10% of IR T1 measurements for a low rotational speed, with a mean accuracy of 2.3% ± 2.6% and 2.8% ± 2.6%, respectively. Increasing rotational speed was found to decrease the accuracy due to increasing temporal smoothing over ranges where the T1 change had a nonconstant slope. SR-VFA T1 mapping was found to have similar accuracy as the SR-VFA-T2 * and VFA methods at low TEs (˜<2 ms), whereas accuracy degraded strongly with later TEs. T2 * correction of the SR-VFA T1 maps was found to consistently improve accuracy and precision, especially at later TEs.

CONCLUSION

SR-VFA-T2 * dynamic T1 mapping was found to be accurate against reference IR T1 measurements within 10% in an agar phantom. Further validation is needed in mixed fat-water phantoms and in vivo.

Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends

Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.

The usage of dental cone-beam computed tomography during the COVID-19 pandemic (from 2020 to 2022): A survey of a regional hospital in the northern Taiwan

Background/purpose

In Taiwan, cone-beam computed tomography (CBCT) has already widely used in dentistry. This study explored preliminarily the usage of dental CBCT during the COVID-19 pandemic (from 2020 to 2022) through a survey of a regional hospital in the northern Taiwan.

Materials and methods

This study used purposeful sampling to select a regional hospital in the northern Taiwan to survey its usage of dental CBCT during the COVID-19 pandemic.

Results

In the surveyed hospital, the number of patients' visits for the usage of dental CBCT increased from 355 in 2020 to 449 in 2021 and further to 488 in 2022 with a growth rate of 37.46 %, while the growth rates compared to the previous year were 26.48 % in 2021 and 8.69 % in 2022, respectively. There were a total of 1292 patients' visits for the dental CBCT. The ages of the 1292 patients (573 males and 719 females) ranged from 4 to 89 years. The 50-59-year age group had the highest number of patients' visits (371, 28.72 %), followed in a descending order by the 60-69-year (293, 22.68 %) and 40-49-year (206, 15.94 %) age groups. The dental CBCT was used mainly for the assessment of dental implants, accounting for 1148 (78.85 %) of the total 1456 irradiations.

Conclusion

During the COVID-19 pandemic, the medical services for dental care and treatments in Taiwan are still maintained normally, and the dental CBCT is also used widely and popularly by the dental patients of all ages, various dental procedures, and various dental specialties.

Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19

Changes in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

Aspergillus fumigatus secretes a protease(s) that displays in silico binding affinity towards the SARS-CoV-2 spike protein and mediates SARS-CoV-2 pseudovirion entry into HEK-293T cells

BACKGROUND

The novel coronavirus disease of 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Data from the COVID-19 clinical control case studies showed that this disease could also manifest in patients with underlying microbial infections such as aspergillosis. The current study aimed to determine if the Aspergillus (A.) fumigatus culture media (i.e., supernatant) possessed protease activity that was sufficient to activate the SARS-CoV-2 spike protein.

METHODS

The supernatant was first analysed for protease activity. Thereafter, it was assessed to determine if it possessed proteolytic activity to cleave a fluorogenic mimetic peptide of the SARS-CoV-2 spike protein that contained the S1/S2 site and a full-length spike protein contained in a SARS-CoV-2 pseudovirion. To complement this, a computer-based tool, HADDOCK, was used to predict if A. fumigatus alkaline protease 1 could bind to the SARS-CoV-2 spike protein.

RESULTS

We show that the supernatant possessed proteolytic activity, and analyses of the molecular docking parameters revealed that A. fumigatus alkaline protease 1 could bind to the spike protein. To confirm the in silico data, it was imperative to provide experimental evidence for enzymatic activity. Here, it was noted that the A. fumigatus supernatant cleaved the mimetic peptide as well as transduced the HEK-293T cells with SARS-CoV-2 pseudovirions.

CONCLUSION

These results suggest that A. fumigatus secretes a protease(s) that activates the SARS-CoV-2 spike protein. Importantly, should these two infectious agents co-occur, there is the potential for A. fumigatus to activate the SARS-CoV-2 spike protein, thus aggravating COVID-19 development.

Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T1 and T2 mapping of entire macaque brain

PURPOSE

Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system.

METHODS

Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B1 + ) measurements for robustness against B1 + inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.

RESULTS

The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35× $$ \times $$ 0.35× $$ \times $$ 1 mm3 can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol.

CONCLUSION

Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.