Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma

BACKGROUND

The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma (lrNPC) is limited due to their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in lrNPC.

METHODS

This multicenter, retrospective study included 921 patients with lrNPC. A machine learning signature and nomogram based on pretreatment MRI features were developed for predicting overall survival (OS) in a training cohort and validated in two independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index (C-index) and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA sequencing (RNA-seq) analysis.

RESULTS

The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving C-indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables significantly improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with significantly distinct OS rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-seq analysis revealed differences in interferon response and lymphocyte infiltration between risk groups.

CONCLUSIONS

An MRI-based radiomic signature predicted OS more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for lrNPC patients.

Artificial intelligence in liver imaging: methods and applications

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.

Government responses to the COVID-19 pandemic of the Gulf Cooperation Council countries: good practices and lessons for future preparedness

The COVID-19 pandemic has dramatically threatened the Gulf Cooperation Council (GCC) countries which have a large proportion of foreign workers. The governments of GCC countries have proactively implemented a comprehensive set of policy measures, and up to our knowledge, a systematic analysis of qualitative and quantitative evidence on the government response is still lacking. We summarized the GCC countries' government response and quantitatively measured that response using four indexes-the Government Response Index, the Stringency Index, the Vaccine Index, and the Initial Response Index, to analyse their response for future pandemic preparedness. Overall, the government response of all the GCC countries to the COVID-19 pandemic has been comprehensive, stringent, and timely. Notably, the GCC countries have implemented comprehensive vaccine policies. In addition, they have worked actively to protect foreign workers to improve their access to health services and secure their essential living conditions, regardless of their immigrant status. All the GCC countries dynamically adjusted their response to the evolving COVID-19 epidemiological burden and started to relax the stringency of the control policies after the Omicron wave, though the governments had different response magnitudes as measured by the four indexes. These findings have provided several important lessons for future pandemic response and preparedness for countries with similar economic, demographic, and health contexts in (1) prompt actions of containment and closure policies with dynamic adjusting, (2) strengthening health system policies, (3) comprehensive vaccination policies with universal access, (4) equitable and free access to testing, diagnosis, and treatment for all, and (5) strengthening the resilience of health systems.

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660-0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769-0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784-0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer

BACKGROUND

The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

PURPOSE

This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.

METHODS

A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS).

RESULTS

The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis.

CONCLUSIONS

The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study

BACKGROUND

Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC.

METHODS

This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes.

RESULTS

The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity.

CONCLUSIONS

We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

4D-MRI Guided Stereotactic Body Radiation Therapy for Unresectable Colorectal Liver Metastases

PURPOSE/OBJECTIVE(S)

This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) guided stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLM).

MATERIALS/METHODS

From March 2018 to January 2022, we identified 76 unresectable CRLM patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50-65 Gy in 5-12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated.

RESULTS

The 4D-MRI significantly improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P < 0.001). The abdominal compression significantly reduced motions in cranial-caudal direction (P = 0.03) with 2 phase T2 weighted images assessing tumor motion. The median follow-up time was 12.5 months. For 98 lesions assessed for best response, the complete response, partial response and stable disease rate were 57.1 %, 30.6 % and 12.2 %, respectively. The local control (LC) rate at 2 year was 97.3%. 46.1% of patients experienced grade 1-2 toxicities and only 2.6% patients experienced grade 3 hematologic toxicities.

CONCLUSION

The 4D-MRI technique allowed precise target delineation and motion tracking in unresectable CRLM patients. High LC rate and mild toxicities were achieved. This study provided evidence for using 4D-MRI guided SBRT as an alternative treatment in unresectable CRLM.

CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center

BACKGROUND

Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors.

METHODS

This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively.

RESULTS

A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI: 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience.

CONCLUSIONS

We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis.

IMPACT

Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.

[Impact of sinonasal anatomic changes after endoscopic anterior skull base surgery on nasal airflow and air conditioning: a computational fluid dynamics study]

Objective: To analyze the impact of the sinonasal anatomic changes after endonasal endoscopic anterior skull base surgery on the nasal airflow and heating and humidification by computational fluid dynamics (CFD), and to explore the correlation between the postoperative CFD parameters and the subjective symptoms of the patients. Methods: The clinical data in the Rhinology Department of the First Affiliated Hospital of Zhengzhou University from 2016 to 2021 were retrospectively analyzed. The patients received the endoscopic resection of the anterior skull base tumor were selected as the case group, and the adults whose CT scans had no sinonasal abnormalities were chosen as the control group. The CFD simulation was performed on the sinonasal models after reconstructed from the patients' sinus CT images during the post-surgical follow-up. All the patients were asked to complete the Empty Nose Syndrome 6-Item Questionnaire (ENS6Q) to assess the subjective symptoms. The comparison between two independent groups and the correlation analysis were carried out by using the Mann-Whitney U test and the Spearman correlation test in the SPSS 26.0 software. Results: Nineteen patients (including 8 males and 11 females, from 22 to 67 years old) in the case group and 2 patients (a male of 38 years old and a female of 45 years old) in the control group were enrolled in this study. After the anterior skull base surgery, the high-speed airflow moved to the upper part of the nasal cavity, and the lowest temperature shifted upwards on the choana. Comparing with the control group, the ratio of nasal mucosal surface area to nasal ventilation volume in the case group decreased [0.41 (0.40, 0.41) mm^-1 vs 0.32 (0.30, 0.38) mm^-1; Z=-2.04, P=0.041], the air flow in the upper and middle part of the nasal cavity increased [61.14 (59.78, 62.51)% vs 78.07 (76.22, 94.43)%; Z=-2.28, P=0.023], the nasal resistance decreased [0.024 (0.022, 0.026) Pa·s/ml vs 0.016 (0.009, 0.018) Pa·s/ml; Z=-2.29, P=0.022], the lowest temperature in the middle of the nasal cavity decreased [28.29 (27.23, 29.35)℃ vs 25.06 (24.07, 25.50)℃; Z=-2.28, P=0.023], the nasal heating efficiency decreased [98.74 (97.95, 99.52)% vs 82.16 (80.24, 86.91)%; Z=-2.28, P=0.023], the lowest relative humidity decreased [(79.62 (76.55, 82.69)% vs 73.28 (71.27, 75.05)%; Z=-2.28, P=0.023], and the nasal humidification efficiency decreased [99.50 (97.69, 101.30)% vs 86.09 (79.33, 87.16)%; Z=-2.28, P=0.023]. The ENS6Q total scores of all patients in the case group were less than 11 points. There was a moderate negative correlation between the proportion of the inferior airflow in the post-surgical nasal cavity negatively and the ENS6Q total scores (r_s=-0.50, P=0.029). Conclusions: The sinonasal anatomic changes after the endoscopic anterior skull base surgery alter the nasal airflow patterns, reducing the efficiency of nasal heating and humidification. However, the post-surgical occurrence tendency of the empty nose syndrome is weak.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.

Genome-wide identification, expression analysis, and transcriptome analysis of the IAA gene family in Zoysia japonica

BACKGROUND

AUX/IAA is an essential signaling molecule and has great physiological importance in various plants, but its function in Zoysia japonica remains unknown.

METHODS AND RESULTS

Genome-wide identification and analysis of AUX/IAA genes used bioinformatics methods to investigate the ZjIAA genes' expression of exogenous IAA hydroponics treatment for 2 h by qRT-PCR, control and exogenous IAA treated zoysia were subjected to transcriptome sequencing. ZjIAAs were distributed across the 13 subfamilies by phylogenetic analysis with Oryza sativa and Arabidopsis thaliana. Multiple sequence alignment revealed that the majority of genes were non-canonical ZjIAAs with incomplete domain. The optimal growth concentration of the IAA hormone was 0.05 mM, and the qRT-PCR analysis revealed that eight ZjIAAs were differentially expressed, with seven genes considerably upregulating and one gene significantly downregulating. The result of transcriptome sequencing revealed that 515 differentially expressed genes (DEGs) were identified, with 344 upregulated genes and 171 downregulated genes. A total of 18 genes were annotated as involved in the plant hormone signal transduction pathway. And 8 ZjIAAs exhibited distinct expressions, 7 upregulated, and only one downregulated, according to the qRT-PCR study.

CONCLUSIONS

Genome-wide identification and analysis increased the understanding of the evolution and function of the IAA family in zoysia. DEGs of control and treatment with 0.05 mM exogenous IAA hormone were investigated by transcriptome sequencing. ZjIAAs had substantial variations in the expression of associated genes, with the majority of genes upregulated and 18 genes implicated in plant hormone signal transduction.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study

Background

This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making.

Methods

A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts.

Results

The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts.

Conclusions

The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Responsiveness of the healthcare system in the Kingdom of Saudi Arabia: evidence from a nationally representative survey

BACKGROUND

Responsiveness is one of the widely used metrics in assessing the performance of healthcare systems. An analysis of the determinants of health care demand and supply and how the Saudi health system responds to the needs of patients (inpatient and outpatient) is needed; hence the need for this study.

METHODS

We analysed data from the Saudi Health Systems Responsiveness survey - a nationally representative survey of 10,000 households interviewed in 2017. Using this dataset, we descriptively analysed the level of responsiveness of inpatient and outpatient services (using the standard World Health Organization (WHO) responsiveness dimensions). Based on a logit modelling approach, the relationship between responsiveness and its key determinants was analysed in terms of healthcare demand and supply.

RESULTS

Over four fifths of respondents are satisfied with the level of inpatient and outpatient responsiveness. Furthermore, we find that those in bad health tend to show lower levels of satisfaction with inpatient and outpatient care. We also find some evidence that age, gender, and to some extent nationality act as correlates of health system responsiveness. Specifically, we find evidence that Saudi nationals are less satisfied with health services compared to foreign nationals.

CONCLUSION

Based on these findings improving the responsiveness of public healthcare facilities would need to be prioritized. Focusing on patients in worse health and lower socio-economic status should also be one of the main priorities.

Overexpression of MsSAG113 gene promotes leaf senescence in alfalfa via participating in the hormone regulatory network

Introduction

Alfalfa (Medicago sativa) is a kind of high quality leguminous forage species, which was widely cultivated in the world. Leaf senescence is an essential process in plant development and life cycle. Here, we reported the isolation and functional analysis of an alfalfa SENESCENCE-ASSOCIATED GENE113 (MsSAG113), which belongs to the PP2C family and mainly plays a role in promoting plant senescence.

Methods

In the study, Agrobacterium-mediated, gene expression analysis, next generation sequencing, DNA pull-down, yeast single hybridization and transient expression were used to identify the function of MsSAG113 gene.

Results

The MsSAG113 gene was isolated from alfalfa, and the transgenic plants were obtained by Agrobacterium-mediated method. Compared with the wildtype, transgenic plants showed premature senescence in leaves, especially when cultivated under dark conditions. Meanwhile, application of exogenous hormones ABA, SA, MeJA, obviously acclerated leaf senescence of transgenic plants. Furthermore, the detached leaves from transgenic plants turned yellow earlier with lower chlorophyll content. Transcriptome analysis identified a total of 1,392 differentially expressed genes (DEGs), involving 13 transcription factor families. Of which, 234 genes were related to phytohormone synthesis, metabolism and transduction. Pull-down assay and yeast one-hybrid assay confirmed that alfalfa zinc finger CCCH domain-containing protein 39 (MsC3H-39) could directly bind the upstream of MsSAG113 gene. In conclusion, the MsSAG113 gene plays a crucial role in promoting leaf senescence in alfalfa via participating in the hormone regulatory network.

Discussion

This provides an essential basis for further analysis on the regulatory network involving senescence-associated genes in alfalfa.

Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging

BACKGROUND AND AIMS

The detection rate for early gastric cancer (EGC) is unsatisfactory, and mastering the diagnostic skills of magnifying endoscopy with narrow-band imaging (ME-NBI) requires rich expertise and experience. We aimed to develop an EGC captioning model (EGCCap) to automatically describe the visual characteristics of ME-NBI images for endoscopists.

METHODS

ME-NBI images (n = 1886) from 294 cases were enrolled from multiple centers, and corresponding 5658 text data were designed following the simple EGC diagnostic algorithm. An EGCCap was developed using the multiscale meshed-memory transformer. We conducted comprehensive evaluations for EGCCap including the quantitative and quality of performance, generalization, robustness, interpretability, and assistant value analyses. The commonly used metrics were BLEUs, CIDEr, METEOR, ROUGE, SPICE, accuracy, sensitivity, and specificity. Two-sided statistical tests were conducted, and statistical significance was determined when P < .05.

RESULTS

EGCCap acquired satisfying captioning performance by outputting correctly and coherently clinically meaningful sentences in the internal test cohort (BLEU1 = 52.434, CIDEr = 36.734, METEOR = 27.823, ROUGE = 49.949, SPICE = 35.548) and maintained over 80% performance when applied to other centers or corrupted data. The diagnostic ability of endoscopists improved with the assistance of EGCCap, which was especially significant (P < .05) for junior endoscopists. Endoscopists gave EGCCap an average remarkable score of 7.182, showing acceptance of EGCCap.

CONCLUSIONS

EGCCap exhibited promising captioning performance and was proven with satisfying generalization, robustness, and interpretability. Our study showed potential value in aiding and improving the diagnosis of EGC and facilitating the development of automated reporting in the future.

Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study

Background

This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predicting major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) and further explore the biological basis under its prediction.

Methods

274 patients undergoing curative surgery after neoadjuvant chemoimmunotherapy for NSCLC at 4 centres from January 2019 to December 2021 were included and divided into a training cohort, an internal validation cohort, and an external validation cohort. ShuffleNetV2x05-based features of the primary tumour on the CT scans within the 2 weeks preceding neoadjuvant administration were employed to develop a deep learning score for distinguishing MPR and non-MPR. To reveal the underlying biological basis of the deep learning score, a genetic analysis was conducted based on 25 patients with RNA-sequencing data.

Findings

MPR was achieved in 54.0% (n = 148) patients. The area under the curve (AUC) of the deep learning score to predict MPR was 0.73 (95% confidence interval [CI]: 0.58–0.86) and 0.72 (95% CI: 0.58–0.85) in the internal validation and external validation cohorts, respectively. After integrating the clinical characteristic into the deep learning score, the combined model achieved satisfactory performance in the internal validation (AUC: 0.77, 95% CI: 0.64–0.89) and external validation cohorts (AUC: 0.75, 95% CI: 0.62–0.87). In the biological basis exploration for the deep learning score, a high deep learning score was associated with the downregulation of pathways mediating tumour proliferation and the promotion of antitumour immune cell infiltration in the microenvironment.

Interpretation

The proposed deep learning model could effectively predict MPR in NSCLC patients treated with neoadjuvant chemoimmunotherapy.

Funding

This study was supported by National Key Research and Development Program of China, China (2017YFA0205200); National Natural Science Foundation of China, China (91959126, 82022036, 91959130, 81971776, 81771924, 6202790004, 81930053, 9195910169, 62176013, 8210071009); Beijing Natural Science Foundation, China (L182061); Strategic Priority Research Program of Chinese Academy of Sciences, China (XDB38040200); Chinese Academy of Sciences, China (GJJSTD20170004, QYZDJ-SSW-JSC005); Shanghai Hospital Development Center, China (SHDC2020CR3047B); and Science and Technology Commission of Shanghai Municipality, China (21YF1438200).

Artificial intelligence in gastric cancer: applications and challenges

Gastric cancer (GC) is one of the most common malignant tumors with high mortality. Accurate diagnosis and treatment decisions for GC rely heavily on human experts' careful judgments on medical images. However, the improvement of the accuracy is hindered by imaging conditions, limited experience, objective criteria, and inter-observer discrepancies. Recently, the developments of machine learning, especially deep-learning algorithms, have been facilitating computers to extract more information from data automatically. Researchers are exploring the far-reaching applications of artificial intelligence (AI) in various clinical practices, including GC. Herein, we aim to provide a broad framework to summarize current research on AI in GC. In the screening of GC, AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation. In the diagnosis of GC, AI can support tumor-node-metastasis (TNM) staging and subtype classification. For treatment decisions, AI can help with surgical margin determination and prognosis prediction. Meanwhile, current approaches are challenged by data scarcity and poor interpretability. To tackle these problems, more regulated data, unified processing procedures, and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

Rare lysosomal disease registries: lessons learned over three decades of real-world evidence

Lysosomal storage disorders (LSD) are rare diseases, caused by inherited deficiencies of lysosomal enzymes/transporters, that affect 1 in 7000 to 1 in 8000 newborns. Individuals with LSDs face long diagnostic journeys during which debilitating and life-threatening events can occur. Clinical trials and classical descriptions of LSDs typically focus on common manifestations, which are not representative of the vast phenotypic heterogeneity encountered in real-world experience. Additionally, recognizing that there was a limited understanding of the natural history, disease progression, and real-world clinical outcomes of rare LSDs, a collaborative partnership was pioneered 30 years ago to address these gaps. The Rare Disease Registries (RDR) (for Gaucher, Fabry, Mucopolysaccharidosis type I, and Pompe), represent the largest observational database for these LSDs. Over the past thirty years, data from the RDRs have helped to inform scientific understanding and the development of comprehensive monitoring and treatment guidelines by creating a framework for data collection and establishing a standard of care, with an overarching goal to improve the quality of life of affected patients. Here, we highlight the history, process, and impact of the RDRs, and discuss the lessons learned and future directions.

Overexpression of abscisic acid-insensitive gene ABI4 from Medicago truncatula, which could interact with ABA2, improved plant cold tolerance mediated by ABA signaling

ABI4 is considered an important transcription factor with multiple regulatory functions involved in many biological events. However, its role in abiotic stresses, especially low-temperature-induced stress, is poorly understood. In this study, the MtABI4 gene was derived from M. truncatula, a widely used forage grass. Analysis of subcellular localization indicated that ABI4 was localized in the nucleus. Identification of expression characteristics showed that ABI4 was involved in the regulatory mechanisms of multiple hormones and could be induced by the low temperature. IP-MS assay revealed that MtABI4 protein could interact with xanthoxin dehydrogenase protein (ABA2). The two-hybrid yeast assay and the biomolecular fluorescence complementarity assay further supported this finding. Expression analysis demonstrated that overexpression of MtABI4 induced an increase in ABA2 gene expression both in M. truncatula and Arabidopsis, which in turn increased the ABA level in transgenic plants. In addition, the transgenic lines with the overexpression of MtABI4 exhibited enhanced tolerance to low temperature, including lower malondialdehyde content, electrical conductivity, and cell membrane permeability, compared with the wide-type lines after being cultivated for 5 days in 4°C. Gene expression and enzyme activities of the antioxidant system assay revealed the increased activities of SOD, CAT, MDHAR, and GR, and higher ASA/DHA ratio and GSH/GSSG ratio in transgenic lines. Additionally, overexpression of ABI4 also induced the expression of members of the Inducer of CBF expression genes (ICEs)-C-repeat binding transcription factor genes(CBFs)-Cold regulated genes (CORs) low-temperature response module. In summary, under low-temperature conditions, overexpression of ABI4 could enhance the content of endogenous ABA in plants through interactions with ABA2, which in turn reduced low-temperature damage in plants. This provides a new perspective for further understanding the molecular regulatory mechanism of plant response to low temperature and the improvement of plant cold tolerance.

How Noise Can Influence Experience-Based Decision-Making under Different Types of the Provided Information

Pervasive noise undermines many cognitive processes. Across two studies, we examined how noise influences experience-based decision-making and whether the nature of the information provided moderates this influence. Study 1 used the repeated choice paradigm and found that noise can significantly reduce people's performance in experience-based decision-making by increasing the likelihood of choosing the option with the lower expected value. This negative influence can be attenuated when experience-congruent suggestions are provided, but significantly worsened when experience-incongruent suggestions are provided. Study 2 investigated how noise influences decision-making performance in two experience-incongruent conditions differing in error salience. By replicating noise's general negative effect, we found that the noise effect could be attenuated when incongruent suggestions were obvious. We suggest that noise can undermine the information updating and integration process, which is necessary for experience-based decision-making. We also discuss the principles for designing better information aids based on these findings.

[Comparison of application effects of colonoscopy, fecal immunochemical test and a novel risk-adapted screening approach in colorectal cancer screening in Xuzhou population]

Objective: To compare the application effect of the colonoscopy, fecal immunochemical test (FIT) and novel risk-adapted screening approach in colorectal cancer screening in Xuzhou population. Methods: From May 2018 to April 2019, 4 280 subjects aged 50-74 were recruited from Gulou district, Yunlong district and Quanshan district of Xuzhou. They were randomly assigned to the colonoscopy group (n=863), FIT group (n=1 723) and novel risk-adapted screening approach group (n=1 694) according to the ratio of 1∶2∶2. For the novel risk-adapted screening approach group, after the risk assessment, high-risk subjects were invited to undergo colonoscopy and low-risk subjects were invited to undergo FIT examination. All FIT positive subjects were invited to undergo colonoscopy. Colonoscopy participation rate [(the number of colonoscopies completed/the number of colonoscopies invited to participate)×100%], detection rate of colorectal lesions [(the number of diagnosed patients/the number of colonoscopies completed)×100%], colonoscopy resource load (the number of colonoscopies completed/the number of diagnosed advanced tumors) and FIT resource load in each group were calculated and compared. Results: The age of all subjects was (61±6) years old, including 1 816 males (42.43%). There was no statistically significant difference in the socio-demographic characteristics of the subjects in different screening groups. The colonoscopy participation rate was 22.60% (195/863) in the colonoscopy group, 57.04% (77/135) in the FIT group, and 33.94% (149/439) in the novel risk-adapted screening approach group, respectively. The colonoscopy participation rate was higher in the FIT group than in the colonoscopy group and the novel risk-adapted screening approach group (P<0.001). The colonoscopy participation rate of novel risk-adapted screening group was significantly higher than the colonoscopy group (P<0.001). The detection rates of advanced tumors were 6.67% (13/195), 9.09% (7/77) and 8.72% (13/149), respectively, and the difference was not statistically significant (P>0.05). The colonoscopy resource load (95%CI) was 15 (13-17) in the colonoscopy group, 11 (9-14) in the FIT group and 11 (10-13) in the novel risk-adapted screening approach group, respectively. Among them, the colonoscopy resource load of high-risk individuals in the novel risk-adapted screening approach group was 12 (9-15). FIT resource loads (95%CI) were 207 (196-218) and 88 (83-94) in the FIT group and the novel risk-adapted screening approach group. Conclusion: The combined application of risk-adapted screening approach and FIT may have a good application effect in colorectal cancer screening.

Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images

IMPORTANCE

Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices.

OBJECTIVE

To develop and validate a deep learning model for screening fetuses with trisomy 21 based on ultrasonographic images.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study used data from all available cases and controls enrolled at 2 hospitals in China between January 2009 and September 2020. Two-dimensional images of the midsagittal plane of the fetal face in singleton pregnancies with gestational age more than 11 weeks and less than 14 weeks were examined. Observers were blinded to subjective fetus nuchal translucency (NT) marker measurements. A convolutional neural network was developed to construct a deep learning model. Data augmentation was applied to generate more data. Different groups were randomly selected as training and validation sets to assess the robustness of the deep learning model. The fetal NT was shown and measured. Each detection of trisomy 21 was confirmed by chorionic villus sampling or amniocentesis. Data were analyzed from March 1, 2021, to January 3, 2022.

MAIN OUTCOMES AND MEASURES

The primary outcome was detection of fetuses with trisomy 21. The receiver operating characteristic curve, metrics of accuracy, area under the curve (AUC), sensitivity, and specificity were used for model performance evaluation.

RESULTS

A total of 822 case and control participants (mean [SD] age, 31.9 [4.6] years) were enrolled in the study, including 550 participants (mean [SD] age, 31.7 [4.7] years) in the training set and 272 participants (mean [SD] age, 32.3 [4.7] years) in the validation set. The deep learning model showed good performance for trisomy 21 screening in the training (AUC, 0.98; 95% CI, 0.97-0.99) and validation (AUC, 0.95; 95% CI, 0.93-0.98) sets. The deep learning model had better detective performance for fetuses with trisomy 21 than the model with NT marker and maternal age (training: AUC, 0.82; 95% CI, 0.77-0.86; validation: AUC, 0.73; 95% CI, 0.66-0.80).

CONCLUSIONS AND RELEVANCE

These findings suggest that this deep learning model accurately screened fetuses with trisomy 21, which indicates that the model is a potential tool to facilitate universal primary screening for trisomy 21.

Expression of a Chlorophyll b Reductase Gene from Zoysia japonica Causes Changes in Leaf Color and Chlorophyll Morphology in Agrostis stolonifera

The NYC-like (NOL) enzyme is considered as an essential enzyme for chlorophyll b degradation, which catalyzes the formation of 7-hydroxymethyl chlorophyll a from chlorophyll b. The ZjNOL gene was cloned from Zoysia japonica with a completed coding sequence of 981-bp in length, encoding 326 amino acids. ZjNOL was localized on the stroma side of the thylakoid membrane, and co-localized with ZjNYC in the chloroplasts. Multiple photoregulatory elements and hormone regulatory elements were identified in the promoter region of the ZjNOL gene, and the expression level of the ZjNOL gene was dramatically up-regulated in senescence leaves, which were regulated by a variety of plant hormones. ZjNOL's ectopic expression in creeping bentgrass produced yellow leaves, thicker cortex, and smaller vascular column cells. Additionally, transgenic plants exhibited morphological alterations in their chloroplast structure, and the number of grana and thylakoids per grana stack reduced dramatically. Transgenic plants also had a lower photosynthetic rate and Fm/Fv than the control. The transgenic plants displayed a decreased chlorophyll content and a greater rate of ion leakage. The properties and activities of ZjNOL will serve as a foundation for future research into gene functions and regulatory processes.

Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?

Clinical symptoms and inflammatory markers cannot reliably distinguish the etiology of CAP, and chest radiographs have abundant information related with CAP. Hence, we developed a context-fusion convolution neural network (CNN) to explore the application of chest radiographs to distinguish the etiology of CAP in children. This retrospective study included 1769 cases of pediatric pneumonia (viral pneumonia, n = 487; bacterial pneumonia, n = 496; and mycoplasma pneumonia, n = 786). The chest radiographs of the first examination, C-reactive protein (CRP), and white blood cell (WBC) were collected for analysis. All patients were stochastically divided into training, validation, and test cohorts in a 7:1:2 ratio. Automatic lung segmentation and hand-crafted pneumonia lesion segmentation were performed, from which three image-based models including a full-lung model, a local-lesion model, and a context-fusion model were built; two clinical characteristics were used to build a clinical model, while a logistic regression model combined the best CNN model and two clinical characteristics. Our experiments showed that the context-fusion model which integrated the features of the full-lung and local-lesion had better performance than the full-lung model and local-lesion model. The context-fusion model had area under curves of 0.86, 0.88, and 0.93 in identifying viral, bacterial, and mycoplasma pneumonia on the test cohort respectively. The addition of clinical characteristics to the context-fusion model obtained slight improvement. Mycoplasma pneumonia was more easily identified compared with the other two types. Using chest radiographs, we developed a context-fusion CNN model with good performance for noninvasively diagnosing the etiology of community-acquired pneumonia in children, which would help improve early diagnosis and treatment.

Assessing China's potential for reducing primary copper demand and associated environmental impacts in the context of energy transition and "Zero waste" policies

To conserve resources and enhance the environmental performance, China has launched the "Zero waste" concept, focused on reutilization of solid waste and recovery of materials, including copper. Although several studies have assessed the copper demand and recycling, there is a lack of understanding on how different waste management options would potentially reduce primary copper demand and associated environmental impacts in China in the context of energy transition. This study addresses this gap in view of a transition to low-carbon energy system and the optimization of copper waste management combining MFA and LCA approaches. Six types of waste streams (C&DW, ELV, WEEE, IEW, MSW, ICW) are investigated in relation to various "Zero waste" strategies including reduction, reuse (repair, remanufacturing or refurbishment), recycling and transition from informal to formal waste management. Under present Chinese policies, reuse and recycling of copper containing products will lead to a somewhat lower dependency on primary copper in 2100 (11187Gg), as well as lower total GHG emissions (64869 Gg CO2-eq.) and cumulative energy demand (1.18x10^12 MJ). Maximizing such "Zero waste" options may lead to a further reduction, resulting in 65% potential reduction of primary copper demand, around 55% potential reduction of total GHG emissions and total cumulative energy demand in 2100. Several policy actions are proposed to provide insights into future waste management in China as well as some of the challenges involved.

Impact of noncommunicable diseases on direct medical costs and worker productivity, Saudi Arabia

Background: The prevalence of noncommunicable diseases (NCDs) has been increasing in Saudi Arabia. Aims: Our objective was to estimate the effect of NCDs on direct medical costs and workforce productivity in Saudi Arabia. Methods: To estimate direct medical costs, we estimated the unit cost of treating 10 NCDs, then multiplied the unit cost by disease prevalence and summed across diseases. To estimate workforce productivity losses, we multiplied gross domestic product per person in the labour force by the loss in productivity from each NCD and the prevalence in the labour force of each NCD. Results: We estimated annual direct medical costs of 11.8 billion international dollars (Int$) for the 10 NCDs assessed (13.6% of total annual health expenditure). We estimated workforce productivity losses of Int$ 75.7 billion (4.5% of gross domestic product). Conclusion: The economic burden of NCDs in Saudi Arabia – particularly the effect on worker productivity – is substantial.

Transcriptome profiling revealed candidate genes, pathways and transcription factors related to nitrogen utilization and excessive nitrogen stress in perennial ryegrass

Ryegrass (Lolium perenne L.), a high-quality forage grass, is a good nutrient source for herbivorous livestock. However, improving nitrogen use efficiency and avoiding nitrate toxicity caused by excessive nitrogen are continual challenges in ryegrass production. The molecular mechanism underlying the response of ryegrass to nitrogen, especially excessive nitrogen, remains unclear. In this study, the transcriptomic changes under different nitrogen levels were investigated in perennial ryegrass by high-throughput next-generation RNA sequencing. Phenotypic characterization showed that treatment with half of the standard N concentration (N0.5) led to a better growth state than the other three treatments. The treatments with the standard N concentration (N1) and treatments with ten times higher than the standard N concentration (N10) contained excessive nitrogen, which placed stress on plant growth. Analysis of differentially expressed genes indicated that 345 and 104 genes are involved in the regulation of nitrogen utilization and excessive nitrogen stress, respectively. KEGG enrichment analysis suggested that "photosynthesis-antenna proteins" may respond positively to appropriate nitrogen conditions, whereas "steroid biosynthesis", "carotenoid biosynthesis" and "C5-branched dibasic acid metabolism" were identified as the top significantly enriched pathways in response to excessive nitrogen. Additionally, 21 transcription factors (TFs) related to nitrogen utilization were classified into 10 families, especially the AP2-EREBP and MYB TF families. Four TFs related to excessive nitrogen stress were identified, including LOBs, NACs, AP2-EREBPs and HBs. The expression patterns of these selected genes were also analyzed. These results provide new insight into the regulatory mechanism of ryegrass in response to nitrogen utilization and excessive nitrogen stress.

Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia

BACKGROUND

The Kingdom of Saudi Arabia (KSA) quickly controlled the spread of SARS-CoV-2 by implementing several non-pharmaceutical interventions (NPIs), including suspension of international and national travel, local curfews, closing public spaces (i.e., schools and universities, malls and shops), and limiting religious gatherings. The KSA also mandated all citizens to respect physical distancing and to wear face masks. However, after relaxing some restrictions during June 2020, the KSA is now planning a strategy that could allow resuming in-person education and international travel. The aim of our study was to evaluate the effect of NPIs on the spread of the COVID-19 and test strategies to open schools and resume international travel.

METHODS

We built a spatial-explicit individual-based model to represent the whole KSA population (IBM-KSA). The IBM-KSA was parameterized using country demographic, remote sensing, and epidemiological data. A social network was created to represent contact heterogeneity and interaction among age groups of the population. The IBM-KSA also simulated the movement of people across the country based on a gravity model. We used the IBM-KSA to evaluate the effect of different NPIs adopted by the KSA (physical distancing, mask-wearing, and contact tracing) and to forecast the impact of strategies to open schools and resume international travels.

RESULTS

The IBM-KSA results scenarios showed the high effectiveness of mask-wearing, physical distancing, and contact tracing in controlling the spread of the disease. Without NPIs, the KSA could have reported 4,824,065 (95% CI: 3,673,775-6,335,423) cases by June 2021. The IBM-KSA showed that mandatory mask-wearing and physical distancing saved 39,452 lives (95% CI: 26,641-44,494). In-person education without personal protection during teaching would have resulted in a high surge of COVID-19 cases. Compared to scenarios with no personal protection, enforcing mask-wearing and physical distancing in schools reduced cases, hospitalizations, and deaths by 25% and 50%, when adherence to these NPIs was set to 50% and 70%, respectively. The IBM-KSA also showed that a quarantine imposed on international travelers reduced the probability of outbreaks in the country.

CONCLUSIONS

This study showed that the interventions adopted by the KSA were able to control the spread of SARS-CoV-2 in the absence of a vaccine. In-person education should be resumed only if NPIs could be applied in schools and universities. International travel can be resumed but with strict quarantine rules. The KSA needs to keep strict NPIs in place until a high fraction of the population is vaccinated in order to reduce hospitalizations and deaths.

Expression of ZjPSY, a Phytoene Synthase Gene from Zoysia japonica Affects Plant Height and Photosynthetic Pigment Contents

Phytoene synthase (PSY) is a key limiting enzyme in the carotenoid biosynthesis pathway for regulating phytoene synthesis. In this study, ZjPSY was isolated and identified from Zoysia japonica, an important lawn grass species. ZjPSY cDNA was 1230 bp in length, corresponding to 409 amino acids. ZjPSY showed higher expression in young leaves and was downregulated after GA₃, ABA, SA, and MeJA treatments, exhibiting a sensitivity to plant hormones. Regulatory elements of light and plant hormone were found in the upstream of ZjPSY CDS. Expression of ZjPSY in Arabidopsis thaliana protein led to carotenoid accumulation and altered expression of genes involved in the carotenoid pathway. Under no-treatment condition, salt treatment, and drought treatment, transgenic plants exhibited yellowing, dwarfing phenotypes. The carotenoid content of transgenic plants was significantly higher than that of wild-type under salt stress and no-treatment condition. Yeast two-hybrid screening identified a novel interacting partner ZjJ2 (DNAJ homologue 2), which encodes heat-shock protein 40 (HSP40). Taken together, this study suggested that ZjPSY may affect plant height and play an important role in carotenoid synthesis. These results broadened the understanding of carotenoid synthesis pathways and laid a foundation for the exploration and utilization of the PSY gene.

Impact of noncommunicable diseases on direct medical costs and worker productivity, Saudi Arabia

Background: The prevalence of noncommunicable diseases (NCDs) has been increasing in Saudi Arabia. Aim: Our objective was to estimate the effect of NCDs on direct medical costs and workforce productivity in Saudi Arabia. Methods: To estimate direct medical costs, we estimated the unit cost of treating 10 NCDs, then multiplied the unit cost by disease prevalence and summed across diseases. To estimate workforce productivity losses, we multiplied gross domestic product per person in the labour force by the loss in productivity from each NCD and the prevalence in the labour force of each NCD. Results: We estimated annual direct medical costs of 11.8 billion international dollars (Int$) for the 10 NCDs assessed (13.6% of total annual health expenditure). We estimated workforce productivity losses of Int$ 75.7 billion (4.5% of gross domestic product). Conclusion: The economic burden of NCDs in Saudi Arabia – particularly the effect on worker productivity – is substantial.

Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer

PURPOSE

We aimed to develop a noninvasive artificial intelligence (AI) model to diagnose signet-ring cell carcinoma (SRCC) of gastric cancer (GC) and identify patients with SRCC who could benefit from postoperative chemotherapy based on preoperative contrast-enhanced computed tomography (CT).

METHODS

A total of 855 GC patients with 855 single GCs were included, of which 249 patients were diagnosed as SRCC by histopathologic examinations. The AI model was generated with clinical, hand-crafted radiomic, and deep learning features. Model diagnostic performance was measured by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, while predictive performance was measured by Kaplan-Meier curves.

RESULTS

In the test cohort (n = 257), the AUC, sensitivity, and specificity of our AI model for diagnosing SRCC were 0.786 (95% CI: 0.721-0.845), 77.3%, and 69.2%, respectively. For the entire cohort, patients with AI-predicted high risk had a significantly shorter median OS compared with those with low risk (median overall survival (OS), 38.8 vs. 64.2 months, p = 0.009). Importantly, in pathologically-confirmed advanced SRCC patients, AI-predicted high-risk status was indicative of a shorter overall survival (median overall survival (OS), 31.0 vs. 54.4 months, p = 0.036) and marked chemotherapy resistance, whereas AI-predicted low-risk status had substantial chemotherapy benefit (median OS [without vs. with chemotherapy], 26.0 vs. not reached, p = 0.013).

CONCLUSIONS

The CT-based AI model demonstrated good performance for diagnosing SRCC, stratifying patient prognosis, and predicting chemotherapy responses. Advanced SRCC patients with AI-predicted low-risk status may benefit substantially from adjuvant chemotherapy. This article is protected by copyright. All rights reserved.

Semi-supervised Long-tail Endoscopic Image Classification

Objective To explore the semi-supervised learning (SSL) algorithm for long-tail endoscopic image classification with limited annotations. Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir, the largest gastrointestinal public dataset with 23 diverse classes. Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling. After splitting the training dataset and the test dataset at a ratio of 4:1, we sampled 20%, 50%, and 100% labeled training data to test the classification with limited annotations. Results The classification performance was evaluated by micro-average and macro-average evaluation metrics, with the Mathews correlation coefficient (MCC) as the overall evaluation. SSL algorithm improved the classification performance, with MCC increasing from 0.8761 to 0.8850, from 0.8983 to 0.8994, and from 0.9075 to 0.9095 with 20%, 50%, and 100% ratio of labeled training data, respectively. With a 20% ratio of labeled training data, SSL improved both the micro-average and macro-average classification performance; while for the ratio of 50% and 100%, SSL improved the micro-average performance but hurt macro-average performance. Through analyzing the confusion matrix and labeling bias in each class, we found that the pseudo-based SSL algorithm exacerbated the classifier's preference for the head class, resulting in improved performance in the head class and degenerated performance in the tail class. Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification, especially when the labeled data is extremely limited, which may benefit the building of assisted diagnosis systems for low-volume hospitals. However, the pseudo-labeling strategy may amplify the effect of class imbalance, which hurts the classification performance for the tail class.

Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia

Non-pharmaceutical interventions have been widely employed to control the COVID-19 pandemic. Their associated effect on SARS-CoV-2 transmission have however been unequally studied across regions. Few studies have focused on the Gulf states despite their potential role for global pandemic spread, in particular in the Kingdom of Saudi Arabia through religious pilgrimages. We study the association between NPIs and SARS-CoV-2 transmission in the Kingdom of Saudi Arabia during the first pandemic wave between March and October 2020. We infer associations between NPIs introduction and lifting through a spatial SEIR-type model that allows for inferences of region-specific changes in transmission intensity. We find that reductions in transmission were associated with NPIs implemented shortly after the first reported case including Isolate and Test with School Closure (region-level mean estimates of the reduction in R0 ranged from 25-41%), Curfew (20-70% reduction), and Lockdown (50-60% reduction), although uncertainty in the estimates was high, particularly for the Isolate and Test with School Closure NPI (95% Credible Intervals from 1% to 73% across regions). Transmission was found to increase progressively in most regions during the last part of NPI relaxation phases. These results can help informing the policy makers in the planning of NPI scenarios as the pandemic evolves with the emergence of SARS-CoV-2 variants and the availability of vaccination.

Effects of Elevation and Distance from Highway on the Abundance and Community Structure of Bacteria in Soil along Qinghai-Tibet Highway

In recent years, highway construction in the Qinghai-Tibet Plateau (QTP) has developed rapidly. When the highway passes through grassland, the soil, vegetation, and ecological environment along the line are disturbed. However, the impact on soil bacteria is still unclear. Soil bacteria play an important role in the ecological environment. The Qinghai-Tibet Highway (QTH) was selected as the research object to explore the changes in bacterial community structure, vegetation, soil, and other indicators. The results showed that the highway-related activities increased the degradation of vegetation along the road, significantly changed the physical and chemical properties of soil, and caused heavy metal pollution. These environmental factors affected the diversity and community structure of soil bacteria. This kind of disturbance shows a trend of gradually increasing from near to far from the highway. Gemmatimonas, Terrimonas, Nitrospira and Bacillus are more tolerant to environmental changes along the highway, while Barnesiella, and Blastococcus are more sensitive. The content of nitrate decreased and the content of ammonium nitrogen increased in the disturbed area, increasing the abundance of nitrifying bacteria. Therefore, the main factor of the disturbance of the QTH on the grassland is the decline of soil nutrient content, and the supplement of soil nutrients such as carbon and nitrogen should be taken into account in the process of ecological restoration of grassland along the line.

Self-Attention Based Virtual Staining for Bright-field Images of Label-free Human Carotid Atherosclerotic Plaque Tissue Section

Histological analysis of carotid atherosclerotic plaque tissue specimens is a widely used method for studying the diagnosis of ischemic heart disease and stroke. Understanding the physiological and pathological mechanisms of carotid atherosclerotic plaque is of great significance for the effective prevention and treatment of plaque formation and rupture. In this work, we adapted a self-attention generative adversarial model to virtually stain label-free human carotid atherosclerotic plaque tissue sections into corresponding H&E stained sections. The self-attention mechanism and multi-layer structure are introduced into the residual steps of the generator and in the discriminator. Our method achieved the best performance (SSIM, PSNR, and LPIPS of 0.53, 20.29, and 0.30, respectively) in comparison with other state-of-the-art methods.Clinical Relevance - The proposed approach allows for the virtual staining of unlabeled human carotid plaque tissue images. It identifies the histopathological features of atherosclerotic plaques in the same tissue sample which could facilitate the development of personalized prevention and other interventional treatments for carotid atherosclerosis.

Cross-Phase Adversarial Domain Adaptation for Deep Disease-free Survival Prediction with Gastric Cancer CT Images

Predicting gastric cancer disease-free survival (DFS) and identifying patients probably with high risk are imperative for more appropriate clinical treatment plans. Compared with CT-based radiomics researches adopting linear Cox proportional hazards models, deep neural networks can perform nonlinear transformations and investigate complex associations of image features with prognosis. Exploring shared information between post-contrast CT (with better visual enhancement) and pre-contrast CT (with few side effects and contraindications) is another challenge. In this work, a cross-phase adversarial domain adaptation (CPADA) framework is proposed to adapt a deep DFS prediction network (DDFS-Net) from arterial phase to pre-contrast phase. The DDFS-Net is designed for feature learning and trained by optimizing the average negative log function of Cox partial likelihood. The CPADA maps the feature space of pre-contrast phase (target) to arterial phase (source) in an adversarial manner by measuring Wasserstein distance. The proposed methods are evaluated on a dataset of 249 gastric cancer patients by concordance index, receiver operating characteristic curves, and Kaplan-Meier survival curves. The results demonstrate that our DDFS-Net outperforms linear survival analysis methods, and the CPADA works better than supervised learning and direct transfer schemes.Clinical Relevance-This work enables preoperative DFS prediction and risk stratification in gastric cancer. It is feasible and effective to infer a patient's risk of failure given a pre-contrast CT image by DDFS-Net adapted by CPADA.

Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

Background

Tumor mutational burden (TMB) is a significant predictor of immune checkpoint inhibitors (ICIs) efficacy. This study investigated the correlation between deep learning radiomic biomarker and TMB, including its predictive value for ICIs treatment response in patients with advanced non-small-cell lung cancer (NSCLC).

Methods

CT images from 327 patients with TMB data (TMB median=6.067 mutations per megabase (range: 0 to 42.151)) were retrospectively collected and randomly divided into a training (n=236), validation (n=26), and test cohort (n=65). We used 3D-densenet to estimate the target tumor area, which used 1020 deep learning features to distinguish High-TMB from Low-TMB patients and establish the TMB radiomic biomarker (TMBRB). The TMBRB was developed in the training cohort combined with validation cohort and evaluated in the test cohort. The predictive value of TMBRB was assessed in a cohort of 123 NSCLC patients who had received ICIs (survival median=462 days (range: 16 to 1128)).

Results

TMBRB discriminated between High-TMB and Low-TMB patients in the training cohort (area under the curve (AUC): 0.85, 95% CI: 0.84 to 0.87))and test cohort (AUC: 0.81, 95% CI: 0.77 to 0.85). In this study, the predictive value of TMBRB was better than that of a histological subtype (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.71, 95% CI: 0.66 to 0.76) or Radiomic model (AUC of training cohort: 0.75, 95% CI: 0.72 to 0.77; AUC of test cohort: 0.74, 95% CI: 0.69 to 0.79). When predicting immunotherapy efficacy, TMBRB divided patients into a high- and low-risk group with distinctly different overall survival (OS; HR: 0.54, 95% CI: 0.31 to 0.95; p=0.030) and progression-free survival (PFS; HR: 1.78, 95% CI: 1.07 to 2.95; p=0.023). Moreover, TMBRB had a better predictive ability when combined with the Eastern Cooperative Oncology Group performance status (OS: p=0.007; PFS: p=0.003). Visual analysis revealed that tumor microenvironment was important for predicting TMB.

Conclusion

By combining deep learning technology and CT images, we developed an individual non-invasive biomarker that could distinguish High-TMB from Low-TMB, which might inform decisions on the use of ICIs in patients with advanced NSCLC.