Policy gaps in addressing market failures and intervention misalignments in tuberculosis control: prospects for improvement in China, India, and Indonesia.

India, Indonesia, and China are the top three countries with the highest tuberculosis (TB) burden. To achieve the end TB target, we analyzed policy gaps in addressing market failures as well as misalignments between National TB Programs (NTP) and health insurance policies in TB control in three countries. In India and Indonesia, we found insufficient incentives to engage private practitioners or to motivate them to improve service quality. In addition, ineffective supervision of practice and limited coverage of drugs or diagnostics was present in all three countries. The major policy misalignment identified in all three countries is that while treatment guidelines encourage outpatient treatment for drug-sensitive patients, the national health insurance scheme covers primarily inpatient services. We therefore advocate for better alignment of TB control programs and broader universal health coverage (UHC) programs to leverage additional resources from national health insurance programs to improve the effective coverage of TB care.

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.

Melatonin serves as a novel treatment in bladder fibrosis through TGF-ß1/Smad and EMT.

BACKGROUND: Melatonin (MEL) is an indole amine molecule primarily produced in the pineal gland. Melatonin has been shown in numerous studies to have antifibrotic effects on the kidney, liver, and other organs. However, it is still unclear how melatonin works in bladder fibrosis. We explored how melatonin affects animals with bladder fibrosis and the underlying mechanisms. MATERIALS AND METHODS: MEL was used to treat human bladder smooth muscle cells (HBdSMCs) after they were stimulated with transforming growth factor-ß1 (TGF-ß1) in vitro. Proteomic analysis and bioinformatic analysis of the altered expression of these proteins were subsequently performed on HBdSMCs from the different processing methods. To construct an in vivo bladder fibrosis model, we injected protamine sulfate (PS) and lipopolysaccharide (LPS) twice a week into the rat bladder for six weeks. After two weeks of PS/LPS treatment, the mice in the treatment group were treated with MEL (20 mg/kg/d) for 4 weeks. Finally, we detected the expression of fibrosis markers from different perspectives. The TGF-ß1/Smad pathway and epithelial-mesenchymal transition (EMT) in cell and bladder tissues were also identified. Further proteomic analysis was also performed. RESULTS: In vitro, we found that TGF-ß1 treatment enhanced the expression of the fibrosis markers collagen III and α-SMA in HBdSMCs. E-cadherin expression decreased while the TGF-ß1/Smad pathway was activated. Vimentin and N-cadherin expression was also elevated at the same time. Similar findings were observed in the LPS group. After MEL treatment, the expression of collagen III and α-SMA decreased, the expression of E-cadherin increased, and the expression of vimentin and N-cadherin also decreased. According to our quantitative proteomics analysis, CCN1 and SQLE may be important proteins involved in the development of bladder fibrosis. MEL decreased the expression of these genes, leading to the relief of bladder fibrosis. Bioinformatics analysis revealed that the extracellular space structure related to metabolic pathways, actin filament binding, and stress fibers can serve as a pivotal focus in the management of fibrosis. CONCLUSION: Melatonin attenuates bladder fibrosis by blocking the TGF-ß1/Smad pathway and EMT. CCN1 appears to be a possible therapeutic target for bladder fibrosis.

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.

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.

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.

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.

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.

An injectable PC10ARGD/Cu2+/DOX hydrogel for combined chemodynamic and chemotherapy of tumors.

Nowadays, chemotherapy is a common clinical treatment for cancer, but it still faces many limitations and challenges. Therefore, the combination of chemotherapy and other treatments often enhances the effectiveness of treatments. Herein, an injectable hydrogel of PC10ARGD/Cu2+/DOX based on Cu2+, hydrophilic doxorubicin (DOX), and genetically engineered polypeptide PC10ARGD was prepared. First, Cu2+ was attached to the histidines in the PC10ARGD polypeptide by the coordination reaction to form PC10ARGD/Cu2+ hydrogel, then the PC10ARGD/Cu2+/DOX hydrogel was prepared by encapsulating the DOX into the PC10ARGD/Cu2+ hydrogel. The results of scanning electron microscopy showed that the PC10ARGD/Cu2+/DOX hydrogel displayed loose porous morphology. In vitro, reactive oxygen species production results showed that the PC10ARGD/Cu2+/DOX hydrogel could continuously produce ·OH in the presence of H2O2. In vitro MTT results showed that the PC10ARGD/Cu2+/DOX hydrogel had a good inhibitory effect on cell activity. Flow cytometry further confirmed the antitumor effect of the PC10ARGD/Cu2+/DOX hydrogel. In vivo experiment results showed that the tumor volume of mice treated with the PC10ARGD/Cu2+/DOX hydrogel was significantly inhibited compared with control groups, which was due to the combination of chemodynamic and chemotherapy. The results of body weight and blood analysis of mice showed that the PC10ARGD/Cu2+/DOX hydrogel possessed good biocompatibility.

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.

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.

A cyclic peptide-based PROTAC induces intracellular degradation of palmitoyltransferase and potently decreases PD-L1 expression in human cervical cancer cells.

Introduction: Our previous research has found that degradation of palmitoyltransferase in tumor cells using a linear peptide PROTAC leads to a significant decrease in PD-L1 expression in tumors. However, this degradation is not a sustained and efficient process. Therefore, we designed a cyclic peptide PROTAC to achieve this efficient anti-PD-L1 effect. Methods: We designed and synthesized an improvement in linear peptide PROTAC targeting palmitoyltransferase DHHC3, and used disulfide bonds to stabilize the continuous N- and C-termini of the peptides to maintain their structure. Cellular and molecular biology techniques were used to test the effect of this cyclic peptide on PD-L1. Results: In human cervical cancer cells, our cyclic peptide PROTAC can significantly downregulate palmitoyl transferase DHHC3 and PD-L1 expressions. This targeted degradation effect is enhanced with increasing doses and treatment duration, with a DC50 value much lower than that of linear peptides. Additionally, flow cytometry analysis of fluorescence intensity shows an increase in the amount of cyclic peptide entering the cell membrane with prolonged treatment time and higher concentrations. The Cellular Thermal Shift Assay (CETSA) method used in this study indicates effective binding between our novel cyclic peptide and DHHC3 protein, leading to a change in the thermal stability of the latter. The degradation of PD-L1 can be effectively blocked by the proteasome inhibitor MG132. Results from clone formation experiments illustrate that our cyclic peptide can enhance the proliferative inhibition effect of cisplatin on the C33A cell line. Furthermore, in the T cell-C33A co-culture system, cyclic peptides target the degradation of PD-L1, thereby blocking the interaction between PD-L1 and PD-1, and promoting the secretion of IFN-γ and TNF-α in the co-culture system supernatant. Conclusion: Our results demonstrate that a disulfide-bridged cyclic peptide PROTAC targeting palmitoyltransferase can provide a stable and improved anti-PD-L1 activity in human tumor cells.

Pain Prevalence and Management in a General Hospital Through Repeated Cross-Sectional Surveys in 2011 and 2021.

Background: There is great scope for improving the quality of pain management. Although pain prevalence has been investigated in several countries, few studies have comparatively assessed changes in pain prevalence and management over a span of multiple years. Aim: This work was aimed at determining the pain prevalence and evaluating the condition of pain management in a Chinese general hospital in 2021 and comparing them with corresponding data from 10 years ago. Methods: Repeated single-center cross-sectional studies were initiated on June 14th, 2011, and September 2nd, 2021, in the same tertiary grade A Chinese general hospital. The same structured questionnaire was used to collect inpatient data on pain intensity and classification and pain management outcomes. We performed statistical analyses to compare categorical variables to assess changes over time. Results: The sample sizes for the investigations in 2011 and 2021 were 2323 and 4454, respectively. In 2021, 24.34% of patients experienced pain; this percentage was significantly lower than that in 2011. Meanwhile, the prevalence of moderate and severe pain decreased from 14.73% in 2011 to 4.98% in 2021. The other six indicators of pain management outcomes also improved significantly. The percentages of patients using painkillers, opioid analgesics, and multiple analgesics increased from 44.61 to 51.38%, 24.01% to 44.61%, and 6.82% to 14.11%, respectively. Furthermore, the percentages of patients who received pain information and who actively reported pain increased from 27.56% to 96.5% and from 85.54% to 98.71%, respectively. The percentage of patients qualified to accurately use the Numerical Rating Scale increased from 10.5% to 79.98%. Conclusion: The quality and outcomes of pain management improved greatly after the establishment and implementation of the pain management system. Nonetheless, pain of different intensities is common after major surgeries, and it is recommended that hospitals popularize and implement perioperative multimodal analgesia strategies to reduce the incidence of postoperative pain.

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.

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.

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.

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.

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.