Blood brain barrier-targeted lipid nanoparticles improved the neuroprotection of Ferrostatin-1 against cerebral ischemic damage in an experimental stroke model.

Cerebral ischemic stroke is a serious disease with high mortality and disability rates. However, few neuroprotective drugs have been used for ischemic stroke in the clinic. Two main reasons may be responsible for this failure: difficulty in penetrating the blood-brain barrier (BBB) and easily inactivated in the blood circulation. Ferroptosis, a lipid oxidation-related cell death, plays significant roles in cerebral ischemia-reperfusion injury. We utilized RVG29, a peptide derived from Rabies virus glycoprotein, to obtain BBB-targeted lipid nanoparticles (T-LNPs) in order to investigate whether T-LNPs improved the neuroprotective effects of Ferrostatin-1 (Fer1, an inhibitor of ferroptosis) against cerebral ischemic damage. T-LNPs significantly increased BBB penetration following oxygen/glucose deprivation exposure in an in vitro BBB model and enhanced the fluorescence distribution in brain tissues at 6 h post-administration in a cerebral ischemic murine model. Moreover, T-LNPs encapsulated Fer1 (T-LNPs-Fer1) significantly enhanced the inhibitory effects of Fer1 on ferroptosis by maintaining the homeostasis of NADPH oxidase 4 (NOX4) and glutathione peroxidase 4 (GPX4) signals in neuronal cells after cerebral ischemia. T-LNPs-Fer1 significantly suppressed oxidative stress [heme oxygenase-1 expression and malondialdehyde (the product of lipid ROS reaction)] in neurons and alleviated ischemia-induced neuronal cell death, compared to Fer1 alone without encapsulation. Furthermore, T-LNPs-Fer1 significantly reduced cerebral infarction and improved behavior functions compared to Fer1-treated cerebral ischemic mice after 45-min ischemia/24-h reperfusion. These findings showed that the T-LNPs helped Fer1 penetrate the BBB and improved the neuroprotection of Fer1 against cerebral ischemic damage in experimental stroke, providing a feasible translational strategy for the development of clinical drugs for the treatment of ischemic stroke.

Neem Leaf Extract Exhibits Anti-Aging and Antioxidant Effects from Yeast to Human Cells.

Neem leaves have long been used in traditional medicine for promoting longevity. However, the precise mechanisms underlying their anti-aging effects remain elusive. In this study, we investigated the impact of neem leaf extract (NLE) extracted from a 50% ethanol solution on the chronological lifespan of Saccharomyces cerevisiae, revealing an extension in lifespan, heightened oxidative stress resistance, and a reduction in reactive oxygen species. To discern the active compounds in NLE, LC/MS and the GNPS platform were employed. The majority of identified active compounds were found to be flavonoids. Subsequently, compound-target pharmacological networks were constructed using the STP and STITCH platforms for both S. cerevisiae and Homo sapiens. GOMF and KEGG enrichment analyses of the predicted targets revealed that "oxidoreductase activity" was among the top enriched terms in both yeast and human cells. These suggested a potential regulation of oxidative stress response (OSR) by NLE. RNA-seq analysis of NLE-treated yeast corroborated the anti-oxidative effect, with "oxidoreductase activity" and "oxidation-reduction process" ranking high in enriched GO terms. Notably, CTT1, encoding catalase, emerged as the most significantly up-regulated gene within the "oxidoreductase activity" cluster. In a ctt1 null mutant, the enhanced oxidative stress resistance and extended lifespan induced by NLE were nullified. For human cells, NLE pretreatment demonstrated a decrease in reactive oxygen species levels and senescence-associated ß-galactosidase activity in HeLa cells, indicative of anti-aging and anti-oxidative effects. This study unveils the anti-aging and anti-oxidative properties of NLE while delving into their mechanisms, providing novel insights for pharmacological interventions in aging using phytochemicals.

Sotagliflozin versus dapagliflozin to improve outcome of patients with diabetes and worsening heart failure: a cost per outcome analysis.

Background and aim: Dapagliflozin inhibits the sodium-glucose cotransporter protein 2 (SGLT-2), while sotagliflozin, belonging to a new class of dual-acting SGLT-1/SGLT-2 inhibitors, has garnered considerable attention due to its efficacy and safety. Both Dapagliflozin and sotagliflozin play a significant role in treating worsening heart failure in diabetes/nondiabetes patients with heart failure. Therefore, this article was to analyze and compare the cost per outcome of both drugs in preventing one event in patients diagnosed with diabetes-related heart failure. Method: The Cost Needed to Treat (CNT) was employed to calculate the cost of preventing one event, and the Number Needed to Treat (NNT) represents the anticipated number of patients requiring the intervention treatment to prevent a single adverse event, or the anticipated number of patients needing multiple treatments to achieve a beneficial outcome. The efficacy and safety data were obtained from the results of two published clinical trials, DAPA-HF and SOLOIST-WHF. Due to the temporal difference in the drugs' releases, we temporarily analyzed the price of dapagliflozin to calculate the price of sotagliflozin within the same timeframe. The secondary analyses aimed to assess the stability of the CNT study and minimize differences between the results of the RCT control and trial groups, employing one-way sensitivity analyses. Result: The final results revealed an annualized Number Needed to Treat (aNNT) of 4 (95% CI 3-7) for preventing one event with sotagliflozin, as opposed to 23 (95% CI 16-55) for dapagliflozin. We calculated dapagliflozin's cost per prevented event (CNT) to be $109,043 (95% CI $75,856-$260,755). The price of sotagliflozin was set below $27,260, providing a favorable advantage. Sensitivity analysis suggests that sotagliflozin may hold a cost advantage. Conclusion: In this study, sotagliflozin was observed to exhibit a price advantage over dapagliflozin in preventing one events, cardiovascular mortality, or all-cause mortality in patients with diabetes.

Gentiopicrin-Loaded Chitosan Nanoparticles as a Topical Agent for the Treatment of Psoriasis.

Psoriasis, a chronic inflammatory skin disease induced by various factors, including genetic factors, immune factors, environmental factors, and psychological factors, is characterized by thickening of the epidermis, excessive proliferation of keratinocytes, abnormal differentiation, and an excessive inflammatory response. Traditional treatments for psoriasis still face challenges because of limited curative effects, notable side effects, and a tendency for recurrence. In contrast, topical therapy provides a favorable option for psoriasis treatment because of its noninvasive and self-administered method. In this study, gentiopicrin (Gen) is encapsulated in the liposomes to form a nanodrug, and then chitosan is covered on the nanodrug to assemble the nanodrug delivery system (CS@Gen), which is used as a topical agent for treating psoriasis. Then M5 (a mixture of five pro-inflammatory cytokines, i.e., IL-17A, IL-22, IL-1α, oncostatin M, and TNF-α)-induced HacaT cells and imiquimod-induced psoriasis mouse models are established, whose results show that CS@Gen induces apoptosis and inhibits the proliferation and cell migration of psoriasis keratinocytes. Additionally, the application of CS@Gen cream can significantly reduce epidermal thickness, diminish skin scaling, and improve other related mechanisms in mice affected by psoriasis. Meanwhile, the prepared CS@Gen can significantly reduce the expression levels of IL-17a, Cxcl2, S100a, Mki67, and other related inflammatory factors, resulting in indirectly inhibiting the inflammation of keratinocytes. In summary, the present study provides an ideal loading for an anti-inflammatory and immunomodulatory drug delivery system for the treatment of psoriasis.

Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors.

OBJECTIVE: The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD: A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS: In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS: The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Radiomics analysis of contrast-enhanced computerized tomography for differentiation of gastric schwannomas from gastric gastrointestinal stromal tumors.

PURPOSE: To assess the performance of radiomics-based analysis of contrast-enhanced computerized tomography (CE-CT) images for distinguishing GS from gastric GIST. METHODS: Forty-nine patients with GS and two hundred fifty-three with GIST were enrolled in this retrospective study. CT features were evaluated by two associate chief radiologists. Radiomics features were extracted from portal venous phase images using Pyradiomics software. A non-radiomics dataset (combination of clinical characteristics and radiologist-determined CT features) and a radiomics dataset were used to build stepwise logistic regression and least absolute shrinkage and selection operator (LASSO) logistic regression models, respectively. Model performance was evaluated according to sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curve, and Delong's test was applied to compare the area under the curve (AUC) between different models. RESULTS: A total of 1223 radiomics features were extracted from portal venous phase images. After reducing dimensions by calculating Pearson correlation coefficients (PCCs), 20 radiomics features, 20 clinical characteristics + CT features were used to build the models, respectively. The AUC values for the models using radiomics features and those using clinical features were more than 0.900 for both the training and validation groups. There were no significant differences in predictive performance between the radiomic and clinical data models according to Delong's test. CONCLUSION: A radiomics-based model applied to CE-CT images showed comparable predictive performance to senior physicians in the differentiation of GS from GIST.

Aurora B facilitates cholangiocarcinoma progression by stabilizing c-Myc.

BACKGROUND: Cholangiocarcinoma (CCA), a malignancy that arises from biliary epithelial cells, has a dismal prognosis, and few targeted therapies are available. Aurora B, a key mitotic regulator, has been reported to be involved in the progression of various tumors, yet its role in CCA is still unclarified. METHODS: Human CCA tissues and murine spontaneous CCA models were used to assess Aurora B expression in CCA. A loss-of-function model was constructed in CCA cells to determine the role of Aurora B in CCA progression. Subcutaneous and liver orthotopic xenograft models were used to assess the therapeutic potential of Aurora B inhibitors in CCA. RESULTS: In murine spontaneous CCA models, Aurora B was significantly upregulated. Elevated Aurora B expression was also observed in 62.3% of human specimens in our validation cohort (143 CCA specimens), and high Aurora B expression was positively correlated with pathological parameters of tumors and poor survival. Knockdown of Aurora B by siRNA and heteroduplex oligonucleotide (HDO) or an Aurora B kinase inhibitor (AZD1152) significantly suppressed CCA progression via G2/M arrest induction. An interaction between Aurora B and c-Myc was found in CCA cells. Targeting Aurora B significantly reduced this interaction and accelerated the proteasomal degradation of c-Myc, suggesting that Aurora B promoted the malignant properties of CCA by stabilizing c-Myc. Furthermore, sequential application of AZD1152 or Aurora B HDO drastically improved the efficacy of gemcitabine in CCA. CONCLUSIONS: Aurora B plays an essential role in CCA progression by modulating c-Myc stability and represents a new target for treatment and chemosensitization in CCA.

Targeting PKM2 improves the gemcitabine sensitivity of intrahepatic cholangiocarcinoma cells via inhibiting ß-catenin signaling pathway.

Gemcitabine is considered the standard first-line chemotherapeutic agent for patients with intrahepatic cholangiocarcinoma (ICC). However, its therapeutic efficacy is hampered by the development of chemoresistance. Pyruvate kinase M2 (PKM2), a crucial mediator of the final step in glycolysis, has been implicated in the origination and advancement of diverse malignancies. Its expression is increased in many tumor types and this may correlate with increased drug sensitivity. However, the specific effect of PKM2 on the gemcitabine sensitivity in ICC remains to be elucidated. In this research, we aimed to elucidate the role and functional significance of PKM2 in ICC, as well as the heightened susceptibility of ICC cells to gemcitabine by targeting PKM2 and the underlying molecular mechanisms. Immunohistochemical and immunofluorescence analyses revealed elevated expression of PKM2 in both tumor cells and macrophages in human ICC tissues. Reducing PKM2 levels significantly restrained the proliferation of tumor cells, impeded cell cycle advance, induced programmed cell death, and suppressed metastasis. In addition, knockdown or pharmacological inhibition of PKM2 could enhance the response of ICC cells to gemcitabine in vitro. Interestingly, conditioned medium co-culture system suggested that conditioned medium from M2 macrophages increased gemcitabine sensitivity of ICC cells. However, silencing PKM2 or pharmacological inhibition of PKM2 in M2 macrophages did not ameliorate the gemcitabine resistance mediated by M2 macrophages derived conditioned medium. Mechanistically, downregulation of PKM2 repressed the expression of ß-catenin and its downstream transcriptional targets, thereby hindering the propagation of ß-catenin signaling cascade. Finally, the results of the subcutaneous xenograft experiment in nude mice provided compelling evidence of a synergistic interaction between PKM2-IN-1 and gemcitabine in vivo. In summary, we reported that PKM2 may function as an advantageous target for increasing the sensitivity of ICC to gemcitabine treatment. Targeting PKM2 improves the gemcitabine sensitivity of ICC cells via inhibiting ß-catenin signaling pathway.

Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature.

PURPOSE: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance. METHODS: A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model. RESULTS: The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01). CONCLUSION: The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application.

Tailored multi-organ segmentation with model adaptation and ensemble.

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited and hence poses a challenge in obtaining sufficient training data for deep learning-based methods. In this paper, we aim to address this issue by combining off-the-shelf single-organ segmentation models to develop a multi-organ segmentation model on the target dataset, which helps get rid of the dependence on annotated data for multi-organ segmentation. To this end, we propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage. The first stage enhances the generalization of each off-the-shelf segmentation model on the target domain, while the second stage distills and integrates knowledge from multiple adapted single-organ segmentation models. Extensive experiments on four abdomen datasets demonstrate that our proposed method can effectively leverage off-the-shelf single-organ segmentation models to obtain a tailored model for multi-organ segmentation with high accuracy.

Citrus Honey Ameliorates Liver Disease and Restores Gut Microbiota in Alcohol-Feeding Mice.

Citrus honey (CH) is rich in nutrients that have a wide variety of biological functions, such as antibacterial, anti-inflammatory, and antioxidant activities, and which demonstrate therapeutic properties, such as anti-cancer and wound-healing abilities. However, the effects of CH on alcohol-related liver disease (ALD) and the intestinal microbiota remain unknown. This study aimed to determine the alleviating effects of CH on ALD and its regulatory effects on the gut microbiota in mice. In total, 26 metabolites were identified and quantified in CH, and the results suggested that the primary metabolites were abscisic acid, 3,4-dimethoxycinnamic acid, rutin, and two markers of CH, hesperetin and hesperidin. CH lowered the levels of aspartate aminotransferase, glutamate aminotransferase, and alcohol-induced hepatic edema. CH could promote the proliferation of Bacteroidetes while reducing the abundance of Firmicutes. Additionally, CH also showed some inhibitory effects on the growth of Campylobacterota and Turicibacter. CH enhanced the secretion of short-chain fatty acids (SCFAs), such as acetic acid, propionic acid, butyric acid, and valeric acid. Given its alleviating functions in liver tissue damage and its regulatory effects on the gut microbiota and SCFAs, CH could be a promising candidate for the therapeutic treatment of ALD.

Hydrogen sulfide protects retinal pigment epithelium cells against ferroptosis through the AMPK- and p62-dependent non-canonical NRF2-KEAP1 pathway.

Oxidative stress-induced ferroptosis of retinal pigment epithelium (RPE) cells contributes to retinal degenerative diseases. The antioxidant molecule hydrogen sulfide (H2S) regulates oxidative stress response, but its effect on the ferroptosis of RPE cells is unclear. In this study, sodium hydrosulfide (NaHS) was used as an exogenous H2S donor to intervene tert-butyl hydroperoxide (t-BHP)-induced ferroptosis of APRE-19 cells. We found that NaHS pretreatment attenuates t-BHP-induced oxidative stress and ferroptosis. Analysis of mRNA-sequencing coupled with FerrDb database identified nuclear factor erythroid-2-related factor 2 (NRF2) as a primary target for the cytoprotective role of H2S. NRF2 inhibitor ML385 reverses the effects of H2S on ferroptosis. Biochemical analysis revealed that H2S stabilizes NRF2. H2S decreases the interaction between NRF2 and KEAP1, but enhances the interaction between KEAP1 and p62. These results suggest that H2S activates the non-canonical NRF2-KEAP1 pathway. Further study demonstrated that H2S stimulates AMPK to interact and phosphorylate p62. Additionally, inhibiting AMPK or knocking down p62 blocks the effects of H2S. We speculate that targeting the non-canonical NRF2-KEAP1 pathway by H2S-based drug may benefit the treatment of retinal degenerative diseases.

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis. METHODS: A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots. RESULTS: At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists' measurements, the model showed similar performance to the radiologists. CONCLUSIONS: The developed model can automatically measure SIRM on flexion-neutral-extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.

Inhibitory Effects of Myriocin on Non-Enzymatic Glycation of Bovine Serum Albumin.

Advanced glycation end products (AGEs) are the compounds produced by non-enzymatic glycation of proteins, which are involved in diabetic-related complications. To investigate the potential anti-glycation activity of Myriocin (Myr), a fungal metabolite of Cordyceps, the effect of Myr on the formation of AGEs resulted from the glycation of bovine serum albumin (BSA) and the interaction between Myr and BSA were studied by multiple spectroscopic techniques and computational simulations. We found that Myr inhibited the formation of AGEs at the end stage of glycation reaction and exhibited strong anti-fibrillation activity. Spectroscopic analysis revealed that Myr quenched the fluorescence of BSA in a static process, with the possible formation of a complex (approximate molar ratio of 1:1). The binding between BSA and Myr mainly depended on van der Waals interaction, hydrophobic interactions and hydrogen bond. The synchronous fluorescence and UV-visible (UV-vis) spectra results indicated that the conformation of BSA altered in the presence of Myr. The fluorescent probe displacement experiments and molecular docking suggested that Myr primarily bound to binding site 1 (subdomain IIA) of BSA. These findings demonstrate that Myr is a potential anti-glycation agent and provide a theoretical basis for the further functional research of Myr in the prevention and treatment of AGEs-related diseases.

Effects of High-Frequency Chest Wall Oscillation Expectoration System on Pulmonary Rehabilitation and Cortisol Function in Patients with Severe AECOPD.

Objective: To investigate the effect of high-frequency chest wall oscillatory expectoration system (HFCWO) on pulmonary rehabilitation and cortisol function in patients with severe acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods: The 65 severe AECOPD patients admitted to our hospital from January 2019 to May 2020 were divided into group A with 33 cases and group B with 32 cases by random number table method. After 14 days of intervention, the improvement time of clinical symptoms in the two groups was recorded, and blood gas, lung function, inflammatory, and cortisol function-related indicators were evaluated before and after treatment. Results: The remission time of expectoration, pulmonary signs, and hospital stay in group A were significantly shorter than those in group B (P < 0.05). Compared with before treatment, blood oxygen partial pressure (PaO2), forced vital capacity (FVC), forced expiratory volume at 1 s (EFV1), and EFV1/FVC increased significantly; blood carbon dioxide partial pressure (PaCO2), C-reactive protein (CRP), interleukin-6 (IL-6), white blood cell count (WBC), plasma cortisol (COR), and adrenocorticotropic hormone (ACTH) levels were significantly decreased, and the above indicators in group A increased or decreased more significantly than those in group B (P < 0.05); there was no significant difference in tolerance and adverse reactions between the two groups (P > 0.05). Conclusion: HFCWO has good pulmonary rehabilitation effect in the treatment of severe AECOPD and can significantly improve the blood gas indexes, inflammation, and cortisol function of patients, which is safe and feasible.

Radiographic measurement of the posterior tibial slope in normal Chinese adults: a retrospective cohort study.

BACKGROUND: Measurement of the posterior tibial slope (PTS) angle has important applications in total knee replacement surgery, high tibial osteotomy, and anterior cruciate ligament reconstruction. This study aimed to determine the mean PTS of knee joints in healthy Chinese adults, and provide data to guide knee surgery in China. METHODS: A retrospective analysis of 1257 (n = 1233, 50.4% male) plain X-ray films of participants aged 25-59 years was performed. The picture archiving and communication system was used for PTS measurement. The PTS was defined as the angle between the vertical line of the tangent of the anterior tibial cortex of the proximal tibia, and the tangent line of the tibial cortex. Two imaging physicians conducted the PTS measurements independently, and both the inter- and intraclass correlation coefficients (ICCs) were calculated. RESULTS: The mean PTS value was 7.68 ± 3.84° (range: 0-21°). The left PTS was significantly smaller in males than in females (7.22 ± 3.89 vs 8.05 ± 3.60; P = 0.005). Additionally, the PTS in participants aged 25-29 years was significantly larger than that in the other age groups (Left side: 8.64 ± 3.73 vs 6.92 ± 3.42, 7.42 ± 3.75, 7.53 ± 3.98; P <  0.001 and Right side: 8.68 ± 3.84 vs 7.48 ± 4.21, 7.13 ± 3.64, 7.66 ± 3.80; P = 0.004). There were no significant differences in PTS between the left and right sides. Two-way analysis of variance suggested that the differences in PTS between age groups were not affected by sex. The interobserver ICC was 0.91 (95% confidence interval [CI]: 0.85-0.94), and the intraobserver ICC was 0.90 (95% CI: 0.82-0.94). CONCLUSIONS: This study demonstrated that there were significant differences in PTS based on sex and age, highlighting the need to provide individualized treatment for knee surgery. It provided valuable information regarding the normal PTS values in Chinese adults and presented regionalised data to guide knee surgery.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

OBJECTIVES: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. METHODS: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). RESULTS: The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). CONCLUSIONS: Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. KEY POINTS: • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

CT-based radiomic nomogram for predicting the severity of patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with severe COVID-19. METHODS: There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts, respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. RESULTS: The radiomics signature consisting of four selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. CONCLUSION: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.

Changes in serum inflammatory factors in acute gouty arthritis patients treated using ultrashort wave combined with loxoprofen sodium.

OBJECTIVES: To study the effect of ultrashort wave combined with loxoprofen sodium on serum inflammatory factors in patients with acute gouty arthritis. METHODS: Records of patients with acute gouty arthritis who were treated in The Fourth Hospital of Changsha from May 2018 to September 2020, were reviewed. Of them, 77 cases were selected and divided into two groups based on the received treatment. The control group (n=39) was treated with loxoprofen sodium, and the treatment group (n=38) was treated with an ultrashort wave combined with loxoprofen sodium, for 10 continuous days. The clinical efficacy of the treatment in two groups was analyzed. RESULTS: After treatment, the quality of life of patients in both groups was improved (P < 0.05), but there was no significant difference in the degree of improvement between the two groups (P > 0.05). After treatment, the VAS score of the treatment group was lower than that of the control group (P < 0.05), the improvement of symptoms and signs of the treatment group was better than that of the control group (P < 0.05). Serum CRP and ESR levels in the treatment group were lower than those in the control group (P < 0.05), and the serum IL-1 ß, IL-8, TNF-a and MMP-3 levels of the treatment group were lower than those of the control group (P < 0.05). The total effective rate of the treatment group (94.87%) was higher than that of the control group (87.18%), the difference was statistically significant (P < 0.05). No adverse reactions occurred in all patients during the treatment. CONCLUSION: An ultrashort wave combined with loxoprofen sodium in the treatment of acute gouty arthritis can reduce the inflammatory reaction, improve the degree of joint pain and swelling, improve the curative effect, and do not increase the adverse reactions. The results may be related to the regulation of IL-1 ß, IL-8, TNF-a and MMP-3.

Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors.

We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2020 were categorized into a training data set (82 patients with high risk, 80 patients with low risk) and a validation data set (35 patients with high risk, 34 patients with low risk) with a ratio of 7:3. Four diagnostic models were constructed by assessing 20 clinical characteristics and 18 radiomic features that were extracted from a lesion mask based on triple-phase CT images. The receiver operating characteristic (ROC) curves were applied to calculate the diagnostic performance of these models, and ROC curves of these models were compared using Delong test in different data sets. The results of ROC analyses showed that areas under ROC curves (AUC) of model 4 [Clinic + CT value of unenhanced (CTU) + CT value of arterial phase (CTA) + value of venous phase (CTV)], model 1 (Clinic + CTU), model 2 (Clinic + CTA), and model 3 (Clinic + CTV) were 0.925, 0.894, 0.909, and 0.914 in the training set and 0.897, 0.866, 0,892, and 0.892 in the validation set, respectively. Model 4, model 1, model 2, and model 3 yielded an accuracy of 88.3%, 85.8%, 86.4%, and 84.6%, a sensitivity of 85.4%, 84.2%, 76.8%, and 78.0%, and a specificity of 91.2%, 87.5%, 96.2%, and 91.2% in the training set and an accuracy of 88.4%, 84.1%, 82.6%, and 82.6%, a sensitivity of 88.6%, 77.1%, 74.3%, and 85.7%, and a specificity of 88.2%, 91.2%, 91.2%, and 79.4% in the validation set, respectively. There was a significant difference between model 4 and model 1 in discriminating the risk rating in gastrointestinal stromal tumors in the training data set (Delong test, p < 0.05). The radiomic models based on clinical features and triple-phase CT images manifested excellent accuracy for the discrimination of risk rating of GISTs.