Ultrasound-based deep learning radiomics nomogram for differentiating mass mastitis from invasive breast cancer.

BACKGROUND: The purpose of this study is to develop and validate the potential value of the deep learning radiomics nomogram (DLRN) based on ultrasound to differentiate mass mastitis (MM) and invasive breast cancer (IBC). METHODS: 50 cases of MM and 180 cases of IBC with ultrasound Breast Imaging Reporting and Data System 4 category were recruited (training cohort, n = 161, validation cohort, n = 69). Based on PyRadiomics and ResNet50 extractors, radiomics and deep learning features were extracted, respectively. Based on supervised machine learning methods such as logistic regression, random forest, and support vector machine, as well as unsupervised machine learning methods using K-means clustering analysis, the differences in features between MM and IBC were analyzed to develop DLRN. The performance of DLRN had been evaluated by receiver operating characteristic curve, calibration, and clinical practicality. RESULTS: Supervised machine learning results showed that compared with radiomics models, especially random forest models, deep learning models were better at recognizing MM and IBC. The area under the curve (AUC) of the validation cohort was 0.84, the accuracy was 0.83, the sensitivity was 0.73, and the specificity was 0.83. Compared to radiomics or deep learning models, DLRN even further improved discrimination ability (AUC of 0.90 and 0.90, accuracy of 0.83 and 0.88 for training and validation cohorts), which had better clinical benefits and good calibratability. In addition, the information heterogeneity of deep learning features in MM and IBC was validated again through unsupervised machine learning clustering analysis, indicating that MM had a unique features phenotype. CONCLUSION: The DLRN developed based on radiomics and deep learning features of ultrasound images has potential clinical value in effectively distinguishing between MM and IBC. DLRN breaks through visual limitations and quantifies more image information related to MM based on computers, further utilizing machine learning to effectively utilize this information for clinical decision-making. As DLRN becomes an autonomous screening system, it will improve the recognition rate of MM in grassroots hospitals and reduce the possibility of incorrect treatment and overtreatment.

CT Anatomical Analysis of C4 Pedicle and Lateral Mass in Children Aged 0-14 in Southern China.

OBJECTIVE: The C4 is the transition point between the upper and lower cervical vertebrae and plays a pivotal role in the middle of the cervical spine. Currently, there are limited reports on large-scale sample studies regarding C4 anatomy in children, and a scarcity of experience exists in pediatric cervical spine surgery. The current study addresses the dearth of anatomical measurements of the C4 vertebral arch and lateral mass in a substantial sample of children. This study aims to measure the imaging anatomy of the C4 vertebral arch and lateral mass in children under 14 years of age across various age groups, investigate the growth and development of these structures. METHODS: We measured 12 indicators, including the size (D1, D2, D3, D4, D5, D6, D7, and D8) and angle (A, C, D, and E) of the C4 vertebral arch and lateral mass, in 513 children who underwent cervical CT examinations at our hospital. We employed the aggregate function for statistical analysis, conducted t-tests for difference statistics, and utilized the least squares method for regression analysis. RESULTS: Overall, as age increased, there was a gradual increase in the size of the vertebral arch and lateral mass. Additionally, the medial inclination angle of the vertebral arch decreased, and the lateral mass flattened gradually. The rate of change decreased gradually with age. The mean value of D1 increased from 2.31 mm to 3.88 mm, of D2 from 16.75 mm to 29.2 mm, of D3 from 2.21 mm to 4.92 mm, and of D4 from 7.34 mm to 11.84 mm. Meanwhile, the mean value of D5 increased from 5.2 mm to 9.71 mm, of D6 from 10.19 mm to 16.16 mm, of D7 from 2.53 mm to 5.67 mm, and of D8 from 6.11 mm to 11.45 mm. Angle A ranged from 49.12° to 54.97°, angle C from 15.28° to 19.83°, angle D from 39.91° to 53.7°, and angle E from 18.63° to 28.08°. CONCLUSION: Prior to cervical spine surgery in children, meticulous CT imaging anatomical measurements is essential. The imaging data serves as a reference for posterior C4 internal fixation, aids in designing posterior cervical screws for pediatric patients, and offer morphological anatomical references for posterior cervical spine surgery and screw design in pediatric patients.

Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.

BACKGROUND: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare. METHODS: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility. RESULTS: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. The C-index was 0.788 (95% confidence interval: 0.73214-0.84386). On the decision curve analysis (DCA), the threshold probability of the nomogram ranged from 1 to 99% (training cohort) and 1 to 75% (validation cohort). CONCLUSION: We successfully developed an ML model for predicting AHD in patients undergoing cervical spine surgery, showcasing its potential to support clinicians in AHD identification and enhance perioperative treatment strategies.

Exploring the causal relationship between inflammatory cytokines and inflammatory arthritis: A Mendelian randomization study.

OBJECTIVES: Previous studies have reported an association between inflammatory cytokines and inflammatory arthritis, including Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriatic arthritis (PsA). This study aims to explore the causal relationship between inflammatory cytokines and AS, RA, and PsA using Mendelian randomization (MR). METHODS: We conducted a bidirectional two-sample MR analysis using genetic summary data from a publicly available genome-wide association study (GWAS) that included 41 genetic variations of inflammatory cytokines, as well as genetic variant data for AS, RA, and PsA from the FinnGen consortium. The main analysis method used was Inverse variance weighted (IVW) to investigate the causal relationship between exposure and outcome. Additionally, other methods such as MR Egger, weighted median (WM), simple mode, and weighted mode were employed to strengthen the final results. Sensitivity analysis was also performed to ensure the reliability of the findings. RESULTS: The results showed that macrophage colony-stimulating factor (MCSF) was associated with an increased risk of AS (OR = 1.163, 95 % CI = 1.016-1.33, p = 0.028). Conversely, high levels of TRAIL and beta nerve growth factor (ß-NGF) were associated with a decreased risk of AS (OR = 0.892, 95 % CI = 0.81-0.982, p = 0.002; OR = 0.829, 95 % CI = 0.696-0.988, p = 0.036). Four inflammatory cytokines were found to be associated with an increased risk of PsA: vascular endothelial growth factor (VEGF) (OR = 1.161, 95 % CI = 1.057-1.275, p = 0.002); Interleukin 12p70 (IL12p70) (OR = 1.189, 95 % CI = 1.049-1.346, p = 0.007); IL10 (OR = 1.216, 95 % CI = 1.024-1.444, p = 0.026); IL13 (OR = 1.159, 95 % CI = 1.05-1.28, p = 0.004). Interleukin 1 receptor antagonist (IL-1rα) was associated with an increased risk of seropositive RA (OR = 1.181, 95 % CI = 1.044-1.336, p = 0.008). Similarly, genetic susceptibility to inflammatory arthritis was found to be causally associated with multiple inflammatory cytokines. Lastly, the sensitivity analysis supported the robustness of these findings. CONCLUSIONS: This study provides additional insights into the relationship between inflammatory cytokines and inflammatory arthritis, and may offer new clues for the etiology, diagnosis, and treatment of inflammatory arthritis.

Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery.

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Comparison of laparoscope-assisted single-needle laparoscopic percutaneous extraperitoneal closure versus open repair for pediatric inguinal hernia.

BACKGROUND: Laparoscopic-assisted repairs for pediatric inguinal hernia have gained gradual acceptance over the past decade. However, consensus about the optimal management is still lacking. The aim of this study is to compare outcomes of a modified laparoscope-assisted single-needle laparoscopic percutaneous extraperitoneal closure (LPEC) versus open repair of pediatric hernias/hydrocele in a single institution. MATERIALS AND METHODS: We retrospectively reviewed the medical data of children who underwent laparoscope-assisted single-needle LPEC and open repair (OR) for inguinal hernia from 2014 to 2019. Data collection included demographics, laterality of hernia, surgical time and time to follow-up. We also reviewed and analyzed the evidence of recurrence, the incidence of metachronous contralateral inguinal hernia (MCIH), and other complications. RESULTS: In our cohort, 961 patients in the OR group and 1098 patients in the LPEC group were analyzed retrospectively. Mean operative time was significantly shorter in the LPEC group (22.3 ± 3.5 min) than in the OR group (27.8 ± 5.9 min) for bilateral hernia repair (p < 0.001). Postoperative recurrence was 1.3% (13/1035) in the OR group and 0.5% (6/1182) in the LPEC group (p = 0.056). Iatrogenic cryptorchidism occurred statistically more frequently in the OR group than in the LPEC group (0.4% vs. 0%, p = 0.013). In addition, the incidence of MCIH was 3.7% (33/887) in the OR group and 0.3% (3/1014) in the LPEC group (p < 0.01). CONCLUSION: Comparing to open technique, laparoscope-assisted single-needle LPEC provides a simple and effective option for pediatric inguinal hernia/hydrocele repair with excellent outcomes, a low incidence of recurrence, and reduced MCIH.

Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.

Background: Anterior cervical decompression and fusion can effectively treat cervical spondylotic myelopathy (CSM). Accurately classifying patients with CSM who have undergone anterior cervical decompression and fusion is the premise of precision medicine. In this study, we used machine learning algorithms to classify patients and compare the postoperative efficacy of each classification. Methods: A total of 616 patients with cervical spondylotic myelopathy who underwent anterior cervical decompression and fusion were enrolled. Unsupervised machine learning algorithms (UMLAs) were used to cluster subjects according to similar clinical characteristics. Then, the results of clustering were visualized. The surgical outcomes were used to verify the accuracy of machine learning clustering. Results: We identified two clusters in these patients who had significantly different baseline clinical characteristics, preoperative complications, the severity of neurological symptoms, and the range of decompression required for surgery. UMLA divided the CSM patients into two clusters according to the severity of their illness. The repose to surgical treatment between the clusters was significantly different. Conclusions: Our results showed that UMLA could be used to rationally classify a heterogeneous cohort of CSM patients effectively, and thus, it might be used as the basis for a data-driven platform for identifying the cluster of patients who can respond to a particular treatment method.

Curcumin antagonizes inflammation and autophagy induced by arsenic trioxide through immune protection in duck spleen.

Arsenic is a toxic heavy metal widely found in the natural environment and has adverse effects on the health of waterfowl and human. Curcumin (CUR), a natural pigment of the golden spice turmeric, exhibits excellent anti-tumor, anti-inflammatory and anti-oxidant activities. But the effects of CUR on duck spleen exposed to arsenic remain largely unknown. In this study, 75 ducks were divided randomly into Control, L-ATO, M-ATO, H-ATO and CUR + H-ATO groups to systematically analyze the underlying role of CUR. The results showed that arsenic trioxide (ATO) led to growth retardation of ducks, hyaline degeneration and sparse cell arrangement on their spleen. And in the ATO-exposed ducks, the levels of immunoglobulins (Ig; IgA, IgG, IgM) in the serum and the expression of autophagy-related genes (Atg5, P62, LC3I, LC3II, LC3II/I, Beclin-1) were significantly upregulated compared with the control ducks. Moreover, ATO also activated NF-κB signal pathway and upregulated the expression of pro-inflammatory cytokines (TNF-α, IFN-γ, IL-1ß, IL-2, IL-18). Meanwhile, application of CUR alleviated the ATO toxicity with the release of growth inhibition, and the reduced hyaline degeneration and distortion of the spleen capsule. CUR also suppressed ATO-induced NF-κB activation, pro-inflammatory cytokine addition and expression of autophagy-related genes. Overall, these results suggested that CUR might exert a protective effect against ATO-induced immunosuppression in ducks via anti-inflammation and autophagy restoring.

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram.

Background: The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. Patients and Methods: We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples. Results: A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62-0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68. Conclusions: The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.

Clinical Study of Intravitreal Injection of Anti-VEGF Drugs Combined with Triamcinolone Acetonide in the Treatment of Coats Disease.

Objective: To investigate the clinical study of intravitreal injection of anti-VEGF drugs combined with intravitreal injection of tretinoin for the treatment of Coats disease. Methods: The medical records of 90 patients (120 eyes) with Coats disease admitted to our hospital from April 2010 to June 2021 were selected as retrospective study subjects and divided into control and treatment groups according to the numerical table method. There were 45 cases and 60 eyes in each group. Among them, intravitreal tretinoin drug was injected into the control group, and anti-VEGF drug was injected into the vitreous sclera of the treatment group on the basis of the control group. Results: Before treatment, the differences in visual acuity changes between the two groups were not statistically significant (P > 0.05). After treatment, the light perception, manipulation, and indexes of both groups were significantly improved, and the treatment group was significantly better than the control group. This difference was statistically significant (P < 0.05) for statistical study comparison. The effective rate of 95.56% in the treatment group was significantly higher than that of 86.67% in the control group, and the complication rate was significantly lower than that of the control group, with statistically significant differences (P < 0.05). Conclusion: Intravitreal injection of anti-VEGF drugs combined with tretinoin injection can significantly improve the clinical efficacy of patients with Coats disease and improve visual acuity and central retinal thickness, which has certain reference value for the clinical treatment of Coats disease.

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis.

Purpose: This study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery. Methods: The clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups. Results: A total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05). Conclusion: Several costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.

Study on the mechanism of Cortex Lycii on lung cancer based on network pharmacology combined with experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: Xie Bai San is a Chinese medicine prescription that has been used to treat lung cancer in China for a long time. It has been proven to alleviate the symptoms and extend the survival time of lung cancer patients. Xie Bai San comprises Cortex Lycii, Cortex Mori, and Radix Glycyrrhizae Preparata. The effects and mechanisms of Cortex Mori and Glycyrrhizae on lung cancer have been reported, whereas the underlying mechanism of Cortex Lycii remains unknown. MATERIAL AND METHODS: Network pharmacology was used to explore the unknown mechanisms underlying the effect of Cortex Lycii on lung cancer. Molecular docking was used to predict the binding of a compound to the protein. The fingerprint of Cortex Lycii was obtained by HPLC. Cell counting Kit-8 assay was used to determine the appropriate concentration of Cortex Lycii extract for human lung adenocarcinoma cells, A549 and H1299. Wound healing assay and Matrigel invasion assay were used to detect the influence of Cortex Lycii extract on the migration and invasion ability of A549 and H1299. The protein expression level was detected by western blot and immunohistochemical staining. RESULTS: Using network pharmacology, 38 components of Cortex Lycii and 79 possible lung cancer-related target genes of Cortex Lycii were obtained. The targets were assigned to 35 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and the PI3K-AKT signaling pathway contained the most targets and had the second-lowest P-value. The molecular docking showed the components of Cortex Lycii bound to HSP90AB1. Among them, 6 components bound to HSP90AB1 in which HSP90AB1 binds to and phosphorylates AKT. The functional experiments showed that Cortex Lycii suppressed the migration and invasion of human lung cancer cells in a dose-dependent manner. Cortex Lycii up-regulated E-Cadherin and down-regulated N-Cadherin, Vimentin, and MMP2. Furthermore, Cortex Lycii made no change in the total AKT and mTOR protein levels, but caused the down-regulation of p-AKT and p-mTOR in human lung cancer cells, which was reversed by Terazosin, an agonist of HSP90. Moreover, acacetin and apigenin, two components of Cortex Lycii, reduced the protein level of p-AKT and p-mTOR, and the reduction was also inhibited by Terazosin. CONCLUSION: Cortex Lycii suppressed epithelial-mesenchymal transition (EMT) in lung cancer cells through the PI3K-AKT-mTOR signaling pathway, possibly by targeting HSP90AB1 and inhibiting HSP90AB1-AKT binding.

Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram.

OBJECTIVE: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. METHODS: The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. RESULTS: The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%-.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01-0.79. CONCLUSION: A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

Arsenic trioxide-induced autophagy affected the antioxidant capacity and apoptosis rate of chicken hepatocytes.

Arsenic has recently received widespread attention due to its high toxicological effects on multiple animals; however, the mechanism underlying this toxicity is unclear. We investigated the damaging effects of arsenic trioxide (ATO) on hepatocytes and the effects of regulating autophagy on the hepatocyte damage induced by ATO exposure. First, we investigated the effects of ATO exposure (0, 0.6, 1.2, 2.4, and 4.8 µM) on the biochemical function and autophagy of chicken hepatocytes. The findings showed that as the concentration of ATO increased, the lactate dehydrogenase (LDH) concentration increased, more autophagosomes were observed via transmission electron microscopy (TEM), and the gene and protein expression levels of P62, LC3Ⅱ, and Beclin1 increased. Adding N-acetyl-l-cystine (NAC, 1 mM) attenuated autophagy and the hepatocyte damage induced by ATO. Then, we used rapamycin (Rapa) and 3-methylpurine (3-MA) to regulate the autophagy induced by exposure to 4.8 µM ATO and observed changes in the antioxidant capacity and apoptosis rate of chicken hepatocytes. Induction of autophagy reduced ATO-induced hepatocyte apoptosis but caused no significant effect on oxidative stress in chicken hepatocytes. Inhibition of autophagy exacerbated ATO-induced hepatocyte oxidative stress and apoptosis. These findings demonstrate that autophagy plays an important role in ATO-induced cell damage.

Pulmonary embolism following the third thoracic tuberculosis surgery: A case report and literature review.

BACKGROUND: The study aimed to analyze the clinical effects of pulmonary embolism succeeding a third surgery conducted for multiple recurrences in thoracic tuberculosis (TB). CASE REPORT: A 74-year-old female patient developed thoracic tuberculosis and was subsequently treated in our hospital in March 2019, October 2020, and February 2021. The third surgical intervention included anterolateral thoracic lesion resection, internal fixation, posterior spinal tuberculous sinus resection, and debridement with suture. The operative time was 172 min resulting in a substantial intraoperative blood loss (2321 ml). Postoperative re-examination of chest CTPA indicated a strip filling defect and pulmonary embolism in the external branch of the right middle lobe of the lung. After completing the active treatment, the D-dimer quantification, WBC, CRP, and ESR values were 1261 ng/ml, 7.71 × 109 /L, 74.66 mg/L, and 63 mm, respectively. Chest CTPA re-examination after the treatment showed no signs of pulmonary embolism. CONCLUSION: Patients with a long-term history of multiple operations, high BMI, cerebral infarction, diabetes, and older age group were more likely to develop pulmonary embolism after spinal tuberculosis surgery. Thus, the possibility of postoperative pulmonary embolism should be thoroughly analyzed before any subsequent surgical treatment in patients with recurrent spinal tuberculosis.

Development and Validation of a Nomogram for Predicting Albumin Transfusion After Spinal Tuberculosis Surgery: Based on Propensity Score Matching Analysis.

BACKGROUND: There have been few literature reports on the use of perioperative parameters to predict the risk of albumin transfusion after spinal tuberculosis surgery based on the application of nomogram and propensity score matching (PSM) analysis. OBJECTIVE: The purpose was to predict the risk of albumin transfusion after spinal tuberculosis surgery based on a combination of PSM and nomogram. METHODS: The clinical data of the patients were collected in our hospital, including preoperative clinical data, preoperative laboratory tests, and postoperative clinical data. All data were divided into 2 groups, including the albumin transfusion group and the non-albumin transfusion group. The PSM analysis was used to adjust the baseline data of the 2 groups. The nomogram was further constructed. The practicability and predictive ability of the model were evaluated. RESULTS: A total of 494 cases were collected in this article; 102 pairs by PSM analysis were used to construct the nomogram. There were statistical differences in surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis by logistic analysis, and these parameters were included in the construction of the nomogram. The C-index of the prediction model was 0.734. The area under the curve was 0.73 and the net benefit was between 0.13 and 0.99. The calculated C-index was 0.71 by the internal verification method. CONCLUSIONS: The PSM analysis had a good matching effect and the nomogram had a good predictive ability. Surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis might be predictors of albumin transfusion after spinal tuberculosis surgery.

Curcumin alleviates arsenic-induced injury in duck skeletal muscle via regulating the PINK1/Parkin pathway and protecting mitochondrial function.

Arsenic is a well-known environmental pollutant due to its toxicity, which can do harm to animals and human. Curcumin is a polyphenolic compound derived from turmeric, commonly accepted to have antioxidant properties. However, whether curcumin can ameliorate the damage caused by arsenic trioxide (ATO) in duck skeletal muscle remains largely unknown. Therefore, the present study aims to investigate the potential molecular mechanism of curcumin against ATO-induced skeletal muscle injury. The results showed that treating with curcumin could attenuate body weight loss induced by ATO and reduced arsenic content accumulation in the skeletal muscle of duck. Curcumin was also able to alleviated the oxidative stress triggered by ATO, which was manifested by the increase of T-AOC and SOD, and MDA decrease. Moreover, we observed that curcumin could ease mitochondrial damage and vacuolate degeneration of nucleus. Our further investigation found that ATO disrupted normal mitochondrial fission/fusion (Drp1, OPA1, Mfn1/2) and restrained mitochondrial biogenesis (PGC-1α, Nrf1/2, TFAM), while curcumin could promote mitochondrial fusion and activated PGC-1α pathway. Furthermore, curcumin was found that it could not only reduce the mRNA and protein levels of mitophagy (PINK1, Parkin, LC3, p62) and pro-apoptotic genes (p53, Bax, Caspase-3, Cytc), but also increased the levels of anti-apoptotic genes (Bcl-2). In conclusion, curcumin was able to alleviate ATO-induced skeletal muscle damage by improving mitophagy and preserving mitochondrial function, which can serve as a novel strategy to take precautions against ATO toxicity.

Case report: Fatal systemic embolism caused by early prosthetic valve endocarditis after Bentall surgery.

Prosthetic valve endocarditis (PVE) is a rare but dangerous complication of Bentall surgery and Staphylococcus epidermidis PVE involving multiple valves simultaneously during the early postoperative period has not been reported. A 42 year old patient admitted to intensive care unit with fever 1 month after aortic valve replacement (Bentall procedure). Echocardiography was of great diagnosis value and suggested large, mobile vegetations on both the prosthetic aortic valve and native tricuspid valve. The presence of Staphylococcus epidermidis was revealed by multiple blood cultures. Surgery was not performed because of the history of aortic valve replacement 1 month ago. He developed acute right femoral artery thromboembolism, multiple cerebral infarction and splenic infarction during hospitalization and died of cerebral infarction after being discharged. This case underlines that patients with early PVE may have poor prognosis and fatal systemic embolism should be aware of in PVE patients with large vegetations present with dyskinesia, abdominal pain, and limb numbness. The timely echocardiography and vascular ultrasound are primary and reliable diagnostic methods in this scenario.