A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

OBJECTIVE: To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. METHODS: The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. RESULTS: A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. CONCLUSIONS: We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.

Arthroscopic treatment of osteoid osteoma in the posterior proximal tibia: A case report and literature review.

BACKGROUND: Osteoid osteoma (OO) is a benign lesion characterized by an increased fibrous component in the bone marrow, presence of bone-like structures within the medullary cavity, and a surrounding sclerotic bone rim. Reports on OO located in the posterior proximal tibia are rare. CASE SUMMARY: Herein, we report the case of an 18-year-old male, admitted for the evaluation of right knee pain. The right knee pain had started 6 months prior without any apparent cause, which was notably severe at night, affecting sleep, and was exacerbated while climbing stairs or bearing weight. The patient also experienced pain on flexion. Three-dimensional computed tomography and magnetic resonance imaging revealed a nodular lesion beneath the cortical bone of the posterior medial plateau of the right tibia and an abnormal signal focus on the posterior lateral aspect of the right tibial plateau associated with extensive bone marrow edema. A small amount of fluid was present in the right knee joint capsule. The patient subsequently underwent arthroscopic excision of the OO. Postoperatively, there was significant relief of pain, and the knee range of motion returned to normal. CONCLUSION: Although OO in the posterior proximal tibia is a rare occurrence, it can be effectively excised through minimally invasive arthroscopic visualization.

Palmitic acid and trans-4-hydroxy-3-methoxycinnamate, the active ingredients of Yaobishu formula, reduce inflammation and pain by regulating gut microbiota and metabolic changes after lumbar disc herniation to activate autophagy and the Wnt/ß-catenin pathway.

The imbalance in gut microbiota triggers an inflammatory response that spreads from the gut to the discs and is associated with lumbar disc herniation (LDH). In this study, we investigated the mechanism of palmitic acid (PA) and trans-4-hydroxy-3-methoxycinnamic acid (THMC) on microbiota, metabolic homeostasis, and autophagy after LDH. The LDH rat model was established by puncturing the exposed intervertebral disc. 16S rDNA was used to assess the gut microbiome composition. The microbial metabolites were analyzed by UPLC-MS. The mechanism of PA and THMC in LDH was explored by fecal microbiota transplantation (FMT). We found that Yaobishu, PA, THMC, and the positive control drug Celebrex attenuated intervertebral disc damage in LDH rats and downregulated TRPV1, IL-1ß, and IL-18 expression. In addition, Yaobishu reduced Oscillospirales and Ruminococcaceae abundances after LDH. PA increased Bacilli's abundance while decreasing Negativicutes and Ruminococcaceae abundances. Metabolomics showed that Yaobishu increased 2-hexanone, methyl isobutyl ketone, 2-methylpentan-3-one, and nonadecanoic acid levels but decreased pantetheine and urocanate levels. PA and THMC reduced uridine and urocanate levels. Yaobishu, PA, and THMC activated autophagy and the Wnt/ß-catenin pathway in LDH rats. Moreover, antibiotics abrogated these effects. FMT-PA and FMT-THMC activated autophagy and decreased IL-1ß, IL-18, Wnt1, ß-catenin, and TRPV1 expression. FMT-PA and FMT-THMC partially reversed the effects of 3-MA. Taken together, our data suggest that Yaobishu, PA, and THMC relieve inflammation and pain by remodeling the gut microbiota and restoring metabolic homeostasis after LDH to activate autophagy and the Wnt/ß-catenin pathway, which provide a new therapeutic target for LDH in the clinic.

Development and validation of a nomogram model for predicting the risk of venous thromboembolism in lymphoma patients undergoing chemotherapy: a prospective cohort study conducted in China.

BACKGROUND: The mechanism of Venous thromboembolism (VTE) is complicated and difficult to prevent due to factors such as bone marrow invasion, therapy, and immune-mediated effects. This study aims to establish a nomogram model for predicting the risk of thrombosis in lymphoma patients undergoing chemotherapy, which has been increasing over the past 30 years. METHODS: The data of lymphoma patients from the Affiliated Cancer Hospital of Chongqing University in China between 2018 and 2020 were analyzed. This included age, sex, body mass index, ECOG score, histological type, Ann Arbour Stage, white blood cells count, haemoglobin level, platelet count, D-dimer level, and chemotherapy cycle. Univariate and multivariate cox analysis was used to determine the risk factors for VTE. Characteristic variables were selected to construct a nomogram model which was then evaluated using ROC curve and calibration. RESULTS: Age, sex, PLT, D-dimer and chemotherapy cycle were considered as independent influencing factors of VTE. The mean (standard deviation) of the C index, AUC and Royston D statistics of 1000 cross-validations of the Nomogram model were 0.78 (0.01), 0.81 (0.01) and 1.61(0.07), respectively. It indicates a good calibration degree and applicability value as shown by the calibration curve. The DCA curve showed a rough threshold range of 0.05-0.60 with a good model. CONCLUSIONS: We have established and validated a nomogram model for predicting the risk of thrombosis in lymphoma patients. This model can assess the risk of thrombosis in each individual patient, enabling the identification of high-risk groups and targeted preventive treatment.

Machine learning in the prediction of post-stroke cognitive impairment: a systematic review and meta-analysis.

Objective: Cognitive impairment is a detrimental complication of stroke that compromises the quality of life of the patients and poses a huge burden on society. Due to the lack of effective early prediction tools in clinical practice, many researchers have introduced machine learning (ML) into the prediction of post-stroke cognitive impairment (PSCI). However, the mathematical models for ML are diverse, and their accuracy remains highly contentious. Therefore, this study aimed to examine the efficiency of ML in the prediction of PSCI. Methods: Relevant articles were retrieved from Cochrane, Embase, PubMed, and Web of Science from the inception of each database to 5 December 2022. Study quality was evaluated by PROBAST, and c-index, sensitivity, specificity, and overall accuracy of the prediction models were meta-analyzed. Results: A total of 21 articles involving 7,822 stroke patients (2,876 with PSCI) were included. The main modeling variables comprised age, gender, education level, stroke history, stroke severity, lesion volume, lesion site, stroke subtype, white matter hyperintensity (WMH), and vascular risk factors. The prediction models used were prediction nomograms constructed based on logistic regression. The pooled c-index, sensitivity, and specificity were 0.82 (95% CI 0.77-0.87), 0.77 (95% CI 0.72-0.80), and 0.80 (95% CI 0.71-0.86) in the training set, and 0.82 (95% CI 0.77-0.87), 0.82 (95% CI 0.70-0.90), and 0.80 (95% CI 0.68-0.82) in the validation set, respectively. Conclusion: ML is a potential tool for predicting PSCI and may be used to develop simple clinical scoring scales for subsequent clinical use. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=383476.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China.

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Development and validation of prediction model for overall survival in patients with lymphoma: a prospective cohort study in China.

OBJECTIVE: The survival of patients with lymphoma varies greatly among individuals and were affected by various factors. The aim of this study was to develop and validate a prognostic model for predicting overall survival (OS) in patients with lymphoma. METHODS: We conducted a prospective longitudinal cohort study in China between January 2014 and December 2018 (n = 1,594). After obtaining the follow-up data, we randomly split the cohort into the training cohort (n = 1,116) and the validation cohort (n = 478). The least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the predictors of the model. Cox stepwise regression analysis was used to identify independent prognostic factors, which were finally displayed as static nomogram and web-based dynamic nomogram. We calculated the concordance index(C-index) to describe how the predicted survival of objectively confirmed prognosis. The calibration plot is used to evaluate the prediction accuracy and discrimination ability of the model. Net reclassification index (NRI) and decision curve analysis (DCA) curves were also used to evaluate the prediction ability and net benefit of the model. RESULTS: Nine variables in the training cohort were considered to be independent risk factors for patients with lymphoma in the final model: age, Ann Arbor Stage, pathologic type, B symptoms, chemotherapy, targeted therapy, lactate dehydrogenase (LDH), ß2-microglobulin and C-reactive protein (CRP). The C-indices of OS were 0.749 (95% CI, 0.729-0.769) in the training cohort and 0.731 (95% CI, 0.762-0.700) in the validation cohort. A good agreement between prediction by nomogram and actual observation was shown in the calibration curve for the probability of survival in both the training cohort and validation cohorts. The areas under curve (AUC) of the area under the receiver operating characteristic (ROC) curves for 1-year, 3-year, and 5-year OS were 0.813, 0.800, and 0.762, respectively, in the training cohort, and 0.802, 0.768, and 0.721, respectively, in the validation cohort. Compared with the Ann Arbor Stage system, NRI and DCA showed that the model had a higher predictive capacity and net benefit. CONCLUSION: The prediction models reliably estimate the outcome of patients with lymphoma. The model had high discrimination and calibration, which provided a simple and reliable tool for the survival prediction of the patients, and it might help patients benefit from personalized intervention.

[Progress in diagnosis and hip arthroscopic treatment of borderline developmental dysplasia of hip with Cam-type femoroacetabular impingement].

Objective: To summarize the biomechanical characteristics, diagnosis, and hip arthroscopic treatment of borderline developmental dysplasia of hip (BDDH) with Cam-type femoroacetabular impingement (Cam FAI). Methods: The literature on BDDH with Cam FAI at home and abroad in recent years was extensively reviewed and analyzed. Results: In patients with BDDH and Cam FAI, the femoral neck anteversion angle and femoral neck shaft angle increase, the pelvis tilts, and the acetabulum rotates, resulting in instability of the hip joint. In order to maintain the stability of the hip joint, the direction of biomechanical action of the hip joint has changed, which further affects the anatomical structures such as the proximal femur and acetabular morphology. BDDH with Cam FAI can be diagnosed clinically by combining lateral center edge angle, anterior center edge angle, and acetabular index. BDDH with Cam FAI can be effectively treated through arthroscopic polishing of the edges of the acetabular proliferative bone, excision of Cam malformations, and minimally invasive repair of the glenoid lip and cartilage of the hip joint. Conclusion: Currently, there is no unified standard for the diagnosis and treatment of BDDH with Cam FAI. Minimally invasive treatment of the hip under arthroscopy can achieve good early- and medium-term effectiveness, and has certain advantages in repairing and maintaining the integrity of the glenoid lip and suturing/compression joint capsule. However, the long-term effectiveness needs to be further followed up to determine. The timing of surgery, intraoperative bone edge depth polishing, and joint capsule suturing/compression techniques also need to be further explored.

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.

Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China.

OBJECTIVE: Nasopharyngeal carcinoma (NPC) is prevailing in Southern China, characterized by distinct geographical distribution. Aimed to predict the overall survival (OS) of patients with nasopharyngeal carcinoma, this study developed and validated nomograms considering demographic variables, hematological biomarkers, and oncogenic pathogens in China. METHODS: The clinicopathological and follow-up data of the nasopharyngeal carcinoma patients obtained from a prospective longitudinal cohort study in the Chongqing University Cancer Hospital between Jan 1, 2017 and Dec 31, 2019 ([Formula: see text]). Cox regression model was used to tested the significance of all available variables as prognostic factors of OS. And independent prognostic factors were identified based on multivariable analysis to model nomogram. Concordance index (C-index), area under the receiver operating characteristic (AUC), calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of nomogram. RESULTS: Data was randomly divided into a training cohort (1227 observers, about 70% of data) and a validation group (408 observers, about 30% of data). At multivariable analysis, the following were independent predictors of OS in NPC patients and entered into the nomogram: age (hazard ratio [HR]: 1.03), stage (stage IV vs. stage I-II, HR: 4.54), radiotherapy (Yes vs. No, HR: 0.43), EBV ([Formula: see text] vs.[Formula: see text], HR: 1.92), LAR ([Formula: see text] vs.[Formula: see text], HR: 2.05), NLR ([Formula: see text] vs. [Formula: see text] HR: 1.54), and PLR ([Formula: see text] vs.[Formula: see text], HR: 1.79). The C-indexes for training cohort at 1-, 3- and 5-year were 0.73, 0.83, 0.80, respectively, in the validation cohort, the C-indexes were 0.74 (95% CI 0.63-0.86), 0.80 (95% CI 0.73-0.87), and 0.77 (95% CI 0.67-0.86), respectively. The calibration curve demonstrated that favorable agreement between the predictions of the nomograms and the actual observations in the training and validation cohorts. In addition, the decision curve analysis proved that the nomogram model had the highest overall net benefit. CONCLUSION: A new prognostic model to predict OS of patients with NPC was developed. This can offer clinicians treatment making and patient counseling. Furthermore, the nomogram was deployed into a website server for use.

SEIR model and simulation research on unsafe psychological state propagation of construction workers considering safety climate and intimate relationships.

The construction industry is a pillar industry of China and occupies an essential position in our economic development. However, in the fast-developing construction industry, the number of its safety accidents is also growing year by year. Safety accidents are often due to unsafe behaviors of construction workers, and unsafe precarious psychological states are important factors for unsafe behaviors. Therefore, this paper, based on a review of existing literature, uses the SEIR model and numerical simulation method to study the spread of unsafe psychological states among construction workers considering safety climate and intimate relationships. It puts forward corresponding countermeasures, which has great practical significance for reducing safety accidents in the construction industry. The results show that: (1) A good safety climate can help alleviate the spread of unsafe psychological states of construction workers. (2) The intimate relationship between construction workers will promote the association between communicable people and susceptible people, which will lead to the spread of an unsafe psychological state. (3) A larger network average degree will increase the spread speed and the density of communicable people, but will not increase the spread range.(4) Forgetting rate has a key role in the propagation of unsafe psychological states. Suggestions are made to hinder the propagation of these states, which will help to reduce the unsafe behavior of construction workers and the accident rate in the construction industry.

Effects of Uremic Clearance Granules on p38 MAPK/NF-κB Signaling Pathway, Microbial and Metabolic Profiles in End-Stage Renal Disease Rats Receiving Peritoneal Dialysis.

Background: End-stage renal disease (ESRD) is the final stage of chronic kidney disease (CKD). In the clinic, Uremic Clearance Granules (UCG) are mainly used in the treatment of early CKD and stabilized renal function. However, the benefits and mechanisms of UCG on ESRD remain unclear. Methods: Rats were randomly divided into four groups: sham group, model group, peritoneal dialysis (PD) group and UCG group. Except for the sham-operated group, ESRD was induced by 5/6 nephrectomy in the other three groups. The PD group and UCG group were then subjected to PD. In addition, the UCG group was given UCG by gavage when PD. Changes in body weight and final kidney weight of rats in each group were monitored. HE and Masson staining were performed to confirm the extent of renal fibrosis. Biochemical kits were used to detect blood urea nitrogen (BUN), serum and urine creatinine (Scr, Cre), and urine protein (UPr) levels. ELISA was used to detect the rats' inflammatory responses. qRT-PCR, WB, and IHC were probed to determine the expression levels of NF-κB and MAPK. 16S rDNA sequencing was performed to analyze the composition of gut microbiota in rats. A liquid chromatograph-mass spectrometer was performed to reveal serum metabolomics changes. Results: UCG increased renal volume and body weight, improved renal fibrosis. It enhanced renal function and decreased the levels of BUN, Scr, Upr, Cre, inflammatory responses, as well as NF-κB and MAPK expressions in renal and colon tissues of ESRD rats. The relative abundances of Bacteroidetes and Firmicutes changed in ESRD rats in response to UCG. Serum metabolomics was utilized to identify 70 differentiated metabolites, which were associated with D-glutamine and D-glutamate metabolism, and Phenylalanine metabolism. Conclusion: Our study confirmed that UCG alleviated ESRD by regulating p38 MAPK/NF-κB signaling pathway, microbial and metabolic profiles.

Correlation of Serum IGF-1R, VEGF, and ET Levels with Bone Mineral Density in Type 2 Diabetic Mellitus Patients Treated with Metformin Plus α-Glucosidase Inhibitors.

Diabetes mellitus is a common chronic disease. This study aimed to investigate the correlation between serum insulin-like growth factor 1 receptor (IGF-1R), vascular endothelial growth factor (VEGF), endothelin (ET) levels, and bone mineral density (BMD) in type 2 diabetic mellitus (T2DM) patients treated with metformin plus α-glucosidase inhibitors and evaluate the predictive value of serum factors in the prognosis of osteoporosis in these patients. It was a prospective study that enrolled 142 patients with T2DM treated in Dinghu District People's Hospital from March 2019 to May 2020. All enrollments were randomized (1 : 1) to receive either metformin (control group) or metformin plus α-glucosidase inhibitors (study group). After 12 weeks of treatment, metformin plus α-glucosidase inhibitors were associated with significantly lower levels of 2 hPG, FPG, HbA1c, and HOMA-IR versus metformin alone (P < 0.05). After treatment, the BMD was positively correlated with IGF-1R and negatively correlated with VEGF and ET. Alpha-glucosidase inhibitors plus metformin for primary T2DM can effectively manage blood glucose and reduce insulin resistance in patients, but the prediction of osteoporosis development remains to be further explored in large sample studies.

Lung Cancer-Specific Mortality Risk and Public Health Insurance: A Prospective Cohort Study in Chongqing, Southwest China.

Objective: The incidence and mortality of lung cancer rank first among malignant tumors, and its long treatment cycle will bring serious economic burdens to lung cancer patients and their families. There are few studies on the prognosis of lung cancer and insurance policies. This article explores the relationship between the lung cancer-specific death and public health insurance, self-paying rate, and the joint effect of public health insurance and self-paying rate. Materials and Methods: A prospective longitudinal cohort study was conducted in Chongqing, China from 2013 to 2019. The selected subjects were patients with C33-C34 coded according to the tenth edition of the International Classification of Diseases (ICD-10), aged 20 years or older. We conduct a subgroup analysis based on public health insurance types and self-paying rates. After following the inclusion and exclusion criteria, the chi-square test was used to describe the demographic and clinical characteristics of patients with different insurance types and different self-paying rates. Multivariate logistic regression was used to analyze the relationship between patients with different insurance types, self-paying rates, and lung cancer treatment methods. Finally, the Cox proportional hazard model and the competitive risk model are used to calculate the cumulative hazard ratio of all-cause death and lung cancer-specific death for different insurance types and different self-paying rate groups. Results: A total of 12,464 patients with lung cancer were included in this study. During the follow-up period (median 13 months, interquartile range 5.6-25.2 months), 5,803 deaths were observed, of which 3,781 died of lung cancer. Compared with patients who received urban resident-based basic medical insurance (URBMI), patients who received urban employee-based basic medical insurance (UEBMI) had a 38.1% higher risk of lung cancer-specific death (Hazard Ratios (HRs) = 1.381, 95% confidence interval (CI): 1.293-1.476, P < 0.005), Compared with patients with insufficient self-paying rate, patients with a higher self-paying rate had a 40.2% lower risk of lung cancer-specific death (HRs = 0.598, 95% CI: 0.557-0.643, P < 0.005). Every 10% increase in self-paying rate of URBMI reduces the risk of lung cancer-specific death by 17.6%, while every 10% increase in self-paying rate of UEBMI reduces the risk of lung cancer-specific death by 18.0%. Conclusions: The National Medical Security Administration should, under the condition of limited medical insurance funds, try to include the original self-paid anti-tumor drugs into the national medical insurance coverage. This can not only reduce the mortality rate of lung cancer patients, but also reduce the family burden of lung cancer patients. On the other hand, high-risk groups should increase their awareness of lung cancer screening and actively participate in the national cancer screening project led by the state.

Yaobishu Regulates Inflammatory, Metabolic, Autophagic, and Apoptosis Pathways to Attenuate Lumbar Disc Herniation.

Objective: Here, we aimed to explore the main mechanism of Yaobishu (YBS) in lumbar disc herniation (LDH). Methods and Results: Eighteen compounds that might act on LDH were obtained through a combination of network pharmacology prediction and identification by high-performance liquid chromatography-mass spectrometry. The key compounds were palmitic acid and trans-4-hydroxy-3-methoxycinnamate (cinnamate). KEGG analysis demonstrated that palmitic acid target genes mainly regulate the PPAR signaling pathway, Ras signaling pathway, and fatty acid metabolism. Cinnamate target genes were primarily involved in chemical carcinogenesis-receptor activation, lipid and atherosclerosis, the HIF-1 signaling pathway, and nitrogen metabolism. The rat LDH model was constructed using autologous nucleus pulposus tissue implantation. Differential expression gene (DEGs) related to metabolism (CDKN1A and UHRF1), inflammation (S100A9 and SOCS3), autophagy (DCN and LEPR), and apoptosis (CTSW and BCL2A1) in dorsal root ganglion (DRG) tissues of the control and LDH groups was evaluated by RNA-Seq. TNF-α stimulated DRG neuronal cells were used to establish an in vitro LDH model. YBS, palmitic acid, and cinnamate reduced the expression of substance P, CGRP, S100A9, CTSW, and cleaved caspase-3, while enhancing the expression of CDKN1A, UHRF1, PCNA, Ki67, SOCS3, DCN, LEPR, and BCL2A1, as well as telomerase activity. Pearson's correlation analysis confirmed that DCN was positively correlated with BCL2A1, indicating that autophagy might be negatively correlated with apoptosis in LDH. YBS, palmitic acid, and cinnamate reduced the Siegal neurological score and serum IL-1ß and IL-18 levels, while increasing changes in the hind paw mechanical withdrawal threshold. The RNA-Seq results further showed that YBS downregulated S100A9 and CTSW expression, while upregulating SOCS3, CDKN1A, UHRF1, DCN, LEPR, and BCL2A1 expression. Conclusion: YBS and its compounds, palmitic acid, and cinnamate, attenuated LDH by regulating the inflammatory, metabolic, autophagic, and apoptotic pathways. Our results might improve the theoretical and experimental basis for clinical applications of LDH disease treatment.

Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning.

Background: There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients. Methods: Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model. Results: In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision-recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% CI: 0.893-0.926) and an AUPRC of 0.43 (95% CI: 0.363-0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802-0.917) and AUPRC of 0.30 (95% CI: 0.265-0.358), respectively. Conclusion: According to the study results, there was no conspicuous decrease in the model's performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.

The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis.

OBJECTIVE: We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions. METHODS: A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively. RESULTS: Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, p=0.016). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p < 0.001) or the AI system (57.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, p=0.13). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p < 0.001) or compared with the AI system (56.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively. CONCLUSIONS: The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.

Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients.

Background: Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis. Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan. We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients. This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients. Methods: Multiple machine-learning models were used in a retrospective cohort of 6586 patients. First, we modeled and validated a nomogram to predict the overall survival of NSCLC patients. Subsequently, five machine-learning models (logistic regression, random forest, XGBoost, decision tree, and light gradient boosting machine) were used to predict survival status. Next, we evaluated the performance of the models. Finally, the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure: time-dependent prediction accuracy. Results: Among the five machine-learning models, the accuracy of random forest model outperformed the others. Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months, the prediction accuracies of both the nomogram and machine-learning models changed as time varied. The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month, and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month. Conclusions: Overall, the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period. Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies, the conventional nomogram was competitive. In real clinical applications, a comprehensive model that combines these two methods may demonstrate superior capabilities.

Preoperative Predictors of Prolonged Hospital Stay in Accelerated Rehabilitation for Patients Undergoing Orthopedic Surgery.

In this study, we performed a retrospective and prospective study of preoperative predictors of the length of stay (LOS) in three groups of surgical patients and conducted a clinical retrospective study of the current research status of preoperative predictors of LOS prolongation in three groups of patients under ERAS (enhanced recovery after surgery) mode, such as patient characteristics and comorbidities. Information such as patients' exercise preferences, exercise time, frequency and duration, footwear, location of knee osteoarthritis, whether there is a past history of knee injury, and smoking and drinking history was collected, and the research data of 312 patients undergoing the three operations were analyzed by SPSS. Meniscal injury-knee arthroscopy sample included a total of 104 people. Surgical sample for anterior cruciate ligament reconstruction included a total of 100 subjects. Knee osteoarthritis-knee replacement surgery sample included 148 people who were divided into two groups in a ratio of 1 : 1: one group used Mailuo Shutong pills during hospitalization (intervention group) and the other group did not (control group). The research conclusions are as follows. Meniscal Knee Arthroscopy. (1) Samples from different causes of injury showed significant differences for all injured sites. (2) Samples with different smoking and drinking histories all showed significant differences for the causes of injury. (3) Exercise hobby, exercise frequency, duration of each exercise and duration of exercise, and warm-up time before exercise all showed positive correlation. Anterior Cruciate Ligament Reconstruction Surgery. (4) Samples from different causes of injury showed significant differences for all the injured sites. (5) Age has a significant negative influence on the wearing of shoes at ordinary times. (6) Exercise hobby: the warm-up time before exercise had a significant negative influence on the injured area. (7) Two groups of analysis items of exercise frequency, exercise duration and exercise duration, preexercise warm-up time, and exercise hobby were typically positively correlated. Total Knee Arthroplasty. (8) There was a significant difference of 0.01 between the hospitalization days of the intervention group and the control group (p < 0.01), and the hospitalization days of the intervention group were significantly lower than those of the control group. These results indicated that Mailuo Shutong pills were of great significance for the treatment of orthopedic patients during the operation period in that it could effectively shorten the hospital stay of all orthopedic patients and strengthen the accelerated rehabilitation. (9) There was a significant positive correlation between the history of knee joint surgery and the use time of Mailuo Shutong pills. (10) There was a markable positive correlation between occupation and sports hobbies, sports time, frequency and duration, and footwear. There was a significant negative correlation between occupation and preexercise warm-up. (11) Exercise time, frequency, and duration have significant positive influence on BMI.

Efficacy of continuous ambulatory peritoneal dialysis combined with hemodialysis versus single hemodialysis in patients with end-stage renal disease.

OBJECTIVE: This study aimed to compare the efficacy of single hemodialysis (HD) and continuous ambulatory peritoneal dialysis (CAPD) combined with HD in the treatment of end-stage renal disease. METHODS: Seventy patients with end-stage renal disease in our hospital from January 2019 to December 2020 were included and divided into 35 patients in the single group (SG) and 35 patients in the combination group (CG) according to a random number table. The SG received HD treatment and the CG received CAPD combined with HD treatment. RESULTS: Hemoglobin and serum albumin levels were higher, blood urea nitrogen (BUN) and serum creatinine (Scr) levels were lower, and interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α) levels were lower in the CG than in SG at the end of treatment (P < 0.05). Ca2+ levels were higher and P3+ levels were decreased at the end of treatment in both groups compared with those before treatment (P < 0.05), and Ca2+ and P3+ levels at the end of treatment in the CG were not different from those in the SG (P > 0.05). The complication rate in the CG was 5.71%, which was lower than 25.71% in the SG (P < 0.05). Quality of life scores were higher in the CG than in the SG at the end of treatment (P < 0.05). CONCLUSION: CAPD combined with HD can improve renal function and nutritional levels more significantly, control inflammatory responses more effectively, and reduce complications compared to single HD treatment in patients with end-stage renal disease.