A nomogram predicting venous thromboembolism risk in primary liver cancer patients.

Cancer frequently causes venous thromboembolism (VTE), a leading cause of cancer-related mortality. Primary liver cancer (PLC) is prevalent and highly fatal, with an increased risk of venous thrombotic complications. Thus, we aimed to develop a nomogram model for predicting VTE in patients with PLC. We retrospectively analyzed 1,565 patients diagnosed with PLC between January 2018 and December 2022 at Chongqing University Cancer Hospital. Univariate logistic analysis and multivariate logistic regression identified eight significant risk factors: activated partial thromboplastin time (APTT) ≤ 32.20 s, D-dimer > 1.44 mg/L, lymphocyte count (LYM) ≤ 1.18 × 109/L, monocyte count (MONO) > 0.42 × 109/L, transarterial chemoembolization (TACE), surgical intervention, immunotherapy, and ß2-microglobulin. The nomogram model exhibited strong discriminatory power, with C indices of 0.753 and 0.710 for the training and validation cohorts, respectively. The calibration curve showed a strong correlation between predicted and actual probabilities. Additionally, decision curve analysis (DCA) and clinical impact curves (CIC) confirmed the model's clinical utility. This nomogram facilitates the identification of high-risk PLC patients, allowing for timely preventive and therapeutic interventions to reduce the risk of thrombosis.

A comparative study on 3D printing-assisted arthroscopic IDEAL point femoral tunnel positioning for anterior cruciate ligament reconstruction versus conventional arthroscopic positioning.

BACKGROUND: This study aimed to investigate the feasibility and precision of using a 3D-printed template for femoral tunnel placement in guiding the optimal positioning of the Internal anatomical stop and Low tension maintenance (IDEAL) bone tunnel during single-bundle anterior cruciate ligament (ACL) reconstruction. METHODS: A retrospective analysis was conducted on 40 patients who underwent arthroscopic single-bundle ACL reconstruction at our hospital between April 2021 and November 2021. In the direct vision group, the IDEAL bone tunnel was positioned using radiofrequency localization directly visualized at the stump. In the 3D-printed positioning group, preoperative CT scans and Digital Imaging and Communications in Medicine (DICOM) data were employed. Following the Quadrant method by Bernard, the femoral tunnel's depth was set at 25% and its height at 29%. Postoperative plain CT scans enabled the reconstruction of 3D models for both groups. The accuracy of femoral tunnel placement was then compared. RESULTS: The central locations of the bone tunnels in the direct vision group were at a mean depth of 25.74 ± 1.84% and a height of 29.22 ± 2.97%. In the 3D printing localization group, these values were 25.39 ± 2.98% for depth and 28.89 ± 2.50% for height, respectively. No significant differences were found in tunnel positioning between the groups. Both groups demonstrated statistically significant improvements in International Knee Documentation Committee Subjective Knee Form (IKDC) and Lysholm scores postoperatively, with no significant differences observed 12 months post-surgery. CONCLUSION: The findings of this study suggest that 3D printing-assisted arthroscopic IDEAL point femoral tunnel positioning and conventional arthroscopic positioning are feasible and effective for ACL reconstruction. Using 3D printing technology to design femoral anchor points in ACL reconstruction allows for the customization of anterior fork reconstruction and precise bone tunnel positioning, supporting the goal of individualized and accurate reconstruction.

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.

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.

[The Design, Synthesis, Characteristics and Photoelectric Properties Research of A Bulky Steric Hindrance 9-Phenyl-Fluorene Functionalized Aniline Intermediate].

In this paper, with sulfuric acid as a catalyst and acetic acid as solution at the temperature of 120 ℃ and refluxing period for 24 hours, a sterically hindered bulky 9,9-diarylfluorene intermediate of [9-(4-anilino)-9-phenyl-fluorene] had been successfully designed and synthesized with Friedel-Crafts reaction. The molecular structure of this compound was characterized in detail with nuclear magnetic resonance hydrogen spectrum, mass spectrometry, infrared ray, and so on. Nuclear magnetic resonance hydrogen spectrum and infrared ray spectrum of the compound indicated that the characteristic functional group amino of the compound at 6.55 ppm, 3 481 and 3 385 cm-1, respectively. UV-visible and fluorescence spectra properties of 9-(4-anilino)-9-phenyl-fluorene were characterized and discussed preliminarily, respectively. The research results show that the 9-(4-anilino)-9-phenyl-fluorene with bulky steric hindrance effects has four main absorption peaks with wavelength of 243, 257, 298 and 311 nm in dichloromethane solution, respectively. Moreover, with the excitation wavelength of 308 nm, an emission spectrum curve was obtained with wavelength of 300～500 nm, which has the maximum emission peak of 328 nm with a slim peak at about 405 nm and a long tail to 500 nm. The long tail was probably attributed to the interaction of intermolecular hydrogen bonding from aniline. The appropriate scope of fluorescence emission (300~500 nm) make the compound overlap with the absorption spectra of the classic blue material Bis(3,5-difluoro-2-(2-pyridyl)phenyl-(2-carboxypyridyl)iridium (Ⅲ) (FIrpic) (300~500 nm). It is possible to obtain excellent host materials through proper molecular tailoring and prepare well for high performance light-emitting device. In order to further understand the photoelectric properties of the compound, we used acetonitrile as solvent and tetrabutylammonium hexafluorophosphate as electrolyte, and the electrochemical properties of the compound was characterized with cyclic voltammetry measurements.The onset of the reduction and oxidation potential of the compound are -0.759 and 0.898 V, and the corresponding HOMO and LUMO energy levels are -5.38 and -3.72 eV, which wolud be beneficial to holes and electrons injection/transportation and further modified to be excellent host materials. All of these data would provide a useful reference for further fabrication of organic semiconductor luminescent device with high performance.

[Clinical analysis of prosthesis replacement for proximal humerus tumors].

OBJECTIVE: To explore surgical strategies, functions and prognosis of artificial prosthesis replacement and soft tissue reconstruction for patients with invasive benign or primary malignant proximal humerus tumor.
 METHODS: A total of 17 patients with invasive benign or primary malignant proximal humerus tumor underwent prosthetic replacement after segment bone tumor resection and soft tissues reconstruction from April 2007 to April 2014 were enrolled. Based on histological types, tumor stages and surgical procedures, the effects of artificial prosthesis replacement and soft tissue reconstruction on prognosis and shoulder joint function were evaluated.
 RESULTS: All patients were followed up for 8 to 96 months (average time: 58.9 months). Among 11 patients with primary malignant tumor, 5 died of tumor recurrence or metastasis, and 6 showed tumor-free survival for 24 to 91 months (average time: 54.83 months). The 6 patients with aggressive benign tumors survived for 39 to 96 months, with an average of 72.33 months. The shoulder joint function of 17 patients recovered to 64.88% of normal. There were significant differences in the shoulder joint function between the patients who underwent half shoulder replacement and those who underwent total shoulder replacement (56.25% vs 72.56%, P<0.05). There were significant differences in the shoulder joint function between the patients who underwent Type I A excision (retention of abductor muscles and rotator cuff) and those who underwent Type I B excision (68.75% vs 61.44%, P<0.05).
 CONCLUSION: The survival of patients with invasive benign or primary malignant proximal humerus, who underwent artificial prosthesis replacement and soft tissue reconstruction, is closely related to tumor types. The shoulder joint function is associated with the methods of prosthesis replacement and soft tissue resection.

Tetramethylpyrazine inhibits CTGF and Smad2/3 expression and proliferation of hepatic stellate cells.

To study the effects of tetramethylpyrazine (TMP) on the proliferation of hepatic stellate cells-T6 (HSC-T6), and the expression of connective tissue growth factor (CTGF) and Smad2/3 in these cells, HSC-T6 cells were cultured with TMP at different concentrations after transforming growth factor-ß1 (TGF-ß1) stimulation. MTT assay was used to assess the cell proliferation. Cells were divided into the control group, TGF-ß1-treated group and TMP-treated groups, which were treated with different concentrations of TMP. Immunocytochemistry and western blot were performed to detect the expression levels of CTGF and Smad2/3 in HSC-T6 cells. MTT analysis indicated that TMP significantly inhibited the proliferation of HSC-T6 cells, in dose-dependent and time-dependent manners. Immunocytochemistry detection and western blot showed that TMP could diminish TGF-ß1-induced CTGF over-expression in HSC-T6 cells. Similarly, the enhancing effects of TGF-ß1 on Smad2/3 expressions in HSC-T6 cells could also be counteracted by TMP treatment. Nuclear translocation of Smad2/3 was blocked by TMP treatment. Correlation analysis suggested a positive correlation between CTGF and Smad2/3 expression levels in HSC-T6 cells. TMP exerts anti-hepatic fibrosis effect through decreasing the expression of CTGF and Smad2/3, as well as inhibiting the proliferation of HSC-T6 cells. Our study provides cellular and molecular bases for further application of TMP in the clinical treatment for hepatic fibrosis.

[Effect of tetramethylpyrazine and rat CTGF miRNA plasmids on connective tissue growth factor, transforming growth factor-beta in high glucose stimulated hepatic stellate cells].

The aim of this research is to evaluate the effect of tetramethylpyrazine (TMP) and connective tissue growth factor (CTGF) miRNA plasmids on the expressive levels of CTGF, transforming growth factor-beta (TGFbeta) and type I collagen of rat hepatic stellate cells (HSC) which are stimulated by high glucose. The rat HSCs which were successfully transfected rat CTGF miRNA plasmids and the rat HSCs which were successfully transfected negative plasmids were cultured in vitro. After stimulus of the TMP and the high glucose, the protein levels and gene expressive levels of CTGF, TGF-beta and type I collagen were tested. The results indicated that high glucose increased the expression of CTGF mRNA, CTGF protein, TGF-beta mRNA,TGF-beta protein and type I collagen (P < 0.05). The expressive levels of CTGF mRNA, CTGF protein, TGF-beta mRNA, TGF-beta and type I collagen in TMP group were lower than those in high glucose group and showed statistically significant differences (P < 0.05). Compared with high glucose group, the expressive levels of CTGF mRNA, CTGF protein, TGF-beta mRNA, TGF-beta and type I collagen in rat CTGF miRNA plasmid interference group were significantly lower (P < 0.05). However, no statistically significant difference was found in CTGF mRNA and CTGF protein levels between TMP group and CTGF miRNA group (P > 0.05), while type I collagen levels showed statistically significant differences (P < 0.05). It is concluded that high glucose could promote the expressions of CTGF, TGF-beta and type I collagen, and TMP and rat CTGF miRNA plasmids could reduce the expressions of CTGF, TGF-beta, type I collagen.

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.

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.

Development and validation of a nomogram model for predicting venous thromboembolism risk in lung cancer patients treated with immune checkpoint inhibitors: A cohort study in China.

OBJECTIVE: Venous thromboembolism (VTE) poses a significant threat to lung cancer patients, particularly those receiving treatment with immune checkpoint inhibitors (ICIs). We aimed to develop and validate a nomogram model for predicting the occurrence of VTE in lung cancer patients undergoing ICI therapy. METHODS: The data for this retrospective cohort study was collected from cancer patients admitted to Chongqing University Cancer Hospital for ICI treatment between 2019 and 2022. The research data is divided into training and validation sets using a 7:3 ratio. Univariate and multivariate analyses were employed to identify risk factors for VTE. Based on these analyses, along with clinical expertise, a nomogram model was crafted. The model's predictive accuracy was assessed through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, clinical impact curve, and other relevant metrics. RESULTS: The initial univariate analysis pinpointed 13 potential risk factors for VTE. The subsequent stepwise multivariate regression analysis identified age, Karnofsky performance status, chemotherapy, targeted, platelet count, lactate dehydrogenase, monoamine oxidase, D-dimer, fibrinogen, and white blood cell count as significant predictors of VTE. These 10 variables were the foundation for a predictive model, illustrated by a clear and intuitive nomogram. The model's discriminative ability was demonstrated by the ROC curve, which showed an area under the curve of 0.815 (95% CI 0.772-0.858) for the training set, and 0.753 (95% CI 0.672-0.835) for the validation set. The model's accuracy was further supported by Brier scores of 0.068 and 0.080 for the training and validation sets, respectively, indicating a strong correlation with actual outcomes. CONCLUSION: We have successfully established and validated a nomogram model for predicting VTE risk in lung cancer patients treated with ICIs.

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.

The development of a prediction model based on random survival forest for the prognosis of non- Hodgkin lymphoma: A prospective cohort study in China.

Background and objective: The pathological staging of non-Hodgkin lymphoma (NHL) is complex, the clinical manifestations are varied, and the prognosis differ considerably. To provide a useful reference for early detection and effective treatment of NHL, we developed a random survival forest (RSF) prognostic model based on machine learning (ML) algorithms using prospective cohort data collected from Chongqing Cancer Hospital from Jan 1, 2017 to Dec 31, 2019 (n = 1449) to compare with the traditional cornerstone method Cox proportional hazards (CPH) model and evaluate the predictability of the model. Methods: Patients were randomly split into a training cohort (TC) and validation cohort (VC) based on 65/35 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extracted the important features. And the RSF was modeled to explore the prognostic factors impacting the overall survival (OS) of patients with NHLs in the TC and validated in the VC. The C-index, the Integrated Brier Score (IBS), Kaplan-Meir method, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were selected to measure performances and discriminations of the models. In addition, individual survival probability predicted for NHL patients. Results: According to the features extracted by LASSO model and univariable Cox model, 16 variables were selected to develop the RSF model with log-rank splitting rule, which were age, ethnicity, medical insurance, Ann Arbor stage, pathology, targeted-therapy, chemo-therapy, peripheral blood neutrophil count to lymphocyte count ratio (NLR), peripheral blood platelet count to lymphocyte count ratio (PLR), serum lactate dehydrogenase (LDH), CD4/CD8, platelet (PLT), absolute neutrophil count (ANC), lymphocyte (LYM), B-symptoms, and (CPR) were important prognostic factors. Compared to the CPH model (C-index = 0.748, IBS = 0.166), the RSF model (C-index = 0.786, IBS = 0.165) is outperformed in predictability and accuracy. The AUC of the RSF model to estimate the 1-, 3-, and 5-year OS in TC were 0.847, 0.847, and 0.809, respectively; while those in the CPH were 0.816, 0.803, and 0.750, respectively. Conclusions: To provide practical implications for the implementation of individualized therapy, the study constructed a high-performed RSF model and reveal that it outperformed the traditional model CPH. And the RSF model ranked the risk variables. In addition, we stratified the risk of NHL patients and estimated individual survival probability based on the RSF model.

[Experience in posterolateral mini-incision total hip arthroplasty].

OBJECTIVE: To explore the surgical techniques in posterolateral mini-incision total hip arthroplasty and clinical efficacy. METHODS: A total of 94 patients (98 hips) had posterolateral mini-incision total hip arthroplasty, from December 2006 to December 2008, and were randomly divided into a posterolateral miniincision total hip arthroplasty group (Group A) and a conventional group of posterolateral incision for total hip arthroplasty (Group B). Forty-nine patients (50 hips) in Group A had posterolateral small incision total hip arthroplasty [26 males, 23 females, between 37.0 and 95.0 years (average 68.9 years), body mass index (BMI) between 20.3 and 29.7 (average BMI 25.4)]. Forty-five patients (48 hips) in Group B had the conventional posterolateral incision [27 males, 18 females, between 45.0 and 92.0 years (average 69.7 years), BMI between 18.7 and 34.1 (average BMI 26.9)]. The incision length, blood loss, drainage, operation time, and postoperative Harris scores were compared between the 2 groups. RESULTS: The incision length was 6.0~10.5 (average length 7.4) cm, average blood loss was 387.6 (140.0~1000.0) mL, average drainage was 143.1 (63.0~375.0) mL, average blood transfusion was 77.6 (0~400.0) mL, average operation time was 84.6 (63.0~130.0) min, and vitalock abduction angle imaging after the operation was 41.6° (averagely 38.0°~57.0°) in Group A. The incision length was 15.0~23.0 (average length 20.0) cm, average blood loss was 513.1 (210.0~1350.0) mL, average drainage was 152.3 (70.0~520.0) mL, average blood transfusion was 142.2 (0~800.0) mL, average operation time was 84.0 (71.0~115.0) min, and postoperative radiographic acetabular cup abduction angle was 42.3° (37.0°~54.0°) in Group B. The follow-up showed that the prosthesis of the 2 groups was in a good position, Harris score was significantly improved with no complications after total hip arthroplasty in the 2 groups. CONCLUSION: With the correct selection of indications and adept operation skills and other circumstances, posterolateral mini-incision total hip arthroplasty may do little damage to the soft tissue with a small scar on the skin. The imaging evaluation after the operation and the Harris score show no difference between the 2 groups. Patients are satisfied with their joint function and postoperative recovery.

Surgical management of multiple knee ligament injuries.

OBJECTIVES: To summarize our experience and mid-term results of reconstruction with Iliotibial tract grafts for multiple ligament injuries. METHODS: Between July 1997 and December 2003, multiple ligament injuries of 15 patients were reconstructed with Iliotibial tract grafts in arthroscopy. There were 5 women and 10 men. The mean age at the time of the surgery was 30.5 years (range 25-43 years). There were 7 cases who were injured with combined ACL rupture and the PCL, and 8 cases were with disruption of both the ACL and the PCL, combined with damage of the medial collateral ligament. RESULTS: Fifteen patients were followed up for a mean of 7.5 years (range 6-12 years). The overall mean postoperative Lysholm score was 84.3 ± 5.7. At final IKDC qualification, 60.0 % of the knees were normal or nearly normal. The overall average Tegner activity score decreased significantly at the re-examination compared to the activity score before accident (3.6 ± 0.5 vs. 5.1 ± 0.6). CONCLUSIONS: Reconstruction with Iliotibial tract grafts in arthroscopy was a reliable treatment for multiple ligament injuries.

[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.

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 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.

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.