Helical sulfonyl-γ-AApeptides modulating Aß oligomerization and cytotoxicity by recognizing Aß helix.

In contrast to prevalent strategies which make use of ß-sheet mimetics to block Aß fibrillar growth, in this study, we designed a series of sulfonyl-γ-AApeptide helices that targeted the crucial α-helix domain of Aß13-26 and stabilized Aß conformation to avoid forming the neurotoxic Aß oligomeric ß-sheets. Biophysical assays such as amyloid kinetics and TEM demonstrated that the Aß oligomerization and fibrillation could be greatly prevented and even reversed in the presence of sulfonyl-γ-AApeptides in a sequence-specific and dose-dependent manner. The studies based on circular dichroism, Two-dimensional nuclear magnetic resonance spectroscopy (2D-NMR) spectra unambiguously suggested that the sulfonyl-γ-AApeptide Ab-6 could bind to the central region of Aß42 and induce α-helix conformation in Aß. Additionally, Electrospray ionisation-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) was employed to rule out a colloidal mechanism of inhibitor and clearly supported the capability of Ab-6 for inhibiting the formation of Aß aggregated forms. Furthermore, Ab-6 could rescue neuroblastoma cells by eradicating Aß-mediated cytotoxicity even in the presence of pre-formed Aß aggregates. The confocal microscopy demonstrated that Ab-6 could still specifically bind Aß42 and colocalize into mitochondria in the cellular environment, suggesting the rescue of cell viability might be due to the protection of mitochondrial function otherwise impaired by Aß42 aggregation. Taken together, our studies indicated that sulfonyl-γ-AApeptides as helical peptidomimetics could direct Aß into the off-pathway helical secondary structure, thereby preventing the formation of Aß oligomerization, fibrillation and rescuing Aß induced cell cytotoxicity.

Dynamic Approach to Synthetic Lectin for Glucose with Boosted Binding Affinity through C-H Hydrogen Bonds.

A biomimetic receptor for glucose has been developed with high affinity and selectivity. The receptor was efficiently synthesized in three steps through dynamic imine chemistry followed by imine-to-amide oxidation. The receptor features two parallel durene panels, forming a hydrophobic pocket for [CH⋅⋅⋅π] interactions, and two pyridinium residues directing four amide bonds towards the pocket. These pyridinium residues not only improve solubility but also provide polarized C-H bonds for hydrogen bonding. Experimental data and DFT calculations show that these polarized C-H bonds significantly enhance substrate binding. These findings demonstrate the power of dynamic covalent chemistry for creating molecular receptors and using polarized C-H bonds for boosted carbohydrate recognition in water, providing a foundation for developing glucose-responsive materials and sensors.

Mini-open compared with the trans-tubular approach in patients with spinal metastases underwent decompression surgery---a retrospective cohort study.

BACKGROUND: This study aimed to evaluate the perioperative safety and efficacy of the Mini-open and trans-tubular approach in patients with spinal metastases who underwent decompression surgery. METHODS: 37 consecutive patients with spinal metastases who underwent decompression surgery through a Mini-open or trans-tubular approach were retrospectively reviewed between June 2017 and June 2022. Thirty-four patients were included in this study. 19 underwent decompression surgery through the Mini-open approach, and 15 underwent the Trans-tubular approach. T-test and chi-square test were used to evaluate the difference between baseline data and primary and secondary outcomes. RESULTS: Baseline characteristics did not differ significantly between Trans-tubular and Mini-open groups except for the Ambulatory status (P < 0.001). There was no significant difference in blood loss between the two groups (P = 0.061). Operative time, intraoperative blood transfusion, intraoperative complication (dural tear), and postoperative hospitalization were comparable in the two groups (P > 0.05). The trans-tubular group had significantly less amount of postoperative drainage (133.5 ± 30.9 ml vs. 364.5 ± 64.2 ml, p = 0.003), and the time of drainage (3.1 ± 0.2 days vs. 4.6 ± 0.5 days, p = 0.019) compared with Mini-open group (P < 0.05). Sub-group analysis showed that for patients with hypo-vascular tumors, the Trans-tubular group had significantly less blood loss than the Mini-open group (951.1 ± 171.7 ml vs. 1599.1 ± 105.7 ml, P = 0.026). CONCLUSIONS: Decompression through Mini-open or Trans-tubular was safe and effective for patients with spinal metastases. The trans-tubular approach might be more suitable for patients with hypo-vascular tumors.

Side Group of Hydrophobic Amino Acids Controls Chiral Discrimination among Chiral Counterions and Metal-Organic Cages.

The self-assembly of chiral Pd12L24 metal-organic cages (MOCs) based on hydrophobic amino acids, including alanine (Ala), valine (Val), and leucine (Leu), into single-layered hollow spherical blackberry-type structures is triggered by nitrates through counterion-mediated attraction. In addition to nitrates, anionic N-(tert-butoxycarbonyl) (Boc)-protected Ala, Val, and Leu were used as chiral counterions during the self-assembly of d-MOCs. Previously, we showed that l-Ala suppresses the self-assembly process of d-Pd12Ala24 but has no effect on l-Pd12Ala24, i.e., chiral discrimination. Here, we indicate when the amino acid used as the chiral counterion has a bulkier side group than the amino acid in the MOC structure, no chiral discrimination exists; otherwise, chiral discrimination exists. For example, Ala can induce chiral discrimination in all chiral MOCs, whereas Leu can induce chiral discrimination only in Pd12Leu24. Moreover, chiral anionic d- and l-alanine-based surfactants have no chiral discrimination, indicating that bulkier chiral counterions with more hydropohobic side groups can erase chiral discrimination.

Risk factors for predicting cement leakage in percutaneous vertebroplasty for spinal metastases.

BACKGROUND: The study aimed to identify the risk factors of cement leakage following percutaneous vertebroplasty for spinal metastases. METHODS: 230 consecutive patients with 530 vertebrae were retrospectively reviewed. Characteristics including age, primary cancer, location, pathological fracture, the integrity of the posterior wall, and the volume of bone cement were considered as potential risk factors. Cement leakage was evaluated by postoperative imaging examination and classified into three subtypes with different potential sequelae: spinal canal leakage, intravascular leakage around vertebrae, intradiscal and paravertebral leakage. Univariate and multivariate analyses were used to assess the risk factors. RESULTS: Leakage was detected in 185 vertebrae (34.9%), 18.3% for intradiscal and paravertebral, 13.2% for intravascular around vertebrae, and 7.0% for spinal canal. Multivariate analysis showed that incomplete posterior wall (P = 0.001) and breast cancer (P = 0.015) were strong predictive factors for spinal canal leakage, incomplete posterior wall (P = 0.024) was for intravascular leakage around vertebrae, thoracic (P = 0.010) and pathological fracture (P = 0.000) were for intradiscal and paravertebral leakage. CONCLUSIONS: Our findings suggest that cement leakage is common following percutaneous vertebroplasty for spinal metastases. The incomplete posterior wall is an unfavourable factor for intravascular leakage around vertebrae. Vertebrae with incomplete posterior wall and breast cancer metastases are more likely to develop spinal canal leakage.

Prediction of early vascular cement leakage following percutaneous vertebroplasty in spine metastases: the Peking University First Hospital Score (PUFHS).

BACKGROUND: Cement leakage into venous blood posed significant challenge to surgeons. The aim of the study was to create a Peking University First Hospital Score (PUFHS) which could evaluate the probability of vascular cement leakage among spine metastases patients following percutaneous vertebroplasty. METHODS: The study retrospectively enrolled 272 spine metastases patients treated with percutaneous vertebroplasty. We randomly extracted all enrolled patients as the training or validation group and baseline characteristic comparison was assessed between the two groups. Creation of the PUFHS was performed in the training group and validation of the PUFHS was performed in the validation group. RESULTS: Of all the 272 patients, the total number of included vertebrae was 632 and the median treated levels were 2 per patient. Vascular cement leakage occurred in 26.47% (72/272) of patients. The baseline characteristics were comparable between the two groups (P > 0.05). Three risk predictors (primary cancer types, number of treated vertebrae levels, and vertebrae collapse) were included in the PUFHS. The area under the receiver operating characteristic curve (AUROC) of the PUFHS was 0.71 in the training group and 0.69 in the validation group. The corresponding correct classification rates were 73.0 and 70.1%, respectively. The calibration slope was 0.78 (95% confidence interval[CI]: 0.45-1.10) in the training group and 1.10 (95% CI: 0.73-1.46) in the validation group. The corresponding intercepts were 0.06 (95% CI: - 0.04-0.17) and - 0.0079 (95% CI: - 0.11-0.092), respectively. CONCLUSIONS: Vascular cement leakage is common among spine metastases after percutaneous vertebroplasty. The PUFHS can calculate the probability of vascular cement leakage, which can be a useful tool to inform surgeons about vascular cement leakage risk in advance.

Risk factors of total blood loss in the posterior surgery for patients with thoracolumbar metastasis.

BACKGROUND: Blood loss in posterior surgery patients with thoracolumbar metastasis posed a significant challenge to surgeons. This study aimed to explore the risk factors of blood loss in posterior surgery for patients with thoracolumbar metastasis. METHODS: One hundred forty-two patients were retrospectively reviewed. Their baseline characteristics were recorded. The Gross equation was used to calculate blood loss on a surgical day. Multivariate linear regression was used to analyze the risk factors. RESULTS: Mean blood loss of 142 patients were 2055 ± 94 ml. Hypervascular primary tumor (kidney, thyroid and liver) (P = 0.017), wide or marginal excision (en-bloc: P = 0.001), metastasis at the lumbar spine (P = 0.033), and the presence of extraosseous tumor mass (P = 0.012) were independent risk factors of blood loss in the posterior surgery. Sub-analysis showed that wide or marginal excision (en-bloc: P < 0.001) and metastasis at lumbar spine (P = 0.007) were associated with blood loss for patients with non-hyper vascular primary tumors. Wide or marginal excision (piece-meal: P = 0.014) and the presence of an extraosseous tumor mass (P = 0.034) were associated with blood loss for patients with hypervascular primary tumors. CONCLUSION: Hypervascular primary tumor (kidney, thyroid, and liver) was an independent risk factor of blood loss in the posterior surgery. The presence of extraosseous tumor mass and wide or marginal excision (piece-meal) were independent risk factors for patients with hypervascular primary tumors. Metastasis at the lumbar spine and wide or marginal excision (en-bloc) were independent risk factors for patients with non-hyper vascular primary tumors.

Ring-in-Ring(s) Complexes Exhibiting Tunable Multicolor Photoluminescence.

One ring threaded by two other rings to form a non-intertwined ternary ring-in-rings motif is a challenging task in noncovalent synthesis. Constructing multicolor photoluminescence systems with tunable properties is also a fundamental research goal, which can lead to applications in multidimensional biological imaging, visual displays, and encryption materials. Herein, we describe the design and synthesis of binary and ternary ring-in-ring(s) complexes, based on an extended tetracationic cyclophane and cucurbit[8]uril. The formation of these complexes is accompanied by tunable multicolor fluorescence outputs. On mixing equimolar amounts of the cyclophane and cucurbit[8]uril, a 1:1 ring-in-ring complex is formed as a result of hydrophobic interactions associated with a favorable change in entropy. With the addition of another equivalent of cucurbit[8]uril, a 1:2 ring-in-rings complex is formed, facilitated by additional ion-dipole interactions involving the pyridinium units in the cyclophane and the carbonyl groups in cucurbit[8]uril. Because of the narrowing in the energy gaps of the cyclophane within the rigid hydrophobic cavities of cucurbit[8]urils, the binary and ternary ring-in-ring(s) complexes emit green and bright yellow fluorescence, respectively. A series of color-tunable emissions, such as sky blue, cyan, green, and yellow with increased fluorescence lifetimes, can be achieved by simply adding cucurbit[8]uril to an aqueous solution of the cyclophane. Notably, the smaller cyclobis(paraquat-p-phenylene), which contains the same p-xylylene linkers as the extended tetracationic cyclophane, does not form ring-in-ring(s) complexes with cucurbit[8]uril. The encapsulation of this extended tetracationic cyclophane by both one and two cucurbit[8]urils provides an incentive to design and synthesize more advanced supramolecular systems, as well as opening up a feasible approach toward achieving tunable multicolor photoluminescence with single chromophores.

The endoplasmic reticulum stress induced by tunicamycin affects the viability and autophagy activity of chondrocytes.

Osteoarthritis (OA) is attributed to a reduction in chondrocytes within joint cartilage, and research has shown that endoplasmic reticulum (ER) stress and autophagy play important roles in the survival of chondrocytes. However, the relationship between ER stress and autophagy in chondrocytes remains unclear. In this study, we investigated the changes in apoptotic and autophagic activity in chondrocytes under ER stress. Following treatment with tunicamycin, the rate of apoptosis among chondrocytes increased. Western blot analysis showed the levels of unfolded protein response (UPR) related proteins increased, followed by elevated expression of light chain 3B-II (LC3B-II) and Beclin-1. An ultrastructural investigation showed that a large number of pre-autophagosomal structures or autophagosomes formed under tunicamycin treatment. However, the autophagy activity was significantly inhibited in chondrocytes after suppression of GRP78 by siRNA. The apoptosis ratio of chondrocytes pre-treated with 3-methyladenine was much higher than that of normal chondrocytes after exposure to tunicamycin. Our study revealed that the tunicamycin-induced persistent UPR expression led to apoptosis of chondrocytes and activation of autophagy incorporation with GRP78. Blocking autophagy accelerated the apoptosis induced by ER stress, which confirmed the protective function of autophagy in the homeostasis of chondrocytes. These findings advance our understanding of chondrocyte apoptosis and provide potential molecular targets for preventing apoptotic death of chondrocytes.

Spherical Supramolecular Structures Constructed via Chemically Symmetric Perylene Bisimides: Beyond Columnar Assembly.

Like other discotic molecules, self-assembled supramolecular structures of perylene bisimides (PBIs) are commonly limited to columnar or lamellar structures due to their distinct π-conjugated scaffolds and unique rectangular shape of perylene cores. The discovery of PBIs with supramolecular structures beyond layers and columns may expand the scope of PBI-based materials. A series of unconventional spherical packing phases in PBIs, including A15 phase, σ phase, dodecagonal quasicrystalline (DQC) phase, and body-centered cubic (BCC) phase, is reported. A strategy involving functionalization of perylene core with several polyhedral oligomeric silsesquioxane (POSS) cages achieved spherical assemblies of PBIs, instead of columnar assemblies, due to the significantly increased steric hindrance at the periphery. This strategy may also be employed for the discovery of unconventional spherical assemblies in other related discotic molecules by the introduction of similar bulky functional groups at their periphery. An unusual inverse phase transition sequence from a BCC phase to a σ phase was observed by increasing annealing temperature.

Regulation Mechanism of MYC Family Transcription Factors in Jasmonic Acid Signalling Pathway on Taxol Biosynthesis.

Paclitaxel is an important anticancer drug. The phytohormone jasmonic acid can significantly induce the biosynthesis of paclitaxel in Taxus, but the molecular mechanism has not yet been resolved. To establish the jasmonic acid signalling pathway of Taxus media, based on the gene of the jasmonic acid signalling pathway of Arabidopsis thaliana, sequence analysis was performed to isolate the jasmonic acid signal from the transcriptome, a transcriptional cluster of pathway gene homologs and the full length of 22 genes were obtained by RACE PCR at 5' and 3': two EI ubiquitin ligase genes, COI1-1 and COI1-2;7 MYC bHLH type transcription factor (MYC2, MYC3, MYC4, JAM1, JAM2, EGL3, TT8); 12 JAZ genes containing the ZIM domain; and MED25, one of the components of the transcriptional complex. The protein interaction between each were confirmed by yeast two hybridization and bimolecular fluorescence complementation based on similar genes interaction in Arabidopsis. A similar jasmonate signaling pathway was illustrated in T. media. All known paclitaxel biosynthesis genes promoters were isolated by genome walker PCR. To investigate the jasmonate signaling effect on these genes' expression, the transcription activity of MYC2, MYC3 and MYC4 on these promoters were examined. There are 12, 10 and 11 paclitaxel biosynthesis genes promoters that could be activated by MYC2, MYC3 and MYC4.

[Effect of mitochondrial dysfunction of chondrocyte on cartilage degeneration].

OBJECTIVE: To evaluate the effect of chondrocyte mitochondrial dysfunction on the development of cartilage degeneration. METHODS: In the study, 10 cartilage samples of the knee joint were collected during total knee arthroplasty surgery because of OA from April to October of 2012 in Peking University First Hospital. All the tissues were taken from transmission electron microscope (TEM) observation grouped by Outerbridge classification. Then, TEM observation, quantitative detection of mitochondrial respiratory chain enzyme complex 1,2,2+3,4 and ATPase activity, detection of the mitochondrial membrane potential by JC-1 method were taken with cultured normal and OA chondrocytes. Healthy chondrocytes from 10 normal cartilage samples were divided into 2 groups: the normal control group and rotenone group. The ultrastuctrure alterations of mitochondria, mitochondrial membrane potential, apoptosis rate and collagen II content were compared. RESULTS: With the aggravation of cartilage degeneration, mitochondria swelling, outer membrane rupture, cristae destruction and disappearance were observed in both the tissue and cell TEM examinations. JC-1 staining showed a decreased membrane potential in OA chondrocytes which had a lower red/green fluorescence ratio of 1.50 than that of the normal chondrocytes of 2.58. mitochondrial respiratory chain (MRC) enzyme complex 1,2,2+3,4 and ATPase activity of the OA chondrocytes also represented a decreased tendency compared with the normal chondrocytes although the difference was not significant (P=0.109,0.197,0.098,0.169,0.145). The mitochondria in the Ro group cells showed OA-like changes morphologically by TEM detection. JC-1 staining showed a decreased mitochondrial membrane potential in the Ro group chondrocytes which had a lower red/green fluorescence ratio of 1.78 than that of the normal ones of 2.58. Apoptosis examination represented a higher apoptosis rate of 7.53% in the Ro group chondrocytes than that of the normal ones of 4.38%. Collagen II content of the chondrocytes in the Ro group was (44.63 ± 7.11) µg/L , significantly lower than (72.88 ± 24.3) µg/L in the control group (P=0.044). CONCLUSION: Mitochondrial function is impaired in OA chondrocyte. Mitochondrial function destruction results in an increased chondrocyte apoptosis rate and a decreased collagen II secretion.

Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques.

BACKGROUND CONTEXT: Intraoperative blood loss is a significant concern in patients with metastatic spinal disease. Early identification of patients at high risk of experiencing massive intraoperative blood loss is crucial as it allows for the development of appropriate surgical plans and facilitates timely interventions. However, accurate prediction of intraoperative blood loss remains limited based on prior studies. PURPOSE: The purpose of this study was to develop and validate a web-based artificial intelligence (AI) model to predict massive intraoperative blood loss during surgery for metastatic spinal disease. STUDY DESIGN/SETTING: An observational cohort study. PATIENT SAMPLE: Two hundred seventy-six patients with metastatic spinal tumors undergoing decompressive surgery from two hospitals were included for analysis. Of these, 200 patients were assigned to the derivation cohort for model development and internal validation, while the remaining 76 were allocated to the external validation cohort. OUTCOME MEASURES: The primary outcome was massive intraoperative blood loss defined as an estimated blood loss of 2,500 cc or more. METHODS: Data on patients' demographics, tumor conditions, oncological therapies, surgical strategies, and laboratory examinations were collected in the derivation cohort. SMOTETomek resampling (which is a combination of Synthetic Minority Oversampling Technique and Tomek Links Undersampling) was performed to balance the classes of the dataset and obtain an expanded dataset. The patients were randomly divided into two groups in a proportion of 7:3, with the most used for model development and the remaining for internal validation. External validation was performed in another cohort of 76 patients with metastatic spinal tumors undergoing decompressive surgery from a teaching hospital. The logistic regression (LR) model, and five machine learning models, including K-Nearest Neighbor (KNN), Decision Tree (DT), XGBoosting Machine (XGBM), Random Forest (RF), and Support Vector Machine (SVM), were used to develop prediction models. Model prediction performance was evaluated using area under the curve (AUC), recall, specificity, F1 score, Brier score, and log loss. A scoring system incorporating 10 evaluation metrics was developed to comprehensively evaluate the prediction performance. RESULTS: The incidence of massive intraoperative blood loss was 23.50% (47/200). The model features were comprised of five clinical variables, including tumor type, smoking status, Eastern Cooperative Oncology Group (ECOG) score, surgical process, and preoperative platelet level. The XGBM model performed the best in AUC (0.857 [95% CI: 0.827, 0.877]), accuracy (0.771), recall (0.854), F1 score (0.787), Brier score (0.150), and log loss (0.461), and the RF model ranked second in AUC (0.826 [95% CI: 0.793, 0.861]) and precise (0.705), whereas the AUC of the LR model was only 0.710 (95% CI: 0.665, 0.771), the accuracy was 0.627, the recall was 0.610, and the F1 score was 0.617. According to the scoring system, the XGBM model obtained the highest total score of 55, which signifies the best predictive performance among the evaluated models. External validation showed that the AUC of the XGBM model was also up to 0.809 (95% CI: 0.778, 0.860) and the accuracy was 0.733. The XGBM model, was further deployed online, and can be freely accessed at https://starxueshu-massivebloodloss-main-iudy71.streamlit.app/. CONCLUSIONS: The XGBM model may be a useful AI tool to assess the risk of intraoperative blood loss in patients with metastatic spinal disease undergoing decompressive surgery.

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accurate prediction models. METHODS: This study collected 455 patients with metastatic spinal disease who underwent posterior decompressive surgery at three tertiary medical institutions. Of these, 220 patients were collected from one medical institution to form the model derivation cohort, while 89 and 146 patients were collected from two other medical institutions to form the external validation cohorts 1 and 2, respectively. Patients in the model derivation cohort were used to develop and internally validate models. To establish the interactive AI platform, machine learning techniques were used to develop prediction models, including logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), and neural network (NN). Furthermore, to enhance the resilience of the study's model, an ensemble machine learning approach was employed using a soft-voting method by combining the results of the above six algorithms. A scoring system incorporating 10 evaluation metrics was used to comprehensively assess the prediction performance of the developed models. The scoring system had a total score of 0 to 60, with higher scores denoting better prediction performance. An interactive AI platform was further deployed via Streamlit. The prediction performance was compared between medical experts and the AI platform in assessing the risk of experiencing postoperative inability to walk among patients with metastatic spinal disease. RESULTS: Among all developed models, the ensemble model outperformed the six other models with the highest score of 57, followed by the eXGBM model (54), SVM model (50), and NN model (50). The ensemble model had the best performance in accuracy and calibration slope, and the second-best performance in precise, recall, specificity, area under the curve (AUC), Brier score, and log loss. The scores of the LR model, RF model, and DT model were 39, 46, and 26, respectively. External validation demonstrated that the ensemble model had an AUC value of 0.873 (95% CI: 0.809-0.936) in the external validation cohort 1 and 0.924 (95% CI: 0.890-0.959) in the external validation cohort 2. In the new ensemble machine learning model excluding the feature of the number of comorbidities, the AUC value was still as high as 0.916 (95% CI: 0.863-0.969). In addition, the AUC values of the new model were 0.880 (95% CI: 0.819-0.940) in the external validation cohort 1 and 0.922 (95% CI: 0.887-0.958) in the external validation cohort 2, indicating favorable generalization of the model. The interactive AI platform was further deployed online based on the final machine learning model, and it was available at https://postoperativeambulatory-izpdr6gsxxwhitr8fubutd.streamlit.app/ . By using the AI platform, researchers were able to obtain the individual predicted risk of postoperative inability to walk, gain insights into the key factors influencing the outcome, and find the stratified therapeutic recommendations. The AUC value obtained from the AI platform was significantly higher than the average AUC value achieved by the medical experts ( P <0.001), denoting that the AI platform obviously outperformed the individual medical experts. CONCLUSIONS: The study successfully develops and validates an interactive AI platform for evaluating the risk of postoperative loss of ambulatory ability in patients with metastatic spinal disease. This AI platform has the potential to serve as a valuable model for guiding healthcare professionals in implementing surgical plans and ultimately enhancing patient outcomes.

Peking University First Hospital Procedure for Culturing Pathogenic Microorganisms for Bone and Joint Infection.

Background: This retrospective cohort study explores a practical approach to acquiring pathogenic microorganisms in patients with bone and joint infections. Methods: From Aug 2018 to Mar 2022, 68 consecutive patients (87 cultures) with bone and joint infection were recruited in this study. All cultures followed the Peking University First Hospital Procedure of Culturing Pathogenic microorganisms for bone and joint infection. Tissue samples were obtained through fluoroscopy-guided biopsy or open debridement. Tissue samples were divided into manual homogenization (MH), manual mixture (MM), and pathological examination. The baseline, antibiotic exposure, laboratory, surgical, and microbial data were reviewed. Independent sample T-test, Mann-Whitney U-test, and Chi-square test were used to detect the difference between patients who received different processing measures. Results: The average age was 55.8±2.4 years old. Thirty-nine patients were male. The total positive culture rate of the manual homogenization group was 80.5% (70/87). Thirty-five patients had mixed infections with more than one microorganism cultured. Staphylococci accounted for 60.23% of all microorganisms. Staphylococcus aureus (18.2%) and Staphylococcus epidermidis (15.9%) were the two most common bacteria cultured in this study. Patients with positive culture in the manual mixture group had significantly higher WBC (p = 0.006), NE% (p = 0.024), ESR (p = 0.003), CRP (p = 0.020) and IL6 (0.050) compared to patients with negative culture. After tissue homogenization, only ESR is still statistically different. Patients without SIRS had a low positive culture rate (59.4%). Tissue homogenization could significantly increase the positive culture rate of patients without SIRS. Pre-culture antibiotic exposure was not an independent risk factor for culture results. Conclusion: Peking University First Hospital Procedure for Culturing Pathogenic microorganisms for Bone and Joint Infections was a practical approach for obtaining pathogenic microorganisms.

Manual Homogenization Improves the Sensitivity of Microbiological Culture for Patients with Pyogenic Spondylitis.

Objective: This study aimed to investigate the effects of manual homogenization on the sensitivity of microbiological culture for patients with pyogenic spondylitis. Methods: From October 2018 to March 2021, patients undergoing fluoroscopy-guided biopsy or open debridement due to pyogenic spondylitis were recruited. Their demographic data and baseline characteristics were recorded. Tissue samples were obtained through fluoroscopy-guided biopsy or open debridement. Tissue samples were divided into three parts: manual homogenization (MH), manual mixture (MM), and pathological examination. Sterile normal saline was set as the negative control to exclude false-positive culture results. The Chi-square test was used to detect the difference of microbiological culture results. Results: Twenty-four consecutive patients (33 tissue cultures) with pyogenic spondylitis treated in our department between October 2018 and March 2021 were recruited in this study. The average age was 61.7±3.2 years old and 10 patients were female. The MH group had a significantly higher positive rate compared with the MM group in aerobic conditions: 78.8% (26 isolates) vs 54.5% (18 isolates), P=0.037 and anaerobic condition: 63.6% (21 isolates) vs 39.4% (13 isolates), P=0.049. The results of subgroup analyses showed that MH could improve the culture sensitivity for patients with previous antibiotics use and without paravertebral abscesses but not reach a significant level on statistics. Conclusion: Based on the present study, manual homogenization could improve the sensitivity of microbiological cultures for patients with pyogenic spondylitis.

Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques.

Background: Individualized therapeutic strategies can be carried out under the guidance of expected lifespan, hence survival prediction is important. Nonetheless, reliable survival estimation in individuals with bone metastases from cancer of unknown primary (CUP) is still scarce. The objective of the study is to construct a model as well as a web-based calculator to predict three-month mortality among bone metastasis patients with CUP using machine learning-based techniques. Methods: This study enrolled 1010 patients from a large oncological database, the Surveillance, Epidemiology, and End Results (SEER) database, in the United States between 2010 and 2018. The entire patient population was classified into two cohorts at random: a training cohort (n=600, 60%) and a validation cohort (410, 40%). Patients from the validation cohort were used to validate models after they had been developed using the four machine learning approaches of random forest, gradient boosting machine, decision tree, and eXGBoosting machine on patients from the training cohort. In addition, 101 patients from two large teaching hospital were served as an external validation cohort. To evaluate each model's ability to predict the outcome, prediction measures such as area under the receiver operating characteristic (AUROC) curves, accuracy, and Youden index were generated. The study's risk stratification was done using the best cut-off value. The Streamlit software was used to establish a web-based calculator. Results: The three-month mortality was 72.38% (731/1010) in the entire cohort. The multivariate analysis revealed that older age (P=0.031), lung metastasis (P=0.012), and liver metastasis (P=0.008) were risk contributors for three-month mortality, while radiation (P=0.002) and chemotherapy (P<0.001) were protective factors. The random forest model showed the highest area under curve (AUC) value (0.796, 95% CI: 0.746-0.847), the second-highest precision (0.876) and accuracy (0.778), and the highest Youden index (1.486), in comparison to the other three machine learning approaches. The AUC value was 0.748 (95% CI: 0.653-0.843) and the accuracy was 0.745, according to the external validation cohort. Based on the random forest model, a web calculator was established: https://starxueshu-codeok-main-8jv2ws.streamlitapp.com/. When compared to patients in the low-risk groups, patients in the high-risk groups had a 1.99 times higher chance of dying within three months in the internal validation cohort and a 2.37 times higher chance in the external validation cohort (Both P<0.001). Conclusions: The random forest model has promising performance with favorable discrimination and calibration. This study suggests a web-based calculator based on the random forest model to estimate the three-month mortality among bone metastases from CUP, and it may be a helpful tool to direct clinical decision-making, inform patients about their prognosis, and facilitate therapeutic communication between patients and physicians.

Machine learning approaches for prediction of early death among lung cancer patients with bone metastases using routine clinical characteristics: An analysis of 19,887 patients.

Purpose: Bone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data. Methods: This study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold. Results: Among all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807-0.833), 0.820 (95% CI: 0.807-0.833), and 0.815 (95% CI: 0.801-0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001). Conclusions: Using machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

Epidemiology and detection of cement leakage in patients with spine metastases treated with percutaneous vertebroplasty: A 10-year observational study.

OBJECTIVES: To investigate the epidemiology of cement leaks and further develop an algorithm to detect the high risk of cement leaks among advanced cancer patients with metastatic spinal disease treated with percutaneous vertebroplasty. METHODS: This study retrospectively analyzed 309 patients with metastatic spinal disease treated with percutaneous vertebroplasty. Patients were randomly divided into a training group and a validation group. In the training group, 13 potential characteristics were analyzed for their abilities to predict cement leaks. Discal cement leakage and paravertebral cement leakage were excluded from the analysis. Those characteristics identified as having significant predictive value were used to develop a predictive algorithm. Internal validation of the algorithm was performed based on discrimination and calibration qualities. RESULTS: Overall, cement leaks occurred in 61.17% (189/309) patients. Among the 13 characteristics analyzed, younger age (P = 0.03), extravertebral bone metastases (P = 0.02), increased number of treated vertebrae levels (P < 0.01), and cortical osteolytic destruction in the posterior wall (P = 0.01) were included in the algorithm. This algorithm generates a score between 0 and 16 points, with higher scores indicating a higher risk of cement leakage. The area under the receiver operating characteristic curve (AUROC) value for the algorithm was 0.75 in the training group and 0.69 in the validation group. The mean correct classification rates for the training and validation groups were 73.5% and 64.9%, respectively, and the corresponding P-values of the goodness-of-fit test were 0.70 and 0.50. CONCLUSIONS: Cement leaks are common in patients with metastatic spinal disease treated with percutaneous vertebroplasty. The present study proposed and internally validated an algorithm that can be used to screen patients at high risk of cement leakage.

A Nomogram to Predict Intra-Spinal Canal Cement Leakage Among Elderly Patients with Spine Metastases: An Internal-Validated Model.

PURPOSE: This study aimed to assess the risk variables for predicting intra-spinal canal cement leakage, especially among elderly patients with spine metastases after being treated with percutaneous vertebroplasty (PVP). Furthermore, we proposed and validated a nomogram to stratify risks of intra-spinal canal cement leakage. METHODS: We retrospectively analyzed 163 elderly patients (age ≧65 years) with spine metastases who underwent PVP. Patients were randomly divided into a training cohort (n=100) and a validation cohort (n=63). The multivariate logistic regression analysis was used to screen potential risk variables in the training cohort. Significant risk variables were included in the nomogram, and the nomogram was developed according to the estimates of the each included variable. The predictive effectiveness of the nomogram was validated using discrimination and calibration performance. RESULTS: The overall prevalence of intra-spinal canal cement leakage was 9.82% (16/163). In the training cohort, female patients (14.71%, 5/34) showed a higher rate of intra-spinal canal cement leakage as compared with male patients (4.55%, 3/66). The nomogram consisted of sex, cortical osteolytic destruction in posterior wall, and load-bearing lines of spine. The nomogram had acceptable discrimination, with the area under the receiver operating characteristic (AUROC) of 0.75 in the training cohort, 0.64 in the validation cohort, and 0.69 in the entire cohort, and also showed favorable calibration based on the goodness-of-fit test. According to the nomogram, three risk groups were developed: the low risk group had an actual probability of 7.03%, the medium risk group was 11.54%, and high risk group was 44.44%. The difference between the three groups was significant (P ˂ 0.01). CONCLUSION: Intra-spinal canal cement leakage after PVP is not scarce among elderly patients. We proposed and internally validated a nomogram that is capable of calculating the risk of intra-spinal canal cement leakage among elderly patients with spine metastases. Careful surgical plan should be conducted among patients with a high risk of developing intra-spinal canal cement leakage.