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Small-scale and flexible acoustic probes are more desirable for exquisite objects like human bodies and complex-shaped components than conventional rigid ones. Herein, a thin-film flexible acoustic sensor (FA-TES) that can detect ultra-broadband acoustic signals in multiple applications is proposed. The device consists of two thin copper-coated polyvinyl chloride films, which are stimulated by acoustic waves and contact each other to generate the triboelectric signal. Interlocking nanocolumn arrays fabricated on the friction surfaces are regarded as a highly adaptive spacer enabling this device to respond to ultra-broadband acoustic signals (100 Hz-4 MHz) and enhance sensor sensitivity for film weak vibration. Benefiting from the characteristics of high shape adaptability and ultrawide response range, the FA-TES can precisely sense human physiological sounds and voice (≤10 kHz) for laryngeal health monitoring and interaction in real-time. Moreover, the FA-TES flexibly arranged on a 3D-printed vertebra model can effectively and accurately diagnose the inner defect by ultrasonic testing (≥1 MHz). It envisions that this work can provide new ideas for flexible acoustic sensor designs and optimize real-time acoustic detections of human bodies and complex components.
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Acústica , Ultrasonido , Humanos , Ultrasonografía , Sonido , FricciónRESUMEN
OBJECTIVE: Prediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC. METHODS: A total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model. RESULTS: In multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM. CONCLUSION: The proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.
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Neoplasias de los Conductos Biliares , Colangiocarcinoma , Humanos , Metástasis Linfática , Modelos Estadísticos , Pronóstico , Aprendizaje Automático , Conductos Biliares IntrahepáticosRESUMEN
Carboxylesterase 1 (CES1), a member of the serine hydrolase superfamily, is involved in a wide range of xenobiotic and endogenous substances metabolic reactions in mammals. The inhibition of CES1 could not only alter the metabolism and disposition of related drugs, but also be benefit for treatment of metabolic disorders, such as obesity and fatty liver disease. In the present study, we aim to develop potential inhibitors of CES1 and reveal the preferred inhibitor structure from a series of synthetic pyrazolones (compounds 1-27). By in vitro high-throughput screening method, we found compounds 25 and 27 had non-competitive inhibition on CES1-mediated N-alkylated d-luciferin methyl ester (NLMe) hydrolysis, while compound 26 competitively inhibited CES1-mediated NLMe hydrolysis. Additionally, Compounds 25, 26 and 27 can inhibit CES1-mediated fluorescent probe hydrolysis in live HepG2 cells with effect. Besides, compounds 25, 26 and 27 could effectively inhibit the accumulation of lipid droplets in mouse adipocytes cells. These data not only provided study basis for the design of newly CES1 inhibitors. The present study not only provided the basis for the development of lead compounds for novel CES1 inhibitors with better performance, but also offered a new direction for the explore of candidate compounds for the treatment of hyperlipidemia and related diseases.
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Adipocitos , Hidrolasas de Éster Carboxílico , Inhibidores Enzimáticos , Pirazolonas , Humanos , Hidrolasas de Éster Carboxílico/metabolismo , Hidrolasas de Éster Carboxílico/antagonistas & inhibidores , Adipocitos/efectos de los fármacos , Adipocitos/metabolismo , Adipocitos/citología , Animales , Ratones , Pirazolonas/farmacología , Pirazolonas/química , Pirazolonas/síntesis química , Relación Estructura-Actividad , Inhibidores Enzimáticos/farmacología , Inhibidores Enzimáticos/química , Inhibidores Enzimáticos/síntesis química , Estructura Molecular , Células Hep G2 , Diferenciación Celular/efectos de los fármacos , Relación Dosis-Respuesta a Droga , Células 3T3-L1RESUMEN
PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS: Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS: The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.
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Aprendizaje Automático , Fracturas Osteoporóticas , Fracturas de la Columna Vertebral , Vertebroplastia , Humanos , Estudios Retrospectivos , Fracturas Osteoporóticas/cirugía , Fracturas Osteoporóticas/etiología , Fracturas Osteoporóticas/diagnóstico , Femenino , Anciano , Masculino , Fracturas de la Columna Vertebral/cirugía , Fracturas de la Columna Vertebral/etiología , Fracturas de la Columna Vertebral/diagnóstico , Medición de Riesgo , Vertebroplastia/métodos , Persona de Mediana Edad , Internet , Fracturas por Compresión/cirugía , Fracturas por Compresión/etiología , Anciano de 80 o más AñosRESUMEN
PURPOSE: The purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions. METHODS: Patients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model. RESULTS: A total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656). CONCLUSION: Age, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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Infección Hospitalaria , Traumatismos de la Médula Espinal , Humanos , Infección Hospitalaria/diagnóstico , Infección Hospitalaria/epidemiología , Aprendizaje Automático , Traumatismos de la Médula Espinal/complicaciones , Traumatismos de la Médula Espinal/cirugía , Traumatismos de la Médula Espinal/diagnóstico , Factores de Riesgo , Curva ROCRESUMEN
Little is known about the prophages in Hafniaceae bacteria. A novel Hafnia phage, yong2, was induced from Hafnia paralvei by treatment with mitomycin C. The phage has an elliptical head with dimensions of approximately 45 × 38 nm and a long noncontractile tail of approximately 157 × 4 nm. The complete genome of Hafnia phage yong2 is a 39,546-bp double-stranded DNA with a G+C content of 49.9%, containing 59 open reading frames (ORFs) and having at least one fixed terminus (GGGGCAGCGACA). In phylogenetic analysis, Hafnia phage yong2 clustered with four predicted Hafnia prophages and one predicted Enterobacteriaceae prophage. These prophages and members of the family Drexlerviridae together formed two distinct subclades nested within a clade, suggesting the existence of a novel class of prophages with conserved sequences and a unique evolutionary status not yet studied before in Hafniaceae and Enterobacteriaceae bacteria.
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Bacteriófagos , Hafnia , Bacteriófagos/genética , Genoma Viral , Genómica , Hafnia/virología , Sistemas de Lectura Abierta , Filogenia , Profagos/genéticaRESUMEN
The freshwater cyanophage Mwe-Yong1112-1 was isolated using Microcystis wesenbergii as a host and found to have an icosahedral head, about 45 nm in diameter, and a flexible tail, approximately 133 nm in length and 4.5 nm in width. The complete genome of the cyanophage is 39,679 bp in length with a G+C content of 66.6%. Mwe-Yong1112-1 shared the highest pairwise average nucleotide identity (ANI) value of 67.7% (below the ≥95% boundary to define a species) and the highest nucleotide sequence similarity of 17.48% (below the >70% boundary to define a genus) with the most closely related phage. In a proteomic tree, Mwe-Yong1112-1 and three unclassified phages formed a monophyletic clade between the families Saparoviridae and Pyrstoviridae, but Mwe-Yong1112-1 occupied a separate branch from the other three phages, suggesting that it represents a new evolutionary lineage. This study enriches the available information about freshwater cyanophages.
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Bacteriófagos , Microcystis , Bacteriófagos/genética , Agua Dulce , Genoma Viral , Humanos , Microcystis/genética , Nucleótidos , Filogenia , ProteómicaRESUMEN
Chemical epigenetic regulation (CER) is an effective method to activate the silent pathway of fungal secondary metabolite synthesis. However, conventional methods for CER study are laborious and time-consuming. In the meantime, the overall profile of the secondary metabolites in the fungi treated by the CER reagent is not well characterized. In this study, suberohydroxamic acid (SBHA), a histone deacetylase inhibitor, was added to a culture of Aspergillus aculeatus DL1011 and a new strategy based on LC-MS/MS analysis integrated with various metabolomic tools (MetaboAnalyst, MS-DIAL, SIRIUS and GNPS) was developed to characterize the profile of induced metabolites. As a result, 13.6%, 29.5% and 27.2% of metabolites were identified as newly biosynthesized, increasing and decreasing in abundance by CER, respectively. The structures of the 18 newly induced secondary metabolites were further identified by the new strategy to demonstrate that 72.2% of them (1 novel compound and 12 known compounds) were first discovered in A. aculeatus upon SBHA treatment. The accuracy of the new approach was confirmed by purification and NMR data analysis of major newly biosynthesized secondary metabolites. The bioassay showed that the newly biosynthesized compounds, roseopurpurin analogues, showed selective activities against DPPH scavenging, cytotoxicity and SHP1 inhibition. Our research demonstrated that CER was beneficial for changing the secondary metabolic profile of fungi and was an effective means of increasing the diversity of active metabolites. Our work also supplied a metabolomic strategy to characterize the profile changes and determine the newly induced compounds in the secondary metabolites of fungi treated with the chemical epigenetic regulator.
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Epigénesis Genética , Espectrometría de Masas en Tándem , Cromatografía Liquida , Aspergillus/química , Metabolismo SecundarioRESUMEN
BACKGROUND: The co-culture strategy which mimics natural ecology by constructing an artificial microbial community is a useful tool to activate the biosynthetic gene clusters to generate new metabolites. However, the conventional method to study the co-culture is to isolate and purify compounds separated by HPLC, which is inefficient and time-consuming. Furthermore, the overall changes in the metabolite profile cannot be well characterized. RESULTS: A new approach which integrates computational programs, MS-DIAL, MS-FINDER and web-based tools including GNPS and MetaboAnalyst, was developed to analyze and identify the metabolites of the co-culture of Aspergillus sydowii and Bacillus subtilis. A total of 25 newly biosynthesized metabolites were detected only in co-culture. The structures of the newly synthesized metabolites were elucidated, four of which were identified as novel compounds by the new approach. The accuracy of the new approach was confirmed by purification and NMR data analysis of 7 newly biosynthesized metabolites. The bioassay of newly synthesized metabolites showed that four of the compounds exhibited different degrees of PTP1b inhibitory activity, and compound N2 had the strongest inhibition activity with an IC50 value of 7.967 µM. CONCLUSIONS: Co-culture led to global changes of the metabolite profile and is an effective way to induce the biosynthesis of novel natural products. The new approach in this study is one of the effective and relatively accurate methods to characterize the changes of metabolite profiles and to identify novel compounds in co-culture systems.
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Aspergillus/crecimiento & desarrollo , Bacillus subtilis/crecimiento & desarrollo , Metabolismo Secundario , Técnicas de CocultivoRESUMEN
Enterobacter hormaechei is an important emerging pathogen, often exhibiting resistance to multiple clinically important antibiotics. In this study, E. hormaechei was found, for the first time, to be lethal to fish. Bacteriophages are considered potential treatments for bacterial infections. The lytic phage vB_EhoM-IME523 (abbreviated 'IME523') infecting multidrug-resistant E. hormaechei was isolated from hospital sewage. IME523 exhibits T4-like morphology, including a prolate icosahedral head 110 ± 1.89 nm (mean ± SD) long and 82 ± 0.75 nm wide, and a contractile tail of ca. 110 ± 0.91 nm in length. The complete genome length of phage IME523 is 172763 bp, with a G + C content of 39.97%. The whole genome sequence of IME523 has a 93.10% average nucleotide identity (ANI) and a 53.3% in silico DNA-DNA hybridization (isDDH) value with the closest-related Enterobacter phage vB_EclM_CIP9 ('CIP9'). ANI and isDDH values between IME523 and other phages were lower than 78 and 22%, respectively. IME523 and CIP9 formed a monophyletic branch in a phylogenetic tree based on the terminase large subunit, DNA polymerase protein and whole genome phylogenetic analysis. Results suggest that IME523 is a novel species in the subfamily Tevenvirinae and forms a novel genus together with CIP9. No IME523 open reading frame was found to be associated with virulence factors or antibiotic resistance genes. IME523 showed promising protection to zebrafish and brocade carp against E. hormaechei challenge.
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Bacteriófagos , Animales , Bacteriófagos/genética , Enterobacter , Genoma Viral , Filogenia , Pez CebraRESUMEN
With the aim of finding more potential inhibitors against NADH-fumarate reductase (specific target for treating helminthiasis and cancer) from natural resources, Talaromyces wortmannii was treated with the epigenome regulatory agent suberoylanilide hydroxamic acid, which resulted in the isolation of four new wortmannilactones derivatives (wortmannilactones I-L, 1-4). The structures of these new compounds were elucidated based on IR, HRESIMS and NMR spectroscopic data analyses. These four new compounds showed potent inhibitory activity against NADH-fumarate reductase with the IC50 values ranging from 0.84 to 1.35µM.
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Ácidos Hidroxámicos/farmacología , Macrólidos/farmacología , Oxidorreductasas actuantes sobre Donantes de Grupo CH-CH/antagonistas & inhibidores , Talaromyces/química , Medios de Cultivo , Macrólidos/química , Estructura Molecular , Análisis Espectral/métodos , VorinostatRESUMEN
Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.
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Neoplasias , Osteoporosis , Enfermedad Pulmonar Obstructiva Crónica , Humanos , Osteoporosis/diagnóstico , Osteoporosis/epidemiología , Enfermedad Crónica , Aprendizaje Automático , Enfermedad Pulmonar Obstructiva Crónica/diagnóstico , Enfermedad Pulmonar Obstructiva Crónica/epidemiologíaRESUMEN
BACKGROUND: The association between playing Ma-jong, chess and cards (PMCC) and shoulder pain among elderly adults in China remains unclear. The present study aimed to examine the association based on a nationally representative large-scale survey. METHODS: The study used two waves of data from China Health and Retirement Longitudinal Study (CHARLS) in 2015 and 2018. All subjects met the inclusion criteria were classified based on the results of the CHARLS wave questionnaire. Logistic regression models were conducted to analyze the cross-sectional and longitudinal association between PMCC and shoulder pain. Odds ratios (OR) were reported to indicate the strength of the association. RESULTS: In total, 8125 participants aged over 60 years (48.3 % male; mean age 67.9 ± 6.4 years) were enrolled in a cross-sectional association study in 2015, and further 6861 elderly adults were followed up in 2018. The overall prevalence of shoulder pain in the cross-sectional population was 15.6 %. In the non-PMCC and PMCC groups, the incidence of shoulder pain was 16.6 % and 10.7 %, respectively (P < 0.001). In the fully adjusted model, the results showed a significant association between PMCC and shoulder pain (OR: 0.798, 95 % CI: 0.662-0.963, P = 0.018). In the longitudinal analysis from 2015 to 2018, the incidence of new-occurrence shoulder pain in non-PMCC and PMCC groups was 22.1 %, and 18.5 %, respectively (P = 0.004). In the fully adjusted model, compared with the non-PMCC group, the risk of the occurrence of shoulder pain for individuals with PMCC was lower (OR: 0.832, 95 % CI: 0.709-0.975, P = 0.023). CONCLUSIONS: PMCC is associated with lower incidence of shoulder pain, which provides evidence for PMCC as a potential protective factor in the occurrence of shoulder pain.
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Dolor de Hombro , Humanos , Dolor de Hombro/epidemiología , Masculino , Femenino , Estudios Transversales , Anciano , China/epidemiología , Estudios Longitudinales , Incidencia , Persona de Mediana Edad , Prevalencia , Factores de Riesgo , Modelos Logísticos , Pueblos del Este de AsiaRESUMEN
Background: Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS. Methods: We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis. Results: A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes. Conclusion: We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.
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Modulating the inflammatory microenvironment to reconstruct the fibrocartilaginous layer while promoting tendon repair is crucial for enhancing tendon-to-bone healing in rotator cuff repair (RCR), a persistent challenge in orthopedics. Small extracellular vesicles (sEVs) hold significant potential to modulate inflammation, yet the efficient production of highly bioactive sEVs remains a substantial barrier to their clinical application. Moreover, achieving minimally invasive local delivery of sEVs to the tendon-to-bone interface presents significant technical difficulties. Herein, the circadian rhythm of adipose-derived stem cells is modulated to increase the yield and enhance the inflammatory regulatory capacity of sEVs. Circadian rhythm-regulated sEVs (CR-sEVs) enhance the cyclic adenosine monophosphate signaling pathway in macrophage (Mφ) via platelet factor 4 delivery, thereby inhibiting Mφ M1 polarization. Subsequently, a triphasic microneedle (MN) scaffold with a tip, stem, and base is designed for the local delivery of CR-sEVs (CR-sEVs/MN) at the tendon-to-bone junction, incorporating tendon-derived decellularized extracellular matrix in the base to facilitate tendon repair. CR-sEVs/MN mitigates inflammation, promotes fibrocartilage regeneration, and enhances tendon healing, thereby improving biomechanical strength and shoulder joint function in a rat RCR model. Combining CR-sEVs with this triphasic microneedle delivery system presents a promising strategy for enhancing tendon-to-bone healing in clinical settings.
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OBJECTIVE: The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS: Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION: The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.
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Inteligencia Artificial , Melanoma , Humanos , Melanoma/patología , Melanoma/mortalidad , Femenino , Masculino , Pronóstico , Persona de Mediana Edad , Factores de Riesgo , Anciano , Estudios Retrospectivos , Metástasis de la Neoplasia , Programa de VERF , Adulto , Algoritmos , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/mortalidad , Neoplasias Cutáneas/terapia , Modelos LogísticosRESUMEN
Objective: To develop and evaluate a nomogram prediction model for recurrence of acute ischemic stroke (AIS) within one year. Method: Patients with AIS treated at the second affiliated hospital of Xuzhou Medical University from August 2017 to July 2019 were enrolled. Clinical data such as demographic data, risk factors, laboratory tests, TOAST etiological types, MRI features, and treatment methods were collected. Cox regression analysis was done to determine the parameters for entering the nomogram model. The performance of the model was estimated by receiver operating characteristic curves, decision curve analysis, calibration curves, and C-index. Result: A total of 645 patients were enrolled in this study. Side of hemisphere (SOH, Bilateral, HR = 0.35, 95 % CI = 0.15-0.84, p = 0.018), homocysteine (HCY, HR = 1.38, 95 % CI = 1.29-1.47, p < 0.001), c-reactive protein (CRP, HR = 1.04, 95 % CI = 1.01-1.07, p = 0.013) and stroke severity (SS, HR = 3.66, 95 % CI = 2.04-6.57, p < 0.001) were independent risk factors. The C-index of the nomogram model was 0.872 (se = 0.016). The area under the receiver operating characteristic (ROC)curve at one-year recurrence was 0.900. Calibration curve, decision curve analysis showed good performance of the nomogram. The cutoff value for low or high risk of recurrence score was 1.73. Conclusion: The nomogram model for stroke recurrence within one year developed in this study performed well. This useful tool can be used in clinical practice to provide important guidance to healthcare professionals.
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PURPOSE: This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. METHODS: Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. RESULTS: Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. CONCLUSIONS: Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05867732 .
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Algoritmos , Hospitales , Humanos , Estudios de Cohortes , Tiempo de Internación , Aprendizaje AutomáticoRESUMEN
Osteosarcoma is a highly aggressive and metastatic malignant tumor. It has the highest incidence of all malignant bone tumors and is one of the most common solid tumors in children and adolescents. Osteosarcoma tissues are often richly infiltrated with inflammatory cells, including tumor-associated macrophages, lymphocytes, and dendritic cells, forming a complex immune microenvironment. The expression of immune checkpoint molecules is also high in osteosarcoma tissues, which may be involved in the mechanism of anti-tumor immune escape. Metabolism and senescence are closely related to the immune microenvironment, and disturbances in metabolism and senescence may have important effects on the immune microenvironment, thereby affecting immune cell function and immune responses. Metabolic modulation and anti-senescence therapy are gaining the attention of researchers as emerging immunotherapeutic strategies for tumors. Through an in-depth study of the interconnection of metabolism and anti- senescence in the tumor immune microenvironment and its regulatory mechanism on immune cell function and immune response, more precise therapeutic strategies can be developed. Combined with the screening and application of biomarkers, personalized treatment can be achieved to improve therapeutic efficacy and provide a scientific basis for clinical decision-making. Metabolic modulation and anti- senescence therapy can also be combined with other immunotherapy approaches, such as immune checkpoint inhibitors and tumor vaccines, to form a multi-level and multi-dimensional immunotherapy strategy, thus further enhancing the effect of immunotherapy. Multidisciplinary cooperation and integrated treatment can optimize the treatment plan and maximize the survival rate and quality of life of patients. Future research and clinical practice will further advance this field, promising more effective treatment options for patients with osteosarcoma. In this review, we reviewed metabolic and senescence characteristics in the immune microenvironment of osteosarcoma and related immunotherapies, and provide a reference for development of more personalized and effective therapeutic strategies.
Asunto(s)
Neoplasias Óseas , Osteosarcoma , Niño , Humanos , Adolescente , Calidad de Vida , Inmunoterapia/métodos , Neoplasias Óseas/tratamiento farmacológico , Osteosarcoma/patología , Microambiente TumoralRESUMEN
Objective: To explore the predictors of death in acute ischemic stroke (AIS) patients within 1 year based on machine learning (ML) algorithms. Methods: This study retrospectively analyzed the clinical data of patients hospitalized and diagnosed with AIS in the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. The patients were randomly divided into training and validation sets at a ratio of 7:3, and the clinical characteristic variables of the patients were screened using univariate and multivariate logistics regression. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), random forest (RF), decision tree (DT), and naive Bayes classifier (NBC), were applied to develop models to predict death in AIS patients within 1 year. During training, a 10-fold cross-validation approach was used to validate the training set internally, and the models were interpreted using important ranking and the SHapley Additive exPlanations (SHAP) principle. The validation set was used to externally validate the models. Ultimately, the highest-performing model was selected to build a web-based calculator. Results: Multivariate logistic regression analysis revealed that C-reactive protein (CRP), homocysteine (HCY) levels, stroke severity (SS), and the number of stroke lesions (NOS) were independent risk factors for death within 1 year in patients with AIS. The area under the curve value of the XGB model was 0.846, which was the highest among the six ML algorithms. Therefore, we built an ML network calculator (https://mlmedicine-de-stroke-de-stroke-m5pijk.streamlitapp.com/) based on XGB to predict death in AIS patients within 1 year. Conclusions: The network calculator based on the XGB model developed in this study can help clinicians make more personalized and rational clinical decisions.