BALANCE TRAINING AND PHYSICAL ABILITY OF BASKETBALL PLAYERS

ABSTRACT Introduction Balance in gait is a fundamental factor for rhythmic changes on the court, and physical fitness is a basic requirement for competitiveness in basketball. Objective Analyze the effects of balance training on the gait and physical fitness of basketball players. Methods Thirty basketball athletes were selected and randomly divided into a control and experimental group. A balance training program including balls was added to the experimental group, while the control group followed only the traditional training program. The experiment lasted 8 weeks, with the interventions applied 3 times a week. Finally, relevant data collected before and after the experiment were statistically analyzed and discussed. Results The experimental and the control groups showed a statistical gain in balance, with a greater change interval in the experimental group, demonstrating the effect of dynamic balance training in improving basketball-related physical indices. Conclusion There are some deficiencies in the traditional basketball training program that can be compensated with the addition of the balance training program presented in this article, fully improving the skills of basketball players. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.</div

sj-docx-2-jic-10.1177_08850666231214243 - Supplemental material for Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis

Supplemental material, sj-docx-2-jic-10.1177_08850666231214243 for Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis by Kunmei Lai, Guo Lin, Caiming Chen and Yanfang Xu in Journal of Intensive Care Medicine</p

sj-docx-1-jic-10.1177_08850666231214243 - Supplemental material for Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis

Supplemental material, sj-docx-1-jic-10.1177_08850666231214243 for Development and Validation of a Predictive Model for Acute Kidney Injury in Sepsis Patients Based on Recursive Partition Analysis by Kunmei Lai, Guo Lin, Caiming Chen and Yanfang Xu in Journal of Intensive Care Medicine</p

Negative Ion Laser Desorption/Ionization Time-of-Flight Mass Spectrometric Analysis of Small Molecules Using Graphitic Carbon Nitride Nanosheet Matrix

Ultrathin graphitic carbon nitride (g-C₃N₄) nanosheets served as a novel matrix for the detection of small molecules by negative ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was described for the first time. In comparison with conventional organic matrices and graphene matrix, the use of g-C₃N₄ nanosheet matrix showed free matrix background interference and increased signal intensity in the analysis of amino acids, nucleobases, peptides, bisphenols (BPs), and nitropolycyclic aromatic hydrocarbons (nitro-PAHs). A systematic comparison of g-C₃N₄ nanosheets with positive and negative ion modes revealed that mass spectra produced by g-C₃N₄ nanosheets in negative ion mode were featured by singly deprotonated ion without matrix interference, which was rather different from the complicated alkali metal complexes in positive ion mode. Good salt tolerance and reproducibility allowed the determination of 1-nitropyrene (1-NP) in sewage, and its corresponding detection limit was lowered to 1 pmol. In addition, the ionization mechanism of the g-C₃N₄ nanosheets as matrix was also discussed. The work expands its application scope of g-C₃N₄ nanosheets and provides an alternative approach for small molecules

Protein-Metal Organic Framework Hybrid Composites with Intrinsic Peroxidase-like Activity as a Colorimetric Biosensing Platform

Artificial enzyme mimetics have received considerable attention because natural enzymes have some significant drawbacks, including enzyme autolysis, low catalytic activity, poor recovery, and low stability to environmental changes. Herein, we demonstrated a facile approach for one-pot synthesis of hemeprotein-metal organic framework hybrid composites (H-MOFs) by using bovine hemoglobin (BHb) and zeolitic imidazolate framework-8 (ZIF-8) as a model reaction system. Surprisingly, the new hybrid composites exhibit 423% increase in peroxidase-like catalytic activity compared to free BHb. Taking advantages of the unique pore structure of H-MOFs with high catalytic property, a H-MOFs-based colorimetric biosensing platform was newly constructed and applied for the fast and sensitive detection of hydrogen peroxide (H₂O₂) and phenol. The corresponding detection limits as low as 1.0 μM for each analyte with wide linear ranges (0–800 μM for H₂O₂ and 0–200 μM for phenol) were obtained by naked-eye visualization. Significantly, a sensitive and selective method for visual assay of trace H₂O₂ in cells and phenol in sewage was achieved with this platform. The stability of H-MOFs was also examined, and excellent reproducibility and recyclability without losing in their activity were observed. In addition, the general applicability of H-MOFs was also investigated by using other hemeproteins (horseradish peroxidase, and myoglobin), and the corresponding catalytic activities were 291% and 273% enhancement, respectively. This present work not only expands the application of MOFs but also provides an alternative technique for biological and environmental sample assay

Table_1_Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.xlsx

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.</p

DataSheet_7_Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.docx

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.</p

DataSheet_4_Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.pdf

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.</p

Table_2_Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.xlsx

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.</p

DataSheet_1_Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms.pdf

BackgroundDeveloping prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.MethodsMachine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.ResultsWe constructed the ML-signature based on 429 (training/validation = 8/2) patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.ConclusionPrevious studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB.</p