Table1_A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning.DOCX

The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.</p

Ultrathin MoS₂ Nanosheets Vertically Grown on CoS₂ Acicular Nanorod Arrays: A Synergistic Three-Dimensional Shell/Core Heterostructure for High-Efficiency Hydrogen Evolution at Full pH

A three-dimensional (3D) MoS2/CoS2 composite with shell/core heterostructure is in situ synthesized, which is composed of CoS2 acicular nanorod arrays and edge-terminated MoS2 nanosheets vertically grown on each CoS2 nanorod. Benefiting from the epitaxial growth of 2H-MoS2 on the surface of CoS2, the edge planes of ultrathin MoS2 nanosheets are significantly exposed. Due to the structural properties, a large number of direct contact areas are formed between MoS2 and CoS2, and a strong interaction between these two phases causes the electrons at the interface to be redistributed. Owing to the synergy of MoS2 and CoS2, significantly increased MoS2 edge sites, and improved mass transfer and electron transport, MoS2/CoS2 exhibits a remarkable catalytic ability for hydrogen evolution reaction (HER) at full pH. In 1.0 M KOH, 1 M PBS (pH = 7.0), and 0.5 M H2SO4, the overpotential at a current density of 10 mA cm–2 is 97, 196, and 126 mV, with Tafel slope of 78.7, 77.5, 42.6 mV dec–1, respectively. In addition to reporting a promising HER catalyst, this work will also provide a meaningful reference for designing 3D nanocatalysts

Additional file 1: of Metabolic derangements of skeletal muscle from a murine model of glioma cachexia

Table S1. Relevant references for the used antibodies. Figure S1. Typical 2D 1H-13C HSQC spectrum of the aqueous extract derived from the gastrocnemius muscle of a HEB mouse. The spectrum was recorded at 25 °C on a Bruker Advance III 850 MHz NMR spectrometer. The inserted dashed box shows the amplification map of the region 1H (3.0–4.6 ppm) and 13C (60–80 ppm) of the full spectrum. The serial numbers indicate the following metabolites: 1, isoleucine; 2, valine; 3, leucine; 4, ethanol; 5, 3-hydroxybutyrate; 6 lactate; 7, alanine; 8, lysine; 9, glutamine; 10, glutathione; 11, glutamate; 12, creatine; 13, taurine; 14, inosinate; 15, glycine; 16, glycerol; 17, choline; 18, myoinositol; 19, glucose; 20, serine; 21, carnosine; 22, anserine; 23, fumarate; 24, phenylalanine; 25, niacinamide; 26, acetate; 27, tyrosine; 28, threonine; 29, aspartate; 30, asparagine; 31, malate; 32, inosine; 33, mannose; 34, histidine; 35, arginine; 36, phosphocholine; 37, NAD+; 38, glycerophosphocholine (GPC). Figure S2. PLS-DA score plots and validation plots of 1D 1H NMR data for aqueous extracts derived from gastrocnemius muscles of mice. (A), (D) CHG5 vs. HEB mice; (B), (E) U87 vs. HEB mice; (C), (F) U87 vs. CHG5 mice. The PLS-DA models were cross-validated to evaluate the robustness by a random permutation test (200 cycles). n = 6–7 mice/group. Table S2. Comparison of metabolite levels between the three groups of mice based on relative integrals calculated from the 1D 1H NMR spectra of aqueous gastrocnemius extracts. Table S3. Comparison of glucose levels between the three groups of mice based on relative integrals calculated from the 1D 1H NMR spectra of sera. (DOCX 439 kb

IL-1β regulated the expression of TNF-α, IL-6 and IL-8.

The expression levels of TNF-α, IL-6 and IL-8 were significantly increased after CSE administration, which were downregulated upon IL-1β knockdown. Representative western blot image of the qualitative expression of TNF-α, IL-6 and IL-8 (A) The quantitative expression of TNF-α (B), IL-6 (C) and IL-8 (D) were calculated with GAPDH as an internal control. Results are represented as Mean ± S.E.M. ** P # P ## P < 0.01 vs model (n = 5). OvExp: overexpression; OvExp-EV: overexpression empty vectors; KD: knockdown; KD-EV: knockdown empty vector.</p

IL-1β affected the expression of PAPP-A mRNA.

The increased IL-1β mRNA expression level was observed in model group, which was significantly reduced in IL-1β knockdown group (A). The PAPP-A mRNA expression level was observed in model group, which was significantly downregulated by the IL-1β knockdown (A) Results are represented as Mean ± S.E.M. ** P ## P < 0.01 vs model(n = 5).PAPP-A: pregnancy-associated plasma protein; OvExp: overexpression; OvExp-EV: overexpression empty vectors; KD: knockdown; KD-EV: knockdown empty vector.</p

Effect of CSE on PAPP-A secretions in Rat A7r5 cells.

A7r5 cells were exposed to different concentrations of CSE and incubated for 8h. PAPP-A secretion was measured in culture supernatants by ELISA. Results are represented as Mean ± S.E.M. ** P < 0.01 vs 0%CSE (n = 5). CSE:cigarette smoke extract; PAPP-A: pregnancy-associated plasma protein A.</p

The efficiency of IL-1β overexpression and knockdown.

A7r5 cells were transiently transfected with the vectors for IL-1β overexpression (A) and knockdown (B), with the related empty vector and untransfected cells as control. Cells were harvested and cell lysates were analyzed by qPCR to detect the efficiency of vectors. Results are represented as Mean ± S.E.M. ** P OvExp: overexpression; OvExp-EV: overexpression empty vectors; KD-EV: knockdown empty vector; KD: knockdown.</p

Primer sequences.

Primer sequences.</p

The apoptosis rate and apoptosis-related protein expression levels.

The apoptosis rate was detected by flow cytometry. The increased apoptosis rate was observed in model group, which was reduced after IL-1β knockdown (A, B). The expression level of Bax protein was higher in model group and vice versa for Bcl-2, which were revered considerably by IL-1β knockdown (C-E). Results are represented as Mean ± S.E.M. ** P ## P < 0.01 vs model (n = 5).OvExp: overexpression; OvExp-EV: overexpression empty vectors; KD: knockdown; KD-EV: knockdown empty vector.</p

Metabolic Profiling of Tumors, Sera, and Skeletal Muscles from an Orthotopic Murine Model of Gastric Cancer Associated-Cachexia

Cachexia is a complex metabolic derangement syndrome that affects approximately 50–80% of cancer patients. So far, few works have been reported to provide a global overview of gastric cancer cachexia (GCC)-related metabolic changes. We established a GCC murine model by orthotopicly implanting BGC823 cell line and conducted NMR-based metabolomic analysis of gastric tissues, sera, and gastrocnemius. The model with typical cachexia symptoms, confirmed by significant weight loss and muscle atrophy, showed distinctly distinguished metabolic profiles of tumors, sera, and gastrocnemius from sham mice. We identified 20 differential metabolites in tumors, 13 in sera, and 14 in gastrocnemius. Tumor extracts displayed increased pyruvate and lactate, and decreased hypoxanthine, inosine, and inosinate, indicating significantly altered glucose and nucleic acid metabolisms. Cachectic mice exhibited up-regulated serum lactate and glycerol, and down-regulated glucose, which were closely related to hyperlipidemia and hypoglycemia. Furthermore, gastrocnemius transcriptomic and metabolomic data revealed that GCC induced perturbed pathways mainly concentrated on carbohydrate and amino acid metabolism. Specifically, cachectic gastrocnemius exhibited increased α-ketoglutarate and decreased glucose. In vitro study indicated that α-ketoglutarate could prompt myoblasts proliferation and reduce glucose deficiency-induced myotubes atrophy. Overall, this work provides a global metabolic overview to understand the metabolic alterations associated with GCC-induced muscle atrophy