Misdiagnosis of multiple myeloma as postoperative bone metastasis of rectal cancer: A case report and literature review.

Multiple myeloma (MM) and bone metastases are both common malignant tumors of the skeleton that share similar clinical manifestations and radiological features. The development of MM following rectal cancer surgery is relatively rare in clinical practice and is easily misdiagnosed as bone metastasis. The present study reported on a patient with MM and postoperative rectal cancer. A 65-year-old man had been diagnosed with low rectal cancer (poorly differentiated, T3N1M0) 10 years prior and underwent curative treatment at that time. During the 6-year follow-up period, no recurrence or metastasis of rectal cancer was detected. The patient was evaluated for bone pain 4 years ago and underwent multiple imaging examinations, including computed tomography (CT), magnetic resonance imaging, emission CT and positron emission tomography/CT at several well-known hospitals in China. All of these hospitals diagnosed the patient with bone metastasis from rectal cancer, in view of the earlier history. The patient's condition did not show any significant improvement despite treatment for bone metastasis. Subsequently, 3 years ago, the patient underwent surgical treatment at our hospital (Affiliated Hospital of Zunyi Medical University, Zunyi, China) for a hernia near the colostomy site combined with incomplete intestinal obstruction. Post-operatively, the patient developed a hematoma in the surgical area, along with stubborn anemia and abnormal coagulation function. No improvement was observed with hemostasis and multiple blood transfusions. The bone marrow smear was consistent with MM, with a significant elevation in serum IgA and ß2 microglobulin. The patient was ultimately diagnosed with MM (IgA-λ type), stage III, according to the Durie-Salmon staging system. The patient's condition improved with treatment for MM.

High-quality TEM specimen preparation for lithium-ion conducting solid electrolytes by low-energy ion milling.

Focused ion beam (FIB) is a widely used method to prepare transmission electron microscopy (TEM) specimen from bulk materials. However, the surface amorphous layer induced by ion beam is an obstacle to obtain high-quality atomic-scale images for quantitative analysis, especially for the analysis of light elements such as lithium in lithium-ion conducting solid electrolytes. Here, taking lithium-ion conducting solid electrolyte materials as an example, the advantages and disadvantages of applying low-energy Ar+ ion for fine milling after FIB are investigated. Combining Monte-Carlo simulations with ion milling experiments, the milling parameters are evaluated and discussed in detail. With optimized parameters, TEM specimens with less beam damage and thinner amorphous layer were prepared, enabling the acquisition of high-quality atomic-scale images. Furthermore, low-energy Ar+ ion milling is also able to remove hydrocarbon contamination formed during the electron beam illumination inside the microscope, making the contaminated TEM specimens reusable.

The impact of sport-specific physical fitness change patterns on lower limb non-contact injury risk in youth female basketball players: a pilot study based on field testing and machine learning.

Background: In recent years, identifying players with injury risk through physical fitness assessment has become a hot topic in sports science research. Although practitioners have conducted many studies on the relationship between physical fitness and the likelihood of injury, the relationship between the two remains indeterminate. Consequently, this study utilized machine learning to preliminary investigate the relationship between individual physical fitness tests and injury risk, aiming to identify whether patterns of physical fitness change have an impact on injury risk. Methods: This study conducted a retrospective analysis by extracting the records of 17 young female basketball players from the sport-specific physical fitness monitoring and injury registration database in Fujian Province. Sports-specific physical fitness tests included physical performance, physiological, biochemical, and subjective perceived responses. The data for each player was standardized individually using Z-scores. Synthetic minority over-sampling techniques and edited nearest neighbor algorithms were used to sample the training set to address the negative impact of class imbalance on model performance. Feature extraction was performed on the dataset using linear discriminant analysis, and the prediction model was constructed using the cost-sensitive neural network. Results: The 10 replicate 5-fold stratified cross-validation showed that the lower limb non-contact injury prediction model based on the cost-sensitive neural network had achieved good discrimination and calibration (average Precision: 0.6360; average Recall: 0.8700; average F2-Score: 0.7980; average AUC: 0.8590; average Brier-score: 0.1020), which could be well applied in training practice. According to the attribution analysis, agility and speed were important physical attributes that affect youth female basketball players' non-contact lower limb injury risk. Specifically, there was enhance in the performance of the 1-min double under, accompanied by an increase in urinary ketone and urinary blood levels following the agility test. The 3/4 basketball court sprint performance improved, while urinary protein and RPE levels decreased after the speed test. Conclusion: The sport-specific physical fitness change pattern can impact the lower limb non-contact injury risk of young female basketball players in Fujian Province, specifically in terms of agility and speed. These findings will provide valuable insights for planning athletes' physical training programs, managing fatigue, and preventing injuries.

A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning.

The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice.

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

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.

Automatic Multichannel Electrocardiogram Record Classification Using XGBoost Fusion Model.

There is an increasing demand for automatic classification of standard 12-lead electrocardiogram signals in the medical field. Considering that different channels and temporal segments of a feature map extracted from the 12-lead electrocardiogram record contribute differently to cardiac arrhythmia detection, and to the classification performance, we propose a 12-lead electrocardiogram signal automatic classification model based on model fusion (CBi-DF-XGBoost) to focus on representative features along both the spatial and temporal axes. The algorithm extracts local features through a convolutional neural network and then extracts temporal features through bi-directional long short-term memory. Finally, eXtreme Gradient Boosting (XGBoost) is used to fuse the 12-lead models and domain-specific features to obtain the classification results. The 5-fold cross-validation results show that in classifying nine categories of electrocardiogram signals, the macro-average accuracy of the fusion model is 0.968, the macro-average recall rate is 0.814, the macro-average precision is 0.857, the macro-average F1 score is 0.825, and the micro-average area under the curve is 0.919. Similar experiments with some common network structures and other advanced electrocardiogram classification algorithms show that the proposed model performs favourably against other counterparts in F1 score. We also conducted ablation studies to verify the effect of the complementary information from the 12 leads and the auxiliary information of domain-specific features on the classification performance of the model. We demonstrated the feasibility and effectiveness of the XGBoost-based fusion model to classify 12-lead electrocardiogram records into nine common heart rhythms. These findings may have clinical importance for the early diagnosis of arrhythmia and incite further research. In addition, the proposed multichannel feature fusion algorithm can be applied to other similar physiological signal analyses and processing.

All-Solid-State Batteries with a Limited Lithium Metal Anode at Room Temperature using a Garnet-Based Electrolyte.

Metallic lithium (Li), considered as the ultimate anode, is expected to promise high-energy rechargeable batteries. However, owing to the continuous Li consumption during the repeated Li plating/stripping cycling, excess amount of the Li metal anode is commonly utilized in lithium-metal batteries (LMBs), leading to reduced energy density and increased cost. Here, an all-solid-state lithium-metal battery (ASSLMB) based on a garnet-oxide solid electrolyte with an ultralow negative/positive electrode capacity ratio (N/P ratio) is reported. Compared with the counterpart using a liquid electrolyte at the same low N/P ratios, ASSLMBs show longer cycling life, which is attributed to the higher Coulombic efficiency maintained during cycling. The effect of the species of the interface layer on the cycling performance of ASSLMBs with low N/P ratio is also studied. Importantly, it is demonstrated that the ASSLMB using a limited Li metal anode paired with a LiFePO4 cathode (5.9 N/P ratio) delivers a stable long-term cycling performance at room temperature. Furthermore, it is revealed that enhanced specific energies for ASSLMBs with low N/P ratios can be further achieved by the use of a high-voltage or high mass-loading cathode. This study sheds light on the practical high-energy all-solid-state batteries under the constrained condition of a limited Li metal anode.

Ultrasensitive, Superhigh Signal-to-Noise Ratio, Self-Powered Solar-Blind Photodetector Based on n-Ga2O3/p-CuSCN Core-Shell Microwire Heterojunction.

Solar-blind photodetectors have captured intense attention due to their high significance in ultraviolet astronomy and biological detection. However, most of the solar-blind photodetectors have not shown extraordinary advantages in weak light signal detection because the forewarning of low-dose deep-ultraviolet radiation is so important for the human immune system. In this study, a high-performance solar-blind photodetector is constructed based on the n-Ga2O3/p-CuSCN core-shell microwire heterojunction by a simple immersion method. In comparison with the single device of the Ga2O3 and CuSCN, the heterojunction photodetector demonstrates an enhanced photoelectric performance with an ultralow dark current of 1.03 pA, high photo-to-dark current ratio of 4.14 × 104, and high rejection ratio (R254/R365) of 1.15 × 104 under a bias of 5 V. Excitingly, the heterostructure photodetector shows high sensitivity to the weak signal (1.5 µW/cm2) of deep ultraviolet and high-resolution detection to the subtle change of signal intensity (1.0 µW/cm2). Under the illumination with 254 nm light at 5 V, the photodetector shows a large responsivity of 13.3 mA/W, superb detectivity of 9.43 × 1011 Jones, and fast response speed with a rise time of 62 ms and decay time of 35 ms. Additionally, the photodetector can work without an external power supply and has specific solar-blind spectrum selectivity as well as excellent stability even through 1 month of storage. Such prominent photodetection, profited by the novel geometric construction and the built-in electric field originating from the p-n heterojunction, meets greatly well the "5S" requirements of the photodetector for practical application.

Biofilm Nanofiber-Coated Separators for Dendrite-Free Lithium Metal Anode and Ultrahigh-Rate Lithium Batteries.

Rechargeable batteries that combine high energy density with high power density are highly demanded. However, the wide utilization of lithium metal anode is limited by the uncontrollable dendrite growth, and the conventional lithium-ion batteries (LIBs) commonly suffer from low rate capability. Here, we for the first time develop a biofilm-coated separator for high-energy and high-power batteries. It reveals that the coating of Escherichia coli protein nanofibers can improve electrolyte wettability and lithium transference number and enhance adhesion between separators and electrodes. Thus, lithium dendrite growth is impeded because of the uniform distribution of the Li-ion flux. The modified separator also enables the stable cycling of high-voltage Li|Li1.2Mn0.6Ni0.2O2 (LNMO) cells at an extremely high rate of 20 C, delivering a high specific capacity of 83.1 mA h g-1, which exceeds the conventional counterpart. In addition, the modified separator in the Li4Ti5O12|LNMO full cell also exhibits a larger capacity of 68.2 mA h g-1 at 10 C than the uncoated separator of 37.4 mA h g-1. Such remarkable performances of the modified separators arise from the conformal, adhesive, and endurable coating of biofilm nanofibers. Our work opens up a new opportunity for protein-based biomaterials in practical application of high-energy and high-power batteries.