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Accurate identification of fungal species is crucial for mycological research, relying significantly on experienced taxonomists’ ability to recognize fungal morphology. With the dwindling number of taxonomists, developing a rapid, accurate, and automated fungal identification method is essential, promising to markedly decrease the time and resources needed for such tasks. To address the challenges in the automatic identification of nematode-trapping fungi (NTF, belong to Orbiliaceae, Orbiliomycetes, a group of filamentous fungi that catch nematodes in soil), specifically focusing on their conidia which are often aggregated and morphologically similar, we developed an enhanced method. Our approach integrates an efficient channel attention (ECA) mechanism within the backbone layer of YOLOv8l, improving the algorithm’s capability to detect global information effectively. This adjustment significantly optimizes the detection and differentiation of limited features and small-sized conidia among aggregated samples. The experimental outcomes demonstrated that our method increases precision by 0.3%, recall by 1.8%, and mean average precision (mAP) by 0.4%, surpassing the performance of existing target detection algorithms. In addition, this method accurately identified the aggregated fungal conidia and distinguished morphologically similar spores. In conclusion, the system accurately detects and recognizes the conidia of NTF in mixed spore environments, thus providing crucial technical support for the automatic detection and early prediction of NTF.

Introduction: Cold and exercise are two important stimuli affecting the secretion of osteokines and adipomyokines, which often occur simultaneously. However, few studies have investigated the changes in osteokines and adipomyokines induced by exercise during severe cold and their corresponding associations. Therefore, this study aimed to investigate the changes in sclerostin and meteorin-like (metrnl) protein before and after cold exercise (ice swimming [IS]) and observe their correlation.Methods: For this, 56 daily ice swimmers’ data were included in this study. Serum sclerostin and metrnl were measured 30 min before IS and 30 min after. The fat mass, visceral fat area, fat-free mass, skeletal muscle mass, lumbar spine, and femoral neck bone mineral density of the ice swimmers were measured.Results: After IS, sclerostin exhibited significant decreases, whereas metrnl showed no significant change. In addition, the baseline level of sclerostin and the decrease in sclerostin were positively correlated with serum metrnl after adjusting for age, gender, and body composition indicators.Discussion: IS caused a significant decrease in sclerostin but did not affect metrnl. Furthermore, the associations between sclerostin and metrnl suggested a correlation between osteokines and adipomyokines; this encourages future exploration of the interconnection between bone, muscle, and fat, which will be beneficial for identifying potential common therapeutic targets for diseases such as osteoporosis, sarcopenia, and obesity.

Traumatic brain injury (TBI) is a severe condition that leads to brain damage and affects brain function. Importantly, TBI incurs public health costs due to its high mortality, and effective treatment for TBI is still lacking. Docosahexaenoic acid (DHA) has a neuroprotective effect that can reduce oxidative, apoptosis, and inflammatory processes. Administration of DHA after TBI attenuates oxidative stress and protein accumulation and is regarded as a potential therapeutic. Iduna is a regulator of parthanatos, and upregulation of Iduna reduces cellular damage and mitochondrial dysfunction. Thus, we speculated that overexpression of Iduna might promote DHA therapy in the treatment of TBI. Here, we found that after combination overexpression of Iduna and DHA in a mouse model of TBI, the expression of inflammatory factors was reduced, while the secretion of neuroprotective factors was increased. In addition, we found that these effects might be mediated by the Wnt/MDM2 pathway, and Iduna might be a therapeutic target for TBI.

Abstract Background Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. Methods Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. Results TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. Conclusions The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

Continuous respiratory monitoring is an important tool for clinical monitoring. Associated with the development of biomedical technology, it has become more and more important, especially in the measuring of gas flow and CO2 concentration, which can reflect the status of the patient. In this paper, a new type of biomedical device is presented, which uses low-power sensors with a piezoresistive silicon differential pressure sensor to measure gas flow and with a pyroelectric sensor to measure CO2 concentration simultaneously. For the portability of the biomedical device, the sensors and low-power measurement circuits are integrated together, and the airway tube also needs to be miniaturized. Circuits are designed to ensure the stability of the power source and to filter out the existing noise. Modulation technology is used to eliminate the fluctuations at the trough of the waveform of the CO2 concentration signal. Statistical analysis with the coefficient of variation was performed to find out the optimal driving voltage of the pressure transducer. Through targeted experiments, the biomedical device showed a high accuracy, with a measuring precision of 0.23 mmHg, and it worked continuously and stably, thus realizing the real-time monitoring of the status of patients.