METHODS: G. lucidum samples from various sources and in varying stages were identified by using δ 13C, δD, δ 18O, δ 15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum.
RESULTS: In the stage model, the δ 13C, δD, δ 18O, δ 15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ 13C, δD, δ 18O, and δ 15N). In the origin model, only four variables, namely δ 13C, δD, δ 18O, and δ 15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%.
CONCLUSION: C, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.
METHODS: Positron emission tomography (PET) and computed tomography (CT) image data from 97 patients with LC and 77 patients with TB nodules were collected. One hundred radiomic features were extracted from both PET and CT imaging using the pyradiomics platform, and 2048 deep learning features were obtained through a residual neural network approach. Four models included traditional machine learning model with radiomic features as input (traditional radiomics), a deep learning model with separate input of image features (deep convolutional neural networks [DCNN]), a deep learning model with two inputs of radiomic features and deep learning features (radiomics-DCNN) and a deep learning model with inputs of radiomic features and deep learning features and clinical information (integrated model). The models were evaluated using area under the curve (AUC), sensitivity, accuracy, specificity, and F1-score metrics.
RESULTS: The results of the classification of TB nodules and LC showed that the integrated model achieved an AUC of 0.84 (0.82-0.88), sensitivity of 0.85 (0.80-0.88), and specificity of 0.84 (0.83-0.87), performing better than the other models.
CONCLUSION: The integrated model was found to be the best classification model in the diagnosis of TB nodules and solid LC.
CASE PRESENTATION: A 65-year-old male recovering from a left massive intracerebral hemorrhage after open debridement hematoma removal had impaired right limb movement, right hemianesthesia, motor aphasia, dysphagia, and complete dependence on his daily living ability. After receiving 3 months of conventional rehabilitation therapy, his cognitive, speech, and swallowing significantly improved but the Brunnstrom Motor Staging (BMS) of his right upper limb and hand was at stage I-I. UG-MNES was applied on the right upper limb for four sessions, once per week, together with conventional rehabilitation. Immediate improvement in the upper limb function was observed after the first treatment. To determine the effect of UG-MNES on long-term functional recovery, assessments were conducted a week after the second and fourth intervention sessions, and motor function recovery was observed after 4-week of rehabilitation. After completing the full rehabilitation course, his BMS was at stage V-IV, the completion time of Jebsen Hand Function Test (JHFT) was shortened, and the scores of Fugl-Meyer Assessment for upper extremity (FMA-UE) and Modified Barthel Index (MBI) were increased. Overall, the motor function of the hemiplegic upper limb had significantly improved, and the right hand was the utility hand. Electromyography (EMG) and nerve conduction velocity (NCV) tests were normal before and after treatment.
CONCLUSION: The minimally invasive, UG-MNES could be a new alternative treatment in stroke rehabilitation for functional recovery of the upper limbs.
METHODS: This cross-sectional study involved 1,719 students. Mplus 8.0 software was used to conduct latent profile analysis to explore the potential categories of depression and anxiety comorbidities. R4.3.2 software was used to explore the network of core depression and anxiety symptoms, bridge these disorders, and evaluate the effects of risk and protective factors.
RESULTS: Three categories were established: "healthy" (57.8%), "mild depression-mild anxiety" (36.6%), and "moderately severe depression-moderate anxiety" (5.6%). "Depressed mood", "nervousness", and "difficulty relaxing" were core symptoms in both the depression-anxiety comorbidity network and the network of risk and protective factors. Stress perception and neuroticism serve as bridging nodes connecting some symptoms of depression and anxiety and are thus considered the most prominent risk factors.
CONCLUSIONS: According to the core and bridging symptoms identified in this study, targeted intervention and treatment can be provided to groups with comorbid depression and anxiety, thereby reducing the risk of these comorbidities in adolescents.
OBJECTIVES: By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies.
METHODS: The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics.
RESULTS: The NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.
RESULTS: Here, we reported the first Oceanospirillum phage, vB_OliS_GJ44, which was assembled into a 33,786 bp linear dsDNA genome, which includes abundant tail-related and recombinant proteins. The recombinant module was highly adapted to the host, according to the tetranucleotides correlations. Genomic and morphological analyses identified vB_OliS_GJ44 as a siphovirus, however, due to the distant evolutionary relationship with any other known siphovirus, it is proposed that this virus could be classified as the type phage of a new Oceanospirivirus genus within the Siphoviridae family. vB_OliS_GJ44 showed synteny with six uncultured phages, which supports its representation in uncultured environmental viral contigs from metagenomics. Homologs of several vB_OliS_GJ44 genes have mostly been found in marine metagenomes, suggesting the prevalence of this phage genus in the oceans.
CONCLUSIONS: These results describe the first Oceanospirillum phage, vB_OliS_GJ44, that represents a novel viral cluster and exhibits interesting genetic features related to phage-host interactions and evolution. Thus, we propose a new viral genus Oceanospirivirus within the Siphoviridae family to reconcile this cluster, with vB_OliS_GJ44 as a representative member.
METHODS: DNA of 400 cynomolgus macaques from 10 Chinese breeding farms was genotyped to characterize their regional origin and rhesus ancestry proportion. A nested PCR assay was used to detect Plasmodium cynomolgi infection in sampled individuals.
RESULTS: All populations exhibited high levels of genetic heterogeneity and low levels of inbreeding and genetic subdivision. Almost all individuals exhibited an Indochinese origin and a rhesus ancestry proportion of 5%-48%. The incidence of P. cynomolgi infection in cynomolgus macaques is strongly associated with proportion of rhesus ancestry.
CONCLUSIONS: The varying amount of rhesus ancestry in cynomolgus macaques underscores the importance of monitoring their genetic similarity in malaria research.