Changes of adrenocorticotropic hormone rhythm and cortisol circadian rhythm in patients with depression complicated with anxiety and their effects on the psychological state of patients

Objective: This work was to explore the rhythm of adrenocorticotropic hormone (ACTH) and cortisol in patients with depression and anxiety and their effects on mental state. In this work, with depression complicated with anxiety patients as the A-MDD group (n = 21), and depression without anxiety symptoms as the NA-MDD group (n = 21). Firstly, data features were extracted according to the electroencephalo-graph (EEG) data of different patients, and a DR model was constructed for diagnosis. The Hamilton Depression Scale 24 (HAMD-24) was employed to evaluate the severity, and the ACTH and cortisol levels were detected and compared for patients in the A-MDD group and NA-MDD group. In addition, the psychological status of the patients was assessed using the Toronto Alexithymia Scale (TAS). As a result, the AI-based DR model showed a high recognition accuracy for depression. The HAMD-24 score in the A-MDD group (31.81 ± 5.39 points) was statistically higher than the score in the NA-MDD group (25.25 ± 5.02 points) (P < 0.05). No visible difference was found in ACTH levels of patients in different groups (P > 0.05). The incidence of cortisol rhythm disorder (CRD) in the A-MDD group was much higher (P < 0.05). The differences in TAS scores between the two groups were significantly statistically significant (P < 0.01). In conclusion, the AI-based DR Model achieves a more accurate identification of depression; depression with or without anxiety has different effects on the mental state of patients. CRD may be one of the biological markers of depression combined with anxiety

How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies

The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths

Genetic improvement of tocotrienol content enhances the oxidative stability of canola oil

BackgroundTocotrienols and tocopherols, which are synthesized in plastids of plant cells with similar functionalities, comprise vitamin E to serve as a potent lipid-soluble antioxidant in plants. The synthesis of tocopherols involves the condensation of homogentisic acid (HGA) and phytyl diphosphate (PDP) under the catalysis of homogentisate phytyltransferase (HPT). Tocotrienol synthesis is initiated by the condensation of HGA and geranylgeranyl diphosphate (GGDP) mediated by homogentisate geranylgeranyl transferase (HGGT). As one of the most important oil crops, canola seed is regarded as an ideal plant to efficiently improve the production of vitamin E tocochromanols through genetic engineering approaches. However, only a modest increase in tocopherol content has been achieved in canola seed to date.MethodsIn this study, we transformed barley HGGT (HvHGGT) into canola to improve total tocochromanol content in canola seeds.Results and discussionThe results showed that the total tocochromanol content in the transgenic canola seeds could be maximally increased by fourfold relative to that in wild-type canola seeds. Notably, no negative impact on important agronomic traits was observed in transgenic canola plants, indicating great application potential of the HvHGGT gene in enhancing tocochromanol content in canola in the future. Moreover, the oil extracted from the transgenic canola seeds exhibited significantly enhanced oxidative stability under high temperature in addition to the increase in total tocochromanol content, demonstrating multiple desirable properties of HvHGGT

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI

IntroductionThis study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia.MethodsA total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison.ResultsThe optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model’s predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics.DiscussionThe constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies

Molecular vasculogenic mimicry–Related signatures predict clinical outcomes and therapeutic responses in bladder cancer: Results from real-world cohorts

Bladder cancer (BLCA) is a heterogeneous disease, and there are many classical molecular subtypes that reflect tumor immune microenvironment (TME) heterogeneity but their clinical utility is limited and correct individual treatment and prognosis cannot be predicted based on them. To find reliable and effective biomarkers and tools for predicting patients’ clinical responses to several therapies, we developed a new systemic indicator of molecular vasculogenic mimicry (VM)–related genes mediated by molecular subtypes based on the Xiangya cohort and additional external BLCA cohorts using a random forest algorithm. A correlation was then done between the VM_Score and classical molecular subtypes, clinical outcomes, immunophenotypes, and treatment options for BLCA. With the VM_Score, it is possible to predict classical molecular subtypes, immunophenotypes, prognosis, and therapeutic potential of BLCA with high accuracy. The VM_Scores of high levels indicate a more anticancer immune response but a worse prognosis due to a more basal and inflammatory phenotype. The VM_Score was also found associated with low sensitivity to antiangiogenic and targeted therapies targeting the FGFR3, β-catenin, and PPAR-γ pathways but with high sensitivity to cancer immunotherapy, neoadjuvant chemotherapy, and radiotherapy. A number of aspects of BLCA biology were reflected in the VM_Score, providing new insights into precision medicine. Additionally, the VM_Score may be used as an indicator of pan-cancer immunotherapy response and prognosis

Evaluation of resistance of banana genotypes with AAB genome to Fusarium Wilt Tropical Race 4 in China

Banana cultivars with the AAB genome group comprise diverse subgroups, such as Plantain, Silk, Iholena, and Pisang Raja, among others, which play an important role in food security in many developing countries. Some of these cultivars are susceptible to Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4), the most destructive pathogen threatening banana production worldwide, and some of them are still largely unknown. We evaluated the resistance of 37 banana genotypes, including Plantain, Silk, Iholena, Maia Maoli/Popoulu, Pisang Raja, Pome, and Mysore, to Foc TR4 under both greenhouse and field conditions. Genotypes from the Silk and Iholena subgroups were highly susceptible to Foc TR4. Pome and Mysore showed resistance and intermediate resistance, respectively. However, Pisang Raja ranged from susceptible to intermediate resistance. One cultivar from the Maia Maoli/Popoulu subgroup was highly susceptible, while the other displayed significant resistance. Most Plantain cultivars exhibited high resistance to Foc TR4, except two French types of cultivar, 'Uganda Plantain' and 'Njombe N°2', which were susceptible. The susceptibility to Foc TR4 of some of the AAB genotypes evaluated, especially Plantain and other cooking bananas, indicates that growers dependent on these varieties need to be included as part of the prevention and integrated Foc TR4 management strategies, as these genotypes play a crucial role in food security and livelihoods