Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

COVID-19 Related Research by Data Mining in Single Cell Transcriptome Profiles

The outbreak of coronavirus disease 2019(COVID-2019)has drawn public attention all over the world.As a newly emerging area,single cell sequencing also exerts its power in the battle over the epidemic.In this review,the up-to-date knowledge of COVID-19 and its receptor is summarized,followed by a collection of the mining of single cell transcriptome profiling data for the information in aspects of the vulnerable cell types in humans and the potential mechanisms of the disease.

Bayesian Decision Analysis for Recurrent Cervical Cancer

Risk modeling for recurrent cervical cancer requires the development of new concepts and methodologies. Unlike most daily decisions, many medical decision making have substantial consequences, and involve important uncertainties and trade-offs. The uncertainties may be about the accuracy of available diagnostic tests, the natural history of the cervical cancer, the effects of treatment in a patient or the effects of an intervention in a group or population as a whole. With such complex decisions, it can be difficult to comprehend all options “in our heads”. This study applied Bayesian decision analysis to an inferential problem of recurrent cervical cancer in survival analysis. A formulation is considered where individual was expected to experience repeated events, along with concomitant variables. In addition, the sampling distribution of the observations is modelled through a proportional intensity Nonhomogeneous Poisson process. The proposed decision models can provide decision support techniques not only for taking action in the light of all available relevant information, but also for minimizing expected loss. The decision process is useful in selecting the best alternative when a patient with recurrent cervical cancer, in particular, the proposed decision process can provide more realistic solutions.

In vivo noninvasive study of diabetic peripheral neuropathy plantar morphology and mechanical properties based on ultrasound

To the Editor:The incidence of diabetes complications increases with the increase in diabetes incidence.Diabetic peripheral neuropathy(DPN)is one of the most common complications in diabetes patients.DPN is very harmful and is an important cause of diabetic foot and amputation.The amputation rate of DPN patients is 10-20 times higher than that of nondiabetic patients.Every 30 s,somewhere in the world,a patient’s lower limb or part of a lower limb will be surgically amputated due to DPN.[1]DPN seriously interferes with the physical and mental health of patients.At the same time,the high cost of treatment and rehabilitation care also causes huge economic pressure on patients.Therefore,it is particularly important to identify,diagnose,and prevent DPN in the early or ultra-early stage.

RF-PSSM:A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

The identification of hepatitis C virus(HCV)virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets.An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases,facilitating studies based on computational methods.In this study,we proposed a new computational approach,rotation forest position-specific scoring matrix(RF-PSSM),to predict the interactions among HCV and human proteins.In particular,PSSM was used to characterize each protein,two-dimensional principal component analysis(2DPCA)was then adopted for feature extraction of PSSM.Finally,rotation forest(RF)was used to implement classification.The results of various ablation experiments show that on independent datasets,the accuracy and area under curve(AUC)value of RF-PSSM can reach 93.74% and 94.29%,respectively,outperforming almost all cutting-edge research.In addition,we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1,which can provide theoretical guidance for future experimental studies.

The correlation of drug resistance and virulence in Mycobacterium tuberculosis

The low success rates in the treatment of multidrug-resistant tuberculosis(MDR-TB)and extensively drug-resistant TB(XDR-TB),which account for 55%and 34%respectively,led the WHO to conclude that MDR/XDR-TB is a serious public health crisis.However,the virulence of MDR/XDR-Mycobacterium Tuberculosis(Mtb)has not been analyzed in details,which could provide a specific guidance for the control and prevention.In this review,we discuss different aspects of MDR/XDR-Mtb virulence and its relationship to fitness cost by probing the following questions:(1)what mediates the virulence of MDR/XDR-Mtb?(What is the relationship between fitness and virulence of Mtb?(2)Is it possible that drug-resistant Mtb(DR Mtb)can show higher fitness?(3)What is the definite effect on fitness of each drug-resistant mutant?(4)What other important factors affecting fitness in the mutant strain?(5)How to study the virulence of a large number of DR Mtb?And what prevention and control measures will be taken in the future,especially for the high virulent DR Mtb?We therefore summarized the congruent relationship between drug resistance and fitness from the global response-related genes to antibiotic resistance-contributing mutation,provided methods to explore the virulence of DR Mtb.This review may offer some critical information and concise guide to creating strategies for the prevention and control of drug-resistant Mtb.

MAML^(2):meta reinforcement learning via meta-learning for task categories

Meta-learning has been widely applied to solving few-shot reinforcement learning problems,where we hope to obtain an agent that can learn quickly in a new task.However,these algorithms often ignore some isolated tasks in pursuit of the average performance,which may result in negative adaptation in these isolated tasks,and they usually need sufficient learning in a stationary task distribution.In this paper,our algorithm presents a hierarchical framework of double meta-learning,and the whole framework includes classification,meta-learning,and re-adaptation.Firstly,in the classification process,we classify tasks into several task subsets,considered as some categories of tasks,by learned parameters of each task,which can separate out some isolated tasks thereafter.Secondly,in the meta-learning process,we learn category parameters in all subsets via meta-learning.Simultaneously,based on the gradient of each category parameter in each subset,we use meta-learning again to learn a new metaparameter related to the whole task set,which can be used as an initial parameter for the new task.Finally,in the re-adaption process,we adapt the parameter of the new task with two steps,by the meta-parameter and the appropriate category parameter successively.Experimentally,we demonstrate our algorithm prevents the agent from negative adaptation without losing the average performance for the whole task set.Additionally,our algorithm presents a more rapid adaptation process within readaptation.Moreover,we show the good performance of our algorithm with fewer samples as the agent is exposed to an online meta-learning setting.