Identification of Specific Abnormal Brain Functional Activity and Connectivity in Cancer Pain Patients: A Preliminary Resting-State fMRI Study.

Objective: This study investigates the differences in brain functional activity and connectivity patterns between Cancer Pain (CP) patients and Healthy Controls (HCs) using resting-state functional magnetic resonance imaging (rs-fMRI) to identify potential neuroimaging biomarkers.

Methods: This study collected rs-fMRI data from 25 CP patients and 25 hCs, processed the functional MRI images, and calculated metrics such as amplitude of low-frequency fluctuation (ALFF), Regional Homogeneity (ReHo), and FC. Through statistical analysis, differences in brain functional activity and connectivity between the cancer pain group and the healthy control group were investigated, followed by machine learning classification.

Construction of scalable multi-channel DNA nanoplatform for the combined detection of ctDNA biomarkers of ovarian cancer.

Single-level biomarker detection has the limitation of insufficient accuracy in cancer diagnosis. Therefore, the strategy of developing highly sensitive, multi-channel biosensors for high-throughput ctDNA determination is critical to improve the accuracy of early diagnosis of clinical tumors. Herein, in order to achieve efficient detection of up to ten targets for early diagnosis of ovarian cancer, a DNA-nanoswitch-based multi-channel (DNA-NSMC) biosensor was built based on the multi-module catalytic hairpin assembly-mediated signal amplification (CHA) and toehold-mediated DNA strand displacement (TDSD) reaction.

Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features.

Prediction of glioma is crucial to provide a precise treatment plan to optimize the prognosis of children with glioma. However, studies on the grading of pediatric gliomas using radiomics are limited. Meanwhile, existing methods are mainly based on only radiomics features, ignoring intuitive information about tumor morphology on traditional imaging features.

Automated detection of MRI-negative temporal lobe epilepsy with ROI-based morphometric features and machine learning.

Objective: Temporal lobe epilepsy (TLE) predominantly originates from the anteromedial basal region of the temporal lobe, and its prognosis is generally favorable following surgical intervention. However, TLE often appears negative in magnetic resonance imaging (MRI), making it difficult to quantitatively diagnose the condition solely based on clinical symptoms. There is a pressing need for a quantitative, automated method for detecting TLE.

Construction of a scalable DNA computing nano-system for large-scale and complex logical operations.

The predictability of Watson-Crick base pairing provides unique structural programmability to DNA, facilitating the development and application of biomolecules in biocomputing. However, in DNA-based biocomputing, the scale of operation that can be achieved by an existing reaction system is very limited. How to expand the operation range of a logic circuit and realize the integration and extensibility of circuits is always the key problem to be solved in this field.

Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information.

Type 2 diabetes mellitus (T2DM) patients may develop into mild cognitive impairment (MCI) or even dementia. However, there is lack of reliable machine learning model for detection MCI in T2DM patients based on machine learning method. In addition, the brain network changes associated with MCI have not been studied.

Correlation between parameters related to sarcopenia and gray matter volume in patients with mild to moderate Alzheimer's disease.

Background: Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain atrophy and closely correlated with sarcopenia. Mounting studies indicate that parameters related to sarcopenia are associated with AD, but some results show inconsistent. Furthermore, the association between the parameters related to sarcopenia and gray matter volume (GMV) has rarely been explored.

Self-Paced Learning and Privileged Information based Cascaded Multi-column Classification algorithm for ASD diagnosis.

Autism spectrum disorder (ASD) is one of the most serious mental disorder in children. Machine learning based computer aided diagnosis (CAD) on resting-state functional magnetic resonance imaging (rs-fMRI) for ASD has attracted widespread attention. In recent years, learning using privileged information (LUPI), a supervised transfer learning method, has been generally used on multi-modality cases, which can transfer knowledge from source domain to target domain in order to improve the prediction capability on the target domain.

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease.

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance.

Theoretical insight on electronic structure and photophysical properties of a series of cyclometalated iridium(III) complexes bearing the substituted phenylpyrazole with different electron-donating or electron-accepting groups.

By using the density functional theory (DFT) and time-dependent density functional theory (TDDFT), the electronic structure and photophysical properties of a series of cyclometalated iridium(III) complexes bearing the substituted phenylpyrazole have been theoretically investigated. All studied iridium(III) complexes have the distorted octahedral geometry with cis-C,C, cis-O,O, and trans-N,N chelate disposition. The lowest lying singlet → singlet absorptions of all studied iridium(III) complexes are respectively located at 405 nm, 387 nm, 382 nm, 370 nm, and 387 nm.

Improving MRI-based analysis of brain structural changes in patients with hypertension via a privileged information learning algorithm.

Hypertension can lead to changes in the brain structure and function, and different blood pressure levels (2017ACC/AHA) have different effects on brain structure. It is important to analyze these changes by machine learning methods, and various characteristics can provide rich information for the analysis of these changes. However, multiple feature extraction involves complex data processing.

Generation of Transgenic Cloned Buffalo Embryos Harboring the EGFP Gene in the Y Chromosome Using CRISPR/Cas9-Mediated Targeted Integration.

Sex control technology is of great significance in the production of domestic animals, especially for rapidly breeding water buffalo (bubalus bubalis), which served as a research model in the present study. We have confirmed that a fluorescence protein integrated into the Y chromosome is fit for sexing pre-implantation embryos in the mouse. Firstly, we optimized the efficiency of targeted integration of exogenous gene encoding enhanced green fluorescent protein (eGFP) and mCherry in Neuro-2a cells, mouse embryonic stem cells, mouse embryonic cells (NIH3T3), buffalo fetal fibroblast (BFF) cells.

Maternal transcription profiles at different stages for the development of early embryo in buffalo.

Maternal mRNAs deposited in the egg during oogenesis are critical during the development of early embryo, before the activation of the embryonic genome. However, there is little known about the dynamic expression of maternally expressed genes in mammals. In this study, we made buffalo parthenogenesis as our research model to analyse maternal transcription profiles of pre-implantation embryo in buffalo using RNA sequencing.

Global transcriptome analysis of different stages of preimplantation embryo development in river buffalo.

Background: Water buffalo () are divided into river buffalo and swamp buffalo subspecies and are essential livestock for agriculture and the local economy. Studies on buffalo reproduction have primarily focused on optimal fertility and embryonic mortality. There is currently limited knowledge on buffalo embryonic development, especially during the preimplantation period.

Four novel polymorphisms of buffalo INSIG2 gene are associated with milk production traits in Chinese buffaloes.

Insulin-induced genes (INSIGs), including INSIG1 and INSIG2, are important mediators that play a pivotal role in the lipid metabolism and could cause the retention of the SCAP/SREBP complex. Therefore, the objective of this study is to detect the single nucleotide polymorphisms (SNPs) of buffalo INSIG2 gene and evaluate their associations with milk production traits in Chinese buffaloes. A total of four SNPs (g.

De Novo Transcriptome Assembly of the Chinese Swamp Buffalo by RNA Sequencing and SSR Marker Discovery.

The Chinese swamp buffalo (Bubalis bubalis) is vital to the lives of small farmers and has tremendous economic importance. However, a lack of genomic information has hampered research on augmenting marker assisted breeding programs in this species. Thus, a high-throughput transcriptomic sequencing of B.

Molecular cloning and expression analysis of the STAT1 gene in the water buffalo (Bubalus bubalis).

Signal transducer and activator of transcription 1 (STAT1) is a critical component of the transcription factor complex in the interferon (IFN) signaling pathways. Of the seven STAT isoforms, STAT1 is a key mediator of type I and type III IFN signaling, but limited information is available for the STAT genes in the water buffalo. Here, we amplified and identified the complete coding sequence (CDS) of the buffalo STAT1 gene by using reverse transcription polymerase chain reaction (RT-PCR).

[Design and implementation of pulse instrument based on DSP].

The Traditional Chinese Medical Pulse Instrument uses the HKG-07B infrared pulse sensor to get pulse signal from the body. It makes full use of the TMS320VC5402 chip to realize time-frequency domain parameters extracting, classification and identification of the pulse signal. The system can store a plenty of pulse signal and realize data communication with the PC via the USB interface.

Evaluation of fasting metabolism of growing water buffalo (Bubalus, Bubalis).

The objectives of the present study were to evaluate fasting metabolism (FM) of water buffalo (Bubalus, Bubalis) at three stages of growth (12, 18 and 24 months) in Guangxi, China. Five female water buffalo were used for each age group and their live weight was on average 254, 326 and 338 kg, respectively. All animals were of average body condition, healthy and de-wormed before start of the study.

Pregnancy and calving rates following transfer of in-vitro-produced river and F1 (river x swamp) buffalo (Bubalus bubalis) embryos in recipients on natural oestrus or synchronised for ovulation.

The main objective of this study was to compare pregnancy and calving rates following transfer of in-vitro-produced fresh river and F1 (river x swamp) buffalo embryos in recipients synchronised by Ovsynch protocol or following natural oestrus. River embryos were produced from cumulus-oocyte complexes (COCs) derived by ovum pick up (OPU) on 40 Murrah and Nili-Ravi donor buffaloes over a twice-weekly collection schedule for 120 single OPUs. F1 embryos were produced by fertilisation of swamp COCs recovered from abattoir ovaries coincubated with river sperm cells.