Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of neural populations and closely tied to cognitive and behavioral states. In contrast, aperiodic fluctuations exhibit a power-law decaying spectral trend, revealing the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in studying brain aperiodic dynamics. These studies demonstrate that aperiodic activity holds significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. Aperiodic activity serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent has emerged as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with activities at the levels of individual neurons, neuronal ensembles, and neural networks collectively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic signals. Aperiodic dynamics currently boasts broad application prospects. It not only provides a novel perspective for investigating brain neural dynamics but also holds immense potential as a neural marker in neuromodulation or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and based on these findings, elaborates on the research prospects of aperiodic dynamics.

Umbilical cord mesenchymal stem cells (UC-MSCs) have been widely used in regenerative medicine, but there is limited research on the stability of UC-MSCs formulation during production. This study aims to assess the stability of the cell stock solution and intermediate product throughout the production process, as well as the final product following reconstitution, in order to offer guidance for the manufacturing process and serve as a reference for formulation reconstitution methods. Three batches of cell formulation were produced and stored under low temperature (2-8 ℃) and room temperature (20-26 ℃) during cell stock solution and intermediate product stages. The storage time intervals for cell stock solution were 0, 2, 4, and 6 h, while for intermediate products, the intervals were 0, 1, 2, and 3 h. The evaluation items included visual inspection, viable cell concentration, cell viability, cell surface markers, lymphocyte proliferation inhibition rate, and sterility. Additionally, dilution and culture stability studies were performed after reconstitution of the cell product. The reconstitution diluents included 0.9% sodium chloride injection, 0.9% sodium chloride injection + 1% human serum albumin, and 0.9% sodium chloride injection + 2% human serum albumin, with dilution ratios of 10-fold and 40-fold. The storage time intervals after dilution were 0, 1, 2, 3, and 4 h. The reconstitution culture media included DMEM medium, DMEM + 2% platelet lysate, 0.9% sodium chloride injection, and 0.9% sodium chloride injection + 1% human serum albumin, and the culture duration was 24 h. The evaluation items were viable cell concentration and cell viability. The results showed that the cell stock solution remained stable for up to 6 h under both low temperature (2-8 ℃) and room temperature (20-26 ℃) conditions, while the intermediate product remained stable for up to 3 h under the same conditions. After formulation reconstitution, using sodium chloride injection diluted with 1% or 2% human serum albumin maintained a viability of over 80% within 4 h. It was observed that different dilution factors had an impact on cell viability. After formulation reconstitution, cultivation in medium with 2% platelet lysate resulted in a cell viability of over 80% after 24 h. In conclusion, the stability of cell stock solution within 6 h and intermediate product within 3 h meets the requirements. The addition of 1% or 2% human serum albumin in the reconstitution diluent can better protect the post-reconstitution cell viability.

Objective: To compare the relative effectiveness of different exercise modalities on inhibitory control in children with attention deficit hyperactivity disorder (ADHD), so as to provide an evidence based basis for the development of effective exercise prescriptions. Methods: The databases of China National Knowledge Internet (CNKI), Pubmed, Web of Science, Embase, and Cochrane were searched to screen the literature of randomized controlled trials of exercise interventions for inhibitory control in children with ADHD up to December 31, 2022. The Cochrane risk of bias assessment tool was used for methodological quality assessment, and Stata 17.0 software was used for network Meta analysis, standardized mean difference ( SMD ) and 95% CI were used as the effect indicators to compare the difference in effect between interventions and rank the effect. Results: Twenty two papers with a total of 1 134 participants aged 6-14.5 years were finally included. Network Meta analysis showed that the impact effects of physical and mental exercises [ SMD (95% CI )=1.08(0.50-1.66)], cognition+exercise [ SMD (95% CI ) =0.81(0.13-1.48)], and ball games [ SMD (95% CI )= 1.54(0.99-2.09)] were significantly superior to that of control group, and the ball games had a significantly better effect than single aerobic exercise [ SMD (95% CI )=1.02(0.20-1.84)], and combined exercises [ SMD (95% CI )=1.08( 0.28 -1.88)]( P < 0.05 ). The results of surface under the cumulative ranking (SUCRA) showed that ball games might be the best means to improve inhibitory control in children with ADHD(SUCRA=95.3). Conclusion It is recommended to appropriately increase ball sports in sports activities to more effectively improve the inhibitory control of children with ADHD.

ObjectiveHair is an essential skin appendage, primarily composed of keratins and keratin-associated proteins. The protein composition and proportion of hair samples vary among different races and sexes. Currently, there is a lack of efficient methods to extract hair proteins. This study aims to explore the application of quantitative hair proteomics in distinguishing individual hair characteristics. MethodsBased on the exploration of sample processing and lysis buffer using three hair samples, we developed a stable and efficient hair protein extraction method, named PLEE (PTM lab for protein extraction from hair with high efficiency). We used the PLEE method to extract seven human hair samples and performed proteomic experiments on them using in-gel digestion method to produce data for analyzing hair protein composition and proportion among individuals. ResultsA total of 274 proteins were identified, among which 107 proteins were commonly present, and the number of non-common proteins ranged from 57-119, with some samples having unique identification proteins. Using the 107 commonly identified proteins for quantitative protein fractionation analysis, various samples were distinguished by clustering and principal component analysis, and technical repeated samples were merged, indicating the stability of the process. In addition, 10 key proteins (KRT33A, KRTAP9-6, KRT83, KRTAP7-1, KRT32, BLMH, KRT38, KRTAP11-1, NPAS1, KRTAP4-3) with large differences between individuals and stable protein identification within the same individual were screened. ConclusionThe protein composition of hair varies among different individuals, and the 10 selected proteins are expected to be key proteins for distinguishing individual hair characteristics and have significant potential applications in individual identification and criminal investigation.

The growth of three plague phages from Qinghai Plateau in two Yersinia pestis strains(plague vaccine strains EV76 and 614F)and four non-Yersinia pestis strains(Yersinia pseudotuberculosis PTB3,PTB5,Escherichia coli V517,and Yersinia enterocolitica 52302-2)were detected through a micromethod based on the OmniLogTM microbial identification system and by the drop method,to provide a scientific basis for future ecological studies and classification based on the host range.For plague vaccine strains EV76 and 614F,successful phage infection and subsequent phage growth were observed in the host bacte-rium.Diminished bacterial growth and respiration and a concomitant decrease in color were observed with the OmniLogTM mi-crobial identification system at 33 ℃ for 48 h.Yersinia pseudotuberculosis PTB5 was sensitive to Yersinia pestis phage 476,but Yersinia pseudotuberculosis PST5 was insensitive to phage 087 and 072204.Three strains of non-Yersinia pestis(Yersinia pseudotuberculosis PTB3,Escherichia coli V517,and Yersinia enterocolitica 52302-2)were insensitive to Yersinia pestis pha-ges 087,072204,and 476 showed similar growth curves.The growth of phages 476 and 087,as determined with the drop method,in two Yersinia pestis strains(plague vaccine strains EV76 and 614F)and four non-Yersinia pestis strains(Yersinia pseudotuberculosis PTB3,Escherichia coli V517,and Yersin-ia enterocolitica 52302-2)showed the same results at 37 ℃,on the basis of comparisons with the OmniLogTM microbial i-dentification system;in contrast,phages 072204 did not show plaques on solid medium at 37 ℃ with plague vaccine strains EV76 and 614F.Determination based on the OmniLogTM detection system can be used as an alternative to the traditional determination of the host range,thus providing favorable application val-ue for determining the interaction between the phage and host bacteria.

The purpose of this study was to enrich the genomic information and provide a basis for further development and utilization of Polygonatum kingianum. The fresh rhizome of P. kingianum was used as the experimental material, and the full-length transcriptome was sequenced by PacBio Sequel platform. The final measured polymerase reads were 1 120 485, with a total of 77.73 GB of data. In NR database, 41 864 homologous sequence alignments were aligned to 5 species; 40 506 were annotated in the KOG database and classified into 26 categories based on their functionality; 69 060 GO annotated 32 functional groups divided into 3 categories: cellular components, molecular functions, and biological processes; 45 779 annotations were added to 145 metabolic pathways in the KEGG database. Among them, there are 127 identified transcripts related to polysaccharide synthesis in the glycolysis/gluconeogenesis metabolic pathway, 144 in the starch and sucrose metabolic pathway, 85 in the amino acid sugar and nucleotide sugar metabolic pathway, and 69 in the fructose and mannose metabolic pathway. In addition, 1 781 transcription factors were detected, distributed in 37 transcription factor families; 180 293 SSR loci were detected, among which single base repeats accounted for the most, accounting for 30.48% of all base repeats, and at least five base repeats, accounting for only 0.14% of single base repeats. The results of this study provide important data supporting for enriching the genomic information of P. kingianum and exploring its effective biosynthetic pathway.

Damarane-type triterpene saponins are the main active ingredients in Gynostemma pentaphyllum (Thunb.) Makino. By using D101 macroporous adsorption resin and silica gel open-columns, C18 medium-pressure column chromatography, and preparative high performance liquid chromatography, we isolated four compounds from the leaves of G. pentaphyllum planted in Guangxi Zhuang Autonomous Region. Their structures were determined by comprehensive analyses of MS, NMR data and circular dichroism spectroscopy and identified as (3β,12β)-dihydroxy-25-peroxyhydrohydrodammarane-20,23-diene-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (1), (3β,12β,24S)-trihydroxydammarane-20,25-diene-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (2), (3β,20α)-dihydroxy-24-en-12β,22S-epoxydammarane-3-O-[β-D-glucopyranosyl(1→2)]-β-D-glucopyranoside (3), (3β,12β,20S)-trihydroxydammarane-24-en-3-O-[6-O-acetyl-β-D-glucopyranosyl(1→2)-β-D-glucopyranosyl]-20-O-β-D-xylopyranosyl(1→3)-α-L-rhamnopyranosyl (1→6)-β-D-glucopyranoside (4). Compounds 1-4 are new dammarane-type triterpene saponins.