Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

OBJECTIVE To explore the construction of selection system for drug directory of the county-level medical community based on multi-criteria decision analysis， and provide decision-making basis for the selection of drug directory of medical community. METHODS Taking county-level medical community in Chongqing as an example，Delphi method and analytic hierarchy process were employed to construct the selection system for drug directory of the county-level medical community. Selected drugs were quantitatively scored based on the constructed index system， and the drug directory was selected according to the drug’s comprehensive score. The implementation effect of the directory was then evaluated through questionnaire surveys one year after the implementation of the directory. RESULTS The expert authority coefficients of the two rounds of consultation were＞ 0.8， with Kendall’s W values of 0.213 and 0.196， respectively （P＜0.001）. Finally， the selection system for drug directory of the medical community was determined to include five evaluation dimensions： safety， effectiveness， economy， accessibility， and innovation， along with eight evaluation indicators. In the drug directory selected according to the above method， the proportions of centrally procured drugs， medical insurance drugs， and essential drugs had all increased compared to before the selection； the comprehensive scores of chemical drugs ranged from 50.25 to 96.31 scores， and the proportion of drugs scoring between 70 and 100 scores had increased from 78.06% before selection to 85.82%. Among them， antiparasitic drugs had the highest comprehensive scores， while drugs for the digestive tract and metabolism were the most numerous. The evaluation scores of each indicator and the comprehensive scores of drugs in the drug directory after the selection process increased significantly than before selection （P＜ 0.05）. CONCLUSIONS The selection system for drug directory of the county-level medical community constructed in this study is scientific， objective and operable. This process facilitates the promotion of standardized and unified management of drugs in the medical community.

Objective To understand the situation of dental cone beam computed tomography (CBCT) quality control testing phantoms in radiation health technical service institutions in Guangdong province, analyze the differences among different phantoms, and provide a reference for dental CBCT quality control testing. Methods The testing phantoms of 49 radiation health technical service institutions were used as the research objects. The designated CBCT equipment was used for scanning and imaging. The Z-score method was used to evaluate the high-contrast resolution, low-contrast resolution, and distance measurement deviation of each phantom. Results The satisfaction rates of various items for the phantoms in 49 institutions ranged from 85.7% to 100%. The distance measurement deviations of four institutions were “suspicious”, and the high-contrast resolution of four institutions and the distance measurement deviation of one institution were “unsatisfactory”. Conclusion The overall performance of dental CBCT quality control testing phantoms in radiological health technical service institutions in Guangdong province is satisfactory. However, there are still some phantoms with poor results in items such as distance measurement deviation and high-contrast resolution. The structural design, material selection, and manufacturing process of the phantom may all affect the results of quality control testing. Therefore, appropriate phantoms, optimized exposure conditions, and suitable reconstruction algorithms should be used in CBCT quality control testing to ensure accurate and reliable measurements.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective　To analyze and study the status quo and deficiencies in the construction of talent teams at all levels of CDCs in Hainan Province and put forward countermeasures to provide a reference basis for the high-quality development of talent team construction in all levels of CDCs in Hainan Province. Methods Using field surveys and data retrieval, spatial analysis was employed to compare the overall differences in human resource status of the provincial-level CDC and CDCs in five regional areas (East, West, South, North, and Central Hainan). The coordination between human resource allocation and development strategies was analyzed. A descriptive analysis mainly utilized CDC survey questionnaires and other research forms to explore the health human resources of the province's CDCs. Results The professional and technical personnel in the provincial CDCs comprise 1 431 individuals, accounting for 82.3% (1 431/1 739) of the total number of actual employees, which falls short of the Central Institutional Reform Commission's (CIRC) mandate that technical professionals comprise at least 85% of the total workforce (CIRC Document [2014] No. 2). Among Hainan's CDC personnel, 115 individuals are recognized as high-level talents within the Hainan Free Trade Port framework. These include one Class C talent, 22 Class D talents, and 93 Class E talents. Class A, B, and C-level talents are deficient. The majority of staff at both the provincial and regional CDCs hold bachelor's degrees. There is a significant proportion of staff with associate degrees or lower qualifications, coupled with a severe shortage of highly educated personnel. Postgraduates with master’s degrees or above account for 27.8% (65/233) in the provincial CDC, indicating low educational credentials among personnel in Hainan's CDCs. The central region, characterized by slower economic and social development, faces greater challenges in attracting and retaining high-level talent. There is a scarcity of public health professionals with interdisciplinary expertise. Some public health staff lack clinical knowledge, experience, and skills in disease treatment. Furthermore, there is a need to strengthen on-site emergency response capabilities for public health emergencies. The structural ratio of senior, intermediate, and junior professional and technical positions in the provincial CDC is 40%∶45%∶15%. The position settings are limited to ranking levels without distinction by professional category, leading to a bottleneck-type competition like crossing the "one log bridge" for technical position promotions. Conclusion　Hainan Province faces significant challenges in developing its public health workforce, both in technical expertise and management capacity. Especially under the context of the closure operation of the Hainan Free Trade Port, it is necessary to continuously strengthen top-level talent design to cultivate a favorable policy, system, and cultural environment, thereby promoting the sustained and healthy development of the province's public health career.

Phosphatidylinositol 4 kinases are the initial and key molecules of the phosphatidyl inositol signaling pathway.Among them,the phosphatidylinositol 4-kinaseⅢ β(PI4KⅢβ)is in-volved in the synthesis of the Golgi PI4P pool,playing a vital role in numerous physiological processes.Meanwhile,the en-zyme is an important host factor mediating the replication of some pathogenic RNA viruses,and participating in other patho-logical processes such as bacterial infection and malaria.In ad-dition,studies have shown that the function of PI4KⅢβ is regu-lated by numerous factors,including host and viral protein bind-ing partners.This review will discuss the structure and the phys-iopathology regulatory mechanism of PI4KⅢβ.

Panax notoginseng is the dried root and rhizome of Panax notoginseng(Burk.)F.H.Chen,a perennial erect herb of the genus Ginseng of the family Wujiaceae.As a traditional Chinese medicine in our country,Panax notoginseng has a good tonic effect,and the Dictionary of Traditional Chinese Medicines has the words that Panax notoginseng is used to tonify the blood,remove the blood stasis and damage,and stop epistaxis.It can also be used to pass the blood and tonify the blood with the best efficacy,and it is the most precious one of the prescription med-icines.Eaten raw,it removes blood stasis and generates new blood,subdues swelling and stabilizes pain,stops bleeding with-out leaving stasis,and promotes blood circulation without hurting the new blood;taken cooked,it can be used to replenish and strengthen the body.Notoginsenoside R1 is a characteristic com-pound in the total saponin of Panax ginseng.In recent years,China's aging has been increasing,and the incidence of neuro-logical disorders has been increasing year by year.Meanwhile,reports on notoginsenoside R1 in the treatment of neurological disorders are increasing,and its neuroprotective effects have been exerted with precise efficacy.The purpose of this paper is to review the treatment of neurological diseases and the mecha-nism of action of notoginsenoside R1,so as to provide a certain theoretical basis for clinical use and new drug development.

This study was aimed at characterizing the variations in hemagglutinin(HA)and neuraminidase(NA)genes of influenza virus subtype A(H1N1)pdm09 isolated during the 2022-2023 influenza monitoring year in Shandong Province,to provide a scientific basis for influenza prevention and control.A total of 14 A(H1N1)pdm09 subtype influenza strains were se-lected randomly by city by the influenza monitoring network laboratory.The vaccine strains recommended by the WHO served as references for whole gene sequencing analysis.A fluorescence method was used to conduct neuraminidase inhibition experi-ments to evaluate drug sensitivity.The A(H1N1)pdm09 influenza virus in Shandong Province,2022-2023 belonged to the 5a.2a evolutionary cluster in the 6B.1A branch.Nucleotide sequence analysis indicated that the HA and NA genes were closely re-lated to the Northern Hemisphere vaccine strain A/Victoria/2570/2019 in the years 2021-2023,and showed homology of 98.5％to 98.7％and 98.8％to 99.1％,respectively.Amino acid sequence analysis revealed 20 amino acid sequence mutations in the HA protein,but only one virus strain was found to have antigen drift,and three virus strains showed loss of HA protein glycosylation sites.No mutations were found at important sites affecting NA enzymes.The neuraminidase inhibition experiment indicated viral sensitivity to anti-influenza drugs.In conclusion,the monitored virus strains had high overall homology with vac-cine strains but showed some amino acid variation.In the future,continued monitoring of the genetic variation characteristics of influenza viruses will be necessary to understand the risk of influenza epidemics,and the effectiveness of influenza vaccines and therapeutic drugs.

This study was aimed at understanding the pollution distribution pattern of avian influenza virus in the environ-ment in poultry related places in Shandong Province,to provide a scientific basis for the prevention,control,prediction,and early warning regarding human infection with avian influenza.From 2020 to 2023,6 523 environmental samples were collected in 16 cities in Shandong Province from four types of poultry-related places.Fluorescence quantitative PCR was used for nucleic acid testing of influenza A virus.Positive samples were further identified for the H5,H7,and H9 subtypes of avian influenza virus.The epidemiological characteristics of avian influenza viruses in the poultry related environment of Shandong Province were described,and inter-rate comparisons were performed with the x2 test.During 2020-2023,6 523 environmental samples were collected,and 1 007 cases positive for avian influenza virus were detected,with a positivity rate of 15.44％.H5,H7,and H9 subtypesand mixed infections were detected.H9 was the main subtype(88.48％)in positive specimens.A significant difference in positivity rates was observed among regions(x2=431.956,P＜0.001),and the highest positivity rate was 28.93％.Significant differences in positivity rates were observed among monitoring sites(x2=304.604,P＜0.001),sample types(x2=109.678,P＜0.001),and quarters(x2=64.963,P＜0.001).The positive detection rate was highest at monitoring sites in urban and rural live poultry markets(20.12％),and the positive detection rate of samples collected by wiping meat cut-ting board surfaces was higher than that of other samples(22.56％).The peak positive detection rate occurred in spring(20.31％).Widespread contamination with avian influenza virus was observed in poultry environments in Shandong Prov-ince.The H9 subtype,the main pathogen,coexisted with H5 and H7 subtypes,thus posing a risk of human infection with avian influenza.Therefore,prevention and control of avian influenza must be strengthened in key seasons,areas,places,and links.

Objective:To evaluate the clinical characteristics and risk factors of infections occurring during hospitalization in patients with multiple myeloma (MM) treated with new generation therapies (including immuno-modulatory drugs,proteasome inhibitors and monoclonal antibodies).Methods:The clinical data were collected from 155 patients with multiple myeloma who were treated in Shanxi Bethune Hospital from March,2017 to March,2022 and were retrospectively analyzed.For this study,the following therapies were considered to be new generation therapies:lenalidomide,pomadomide,bortezomib,ixazomib,daratumumab. The clinical characteristics and risk factors of infection were analyzed.Results:A total of 155 patients were included in this study.The median follow-up time was 20 months.Of 155 patients with MM,242 infection episodes were identified.Among the 242 infections,the incidence of clinically defined infection (CDI)was the highest (186,76.86％),followed by microbiologically defined infection (MDI)in 50 cases (20.66％),and fever at unknown focus (FUF)in 6 cases (2.48％).35 cases (14.46％)of bacteria,10 cases (4.13％)of viruses,and 5 cases (2. 07％)of fungi were clearly infected.The most common site of infection was the lower respiratory tract in 90 cases (37.19％),the upper respiratory tract in 83 cases (34.30％),and the digestive tract in 27 cases (11.16％).All-cause mortality was 8.39％(13/155).In univariate analysis,there was a higher correlation between ISS stage Ⅲ,the number of treatment lines ≥2,frail and infected patients with multiple myeloma.In multivariate analysis,ISS stage Ⅲ(OR=2.96,95％CI:1.19-7.40,P=0.02),the number of treatment lines ≥2 (OR=2.91,95％CI:1.13-7.51,P=0.03)and frail (OR=3.58,95％CI:1.44-8.89,P=0. 01)were risk factors for infection in patients with multiple myeloma in the era of new drugs.Conclusion:Patients with multiple myeloma treated with new agents are prone to bacterial infection during hospitalization.ISS stage Ⅲ,lines of therapy(≥2)and frail were associated with high risk for infection.