To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive neuromodulation technique with the potential to precisely target deep brain structures. This study explores the neural and behavioral effects of tTIS on the superior colliculus (SC), a region involved in eye movement control, in mice. Computational modeling revealed that tTIS delivers more focused stimulation to the SC than traditional transcranial alternating current stimulation. In vivo experiments, including Ca²⁺ signal recordings and eye movement tracking, showed that tTIS effectively modulates SC neural activity and induces eye movements. A significant correlation was found between stimulation frequency and saccade frequency, suggesting direct tTIS-induced modulation of SC activity. These results demonstrate the precision of tTIS in targeting deep brain regions and regulating eye movements, highlighting its potential for neuroscientific research and therapeutic applications.
Animals ; Superior Colliculi/physiology* ; Transcranial Direct Current Stimulation/methods* ; Eye Movements/physiology* ; Male ; Mice ; Mice, Inbred C57BL

OBJECTIVE: CRISPR-Cas protects bacteria from exogenous DNA invasion and is associated with bacterial biofilm formation and pathogenicity. METHODS: We analyzed the type I-F CRISPR-Cas system of Legionella pneumophila WX48, including Cas1, Cas2-Cas3, Csy1, Csy2, Csy3, and Cas6f, along with downstream CRISPR arrays. We explored the effects of the CRISPR-Cas system on the in vitro growth, biofilm-forming ability, and pathogenicity of L. pneumophila through constructing gene deletion mutants. RESULTS: The type I-F CRISPR-Cas system did not affect the in vitro growth of wild-type or mutant strains. The biofilm formation and intracellular proliferation of the mutant strains were weaker than those of the wild type owing to the regulation of type IV pili and Dot/Icm type IV secretion systems. In particular, Cas6f deletion strongly inhibited these processes. CONCLUSION The type I-F CRISPR-Cas system may reduce biofilm formation and intracellular proliferation in L. pneumophila.
Legionella pneumophila/pathogenicity* ; CRISPR-Cas Systems ; Biofilms/growth & development* ; Phenotype ; Bacterial Proteins/metabolism* ; Gene Deletion

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

Objective:To construct the sphingosine kinase 1(SPHK1)overexpression lentiviral vector,and to establish the SKOV3 lentiviral stable transfection cell line.Methods:According to the SPHK1 data information provided by the National Center for Biotechnology Information(NCBI)database,the primers were designed and synthesized,the target gene was amplified,and connected to the GV492 plasmid treated with Bam HⅠ and AgeⅠ restriction enzymes to construct the SPHK1 overexpression lentiviral vector;the positive clones were selected for PCR and sequencing identification;the lentiviral plasmid and the lentiviral packaging auxiliary plasmid were co-transfected into the HEK-293T cells for packaging and titer determination;according to the measured optimal multiplicity of infection(MOI)of 10,the corresponding lentiviral amounts in various groups were transfected into the SKOV3 cells,and the SKOV3 cells were divided into blank group(without treatment),GV492 control group(GV492 control lentivirus infected SKOV3 cells),and GV492-SPHK1 overexpression group(GV492-SPHK1 overexpression lentivirus infected SKOV3 cells,ov-SPHK1 group);the optimal concentration of 2 mg·L-1 puromycin was used to screen the stably transfected SKOV3 cell line;after 48 h,the medium was changed and replaced with 1 mg·L-1 puromycin for screening for 14 d;the morphology and fluorescence expression of the cells were observed under fluorescence microscope;real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the expression levels of SPHK1 mRNA in the SKOV3 cells in various groups;Western blotting method was used to detect the expression level of SPHK1 protein in the SKOV3 cells in various groups.Results:The PCR sequencing results showed that the gene sequence of the SPHK1 overexpression lentiviral vector was completely consistent with the target sequence,and the SPHK1 overexpression lentiviral vector was successfully constructed;the titer determination results showed that the lentiviral titers in GV492 control group and ov-SPHK1 group were 5×1011 and 8×1011 TU·L?1,respectively;the SKOV3 cells in GV492 control group and ov-SPHK1 group were in good state and showed strong fluorescence expression,suggesting that the SKOV3 stable transfection cell line overexpressing SPHK1 was successfully established;the RT-qPCR results showed that compared with blank group and GV492 control group,the expression level of SPHK1 mRNA in the SKOV3 cells in ov-SPHK1 group was significantly increased(P<0.01);the Western blotting results showed that compared with blank group and GV492 control group,the expression level of SPHK1 protein in the SKOV3 cells in ov-SPHK1 group was significantly increased(P<0.01).Conclusion:The SPHK1 overexpression lentiviral vector is successfully constructed,and the SKOV3 stable transfection cell line is established.

Objective To explore the efficacy and safety of pegylated doxorubicin liposome(PLD)in neoadjuvant chemotherapy(NAC)for breast cancer.Methods A total of 126 breast cancer patients treated with the NAC regimen containing PLD in the Third Xiangya Hospital of Central South University from Janu-ary 2018 to December 2022 were selected as the research subjects.The clinical efficacy,pathological efficacy and occurrence of adverse reactions of the patients were observed,and total pathological complete response(tpCR)rates of different molecular subtypes were analyzed.The changes in left ventricular ejection fraction(LVEF)were compared between before and after treatment.Results Among 126 patients,the clinical efficacy of NAC in 119 cases was evaluated,and the objective response rate(ORR)was 67.2％(80/119).A total of 123 patients were evaluated for the pathological efficacy,breast cancer pathological complete response(bpCR)rate was 13.8％(17/123);tpCR rate was 12.2％(15/123),in which the the human epidermal growth factor receptor 2(HER2)positive[hormone receptor(HR)negative]type had the highest rate(33.3％),followed by triple-negative(21.7％).There was no hand-foot syndrome occurred,and only one case developed the grade 2 oral mucositis.LVEF showed no statistically significant difference between before and after treatment[69.9％(51.0％,87.0％)vs.69.0％(57.0％,85.0％)](P＞0.05).Conclusion The NAC regimen containing PLD has a better effect on HER2—positive and triple-negative breast cancer.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Diabetic kidney disease （DKD） is the main cause of end-stage renal disease. Its high prevalence， mortality rate， and medical cost bring a heavy economic burden to society and families， and DKD has become one of the most important public health problems. Intestinal microecology is the most important and complex micro-ecosystem in the human body， which is involved in important life activities such as material and energy metabolism， immune system regulation， and signal transduction， thereby maintaining the dynamic balance of the human internal environment. The dynamic balance between the intestinal microecology and the body is essentially a Yin-Yang balance. Once this balance is broken， intestinal microbiota imbalance， intestinal mucosal barrier damage， immune dysfunction， and reduction of metabolite short-chain fatty acids （SCFAs） will occur， which play an important role in the progression of DKD. From the perspective of the Yin-Yang theory of traditional Chinese medicine （TCM）， the imbalance of intestinal microecology in DKD is equivalent to the excessive or insufficient constraint of Yin and Yang， or Yin deficiency affecting Yang， or Yang deficiency affecting Yin， or waning and waxing of Yin and Yang. For different pathogenesis changes， "Yin disease treated through Yang"， "treating Yin for Yang"， or "treating Yang for Yin" methods are adopted to regulate intestinal microbiota， inhibit immune inflammation， protect intestinal mucosal barrier， and increase SCFAs through TCM， thereby reconciling Yin and Yang to achieve the condition where "Yin is at peace and Yang is compact". Based on the Yin-Yang theory， this paper intended to interpret the scientific connotation of TCM in the treatment of DKD with intestinal microecology as the target and TCM in the treatment of DKD by regulating intestinal microecology as the breakthrough point to provide a novel insight for the occurrence and development of DKD and the mechanism of TCM.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Objective:To explore the value of salivary gland imaging based on deep learning and Delta radiomics in assessing salivary gland injury after 131I treatment in post-thyroidectomy thyroid cancer patients. Methods:A retrospective analysis on 223 patients (46 males, 177 females, age(47.7±14.0) years ) with papillary thyroid cancer, who underwent total thyroidectomy and 131I treatment in Affiliated Hospital of Guilin Medical University between December 2019 and January 2022, was conducted. All patients underwent salivary gland 99Tc mO 4- imaging before and after 131I therapy. The patients were categorized according to salivary gland function based on 99Tc mO 4- imaging results (normal salivary gland vs salivary gland injury), and divided into training and test sets in a ratio of 7∶3. A ResNet-34 neural network model was trained using images at the time of maximum salivary gland radioactivity and those based on background radioactivity counts for structured image feature data. The Delta radiomics approach was then used to subtract the image feature values of the two periods, followed by feature selection through t-test, correlation analysis, and the least absolute shrinkage and selection operator( LASSO) algorithm, to develop logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) predictive models. The diagnostic performance of 3 models for salivary gland function on the test set was compared with that of the manual interpretation. The AUCs of the 3 models on the test set were compared (Delong test). Results:Among the 67 cases of the test set, the diagnostic accuracy of 3 physicians were 89.6%(60/67), 83.6%(56/67), and 82.1%(55/67) respectively, with the time required for diagnosis of 56, 74 and 55 min, respectively. The accuracies of LR, SVM, and KNN models were 91.0%(61/67), 86.6%(58/67), and 82.1%(55/67), with the required times of 12.5, 15.3 and 17.9 s, respectively. All 3 radiomics models demonstrated good classification and predictive capabilities, with AUC values for the training set of 0.972, 0.965, and 0.943, and for the test set of 0.954, 0.913, and 0.791, respectively. There were no significant differences among the AUC values for the test set ( z values: 0.72, 1.18, 1.82, all P>0.05). Conclusion:The models based on deep learning and Delta radiomics possess high predictive value in assessing salivary gland injury following 131I treatment after surgery in patients with thyroid cancer.