With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

ObjectiveTo explore the effects of the Tai Chi training on bone density， bone turnover markers， and heart rate variability for people with high-risk osteoporosis， and to provide evidence for the prevention of osteoporosis at early stage. MethodsSixty-six cases of people with high risk of osteoporosis were included， and they were divided into 33 cases each in the intervention group and the control group using the random number table method. The control group received osteoporosis health education three times a week， and the intervention group received Tai Chi training under the guidance of a trainer three times a week for 40 mins each time on the basis of the control group， and both groups were intervened for 12 weeks. Dual-energy X-ray absorptiometry was used to measure the bone density of L₁~L₄ vertebrae， bilateral femoral necks and bilateral total hips in the two groups before and after the intervention； enzyme-linked immunosorbent assay was used to determine bone turnover markers before and after the intervention， including pro-collagen type Ⅰ pro-amino-terminal prepropyl peptide （P1NP） and β-collagen type Ⅰ cross-linking carboxy-terminal peptide （β-CTX）. Seven cases with good compliance in the intervention group were selected. After wearing the heart rate sensor， they successively performed Tai Chi training and walking activities recommended by the guideline for 20 mins each， and the heart rate variability （HRV） during exercise was collected， including time-domain indexes such as standard deviation of normal sinus intervals （SDNN）， root-mean-square of the difference between adjacent RR intervals （RMSSD）， frequency-domain metrics such as low-frequency power （LF）， high-frequency power （HF）， and low-frequency/high-frequency power ratio （LF/HF）， as well as nonlinear metrics such as approximate entropy （ApEn）， sample entropy （SampEn）. ResultsFinally， 63 cases were included in the outcome analysis， including 30 cases in the intervention group and 33 cases in the control group. After the intervention， the differences of L₁~L₄ vertebrae， bone density of bilateral femoral neck and bilateral total hip in the intervention group were not statistically significant when compared with those before intervention （P＞0.05）， while the bone density of all parts of the control group decreased significantly compared with that before intervention （P＜0.05）， and the difference in the bone density of the L₁~L₄ vertebrae， bilateral femoral neck， and the right total hip before and after the intervention of the intervention group was smaller than that of the control group （P＜0.05）. The differences in P1NP and β-CTX between groups before and after intervention was not statistically significant （P＞0.05）. Compared with walking exercise， LF decreased， HF increased and LF/HF decreased during Tai Chi exercise （P＜0.05）； the time domain indexes and non-linear indexes between groups had no statistically significant difference （P＞0.05）. ConclusionTai Chi exercise can maintain lumbar， hip， and femoral bone density and improve sympathetic/parasympathetic balance in people at high risk for osteoporosis， but cannot significantly improve bone turnover markers.

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.

Interactions between brain-resident and peripheral infiltrated immune cells are thought to contribute to neuroplasticity after cerebral ischemia. However, conventional bulk sequencing makes it challenging to depict this complex immune network. Using single-cell RNA sequencing, we mapped compositional and transcriptional features of peri-infarct immune cells. Microglia were the predominant cell type in the peri-infarct region, displaying a more diverse activation pattern than the typical pro- and anti-inflammatory state, with axon tract-associated microglia (ATMs) being associated with neuronal regeneration. Trajectory inference suggested that infiltrated monocyte-derived macrophages (MDMs) exhibited a gradual fate trajectory transition to activated MDMs. Inter-cellular crosstalk between MDMs and microglia orchestrated anti-inflammatory and repair-promoting microglia phenotypes and promoted post-stroke neurogenesis, with SOX2 and related Akt/CREB signaling as the underlying mechanisms. This description of the brain's immune landscape and its relationship with neurogenesis provides new insight into promoting neural repair by regulating neuroinflammatory responses.
Humans ; Ischemic Stroke ; Brain/metabolism* ; Macrophages ; Brain Ischemia/metabolism* ; Microglia/metabolism* ; Gene Expression Profiling ; Anti-Inflammatory Agents ; Neuronal Plasticity/physiology* ; Infarction/metabolism*

Objective To observe the expression and localization of group Ⅰ metabotropic glutamate receptors (mGluR1/ 5) in rat superior cervical ganglion (SCG) and the effect of chronic intermittent hypoxia (CIH) on mGluR1/ 5 protein level. Methods Twelve male SD rats were randomly divided into control group(Ctrl)and CIH group(CIH), 6 rats in each group. After 6 weeks of modeling, the effect of CIH on mGluR1/ 5 protein level was detected by Western blotting, the expression and distribution of mGluR1/ 5 in SCG were detected by immunohistochemistry and double-immunofluorescent staining. Results mGluR1/ 5 was expressed in rat SCG. mGluR1 was distributed in neurons and small intensely fluorescent (SIF) cells, but not in satellite glial cells (SGCs), nerve fibers and blood vessels, whereas mGluR5 was mainly distributed in nerve fibers and a little in neurons, but not in SGCs, SIF cells and blood vessels. CIH increased the protein levels of mGluR1/ 5 (P<0. 01) in rat SCG. Conclusion Both mGluR1 and mGluR5 are expressed in the rat SCG, but their distribution are different, and the increased protein levels of both may be involved in CIH-induced hypertension.

Objective To investigate the clinical value of coronary computed tomography angiography(CCTA)based CT derived fractional flow reserve(CT-FFR)and ΔCT-FFR in improving the diagnostic efficiency for coronary abnormal hemodynamics in patients with severe calcification.Methods We retrospectively analyzed the clinical data of coronary artery disease(CAD)patients who underwent CCTA,CT-FFR,invasive coronary angiography(ICA)and FFR during hospitalization from January 2018 to June 2019 in Chinese PLA General Hospital.Severe calcification was defined as coronary artery calcium score(CACS)≥100 on single vessel level.A total of 107 CAD patients with 149 coronary arteries were included in the present study.The enrolled coronary arteries were assigned to CACS≥100 group(n=56)and CACS<100 group(n=93).CT-FFR was performed on the deep FFR platform based on machine learning(ML)algorithms and ΔCT-FFR was defined as CT-FFR difference between proximal and distal to the coronary lesion.The correlation and consistency between CT-FFR and FFR values were analyzed by Pearson and Bland-Altman methods.We attempted to analyze the incremental value of ΔCT-FFR for coronary functional evaluation,especial for coronary arteries with severe calcification,regarding FFR≤0.8 as the diagnostic gold standard.Comparison of receiver operating characteristic curves(ROC)between different diagnostic methods was presented by Delong test.Results Pearson and Bland-Altman analyses showed appreciable correlation(CACS≥100 group,r=0.71,P<0.01;CACS<100 group,r=0.73,P<0.01)and consistency(CACS≥100 group,Mean=-0.01,P=0.25;CACS<100 group,Mean=0,P=0.96)between CT-FFR and FFR values in both groups.FFR(0.80±0.08 vs.0.84±0.09,P=0.004)and CT-FFR(0.81±0.06 vs.0.85±0.06,P<0.001)levels were significant lower in CACS≥100 group than those in CACS<100 group,while ΔCT-FFR(0.14±0.06 vs.0.09±0.06,P<0.001)levels were significant higher in CACS≥100 group.Moreover,the diagnostic efficiency of CT-FFR in CACS≥100 group was inferior to that in CACS<100 group[AUC=0.792(95%CI 0.663-0.889)vs.AUC=0.929(95%CI 0.856-0.972),P=0.04],while it achieved significant improvement after ΔCT-FFR adjustment[AUC=0.876(95%CI 0.760-0.949)vs.AUC=0.792(95%CI 0.663-0.889),P=0.02]and was similar to that in CACS<100 group(P=0.37).Conclusion For coronary arteries with severe calcification,CT-FFR demonstrated significant incremental value in improving the diagnostic efficiency of coronary abnormal hemodynamics after ΔCT-FFR adjustment.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Objective:To explore the relationship between the triglyceride glucose (TyG) index and the risk of developing hypertension among rural Chinese adults.Methods:A prospective cohort study was conducted from 2007 to 2008, involving 20 194 adults selected through random cluster sampling from a rural community in Luoyang City, Henan Province. Follow-ups were carried out in 2013-2014 and 2018-2020. After excluding participants with hypertension at baseline, those with missing TyG index data, individuals who passed away during follow-up, and those with incomplete hypertension status at the second visit, 9 802 participants were included in the analysis. Baseline and follow-up assessments included questionnaire interviews, physical measurements (including blood pressure), and blood sample collection for fasting lipid and glucose levels. Participants were divided into four groups according to TyG index quartiles, and a modified Poisson regression model was utilized to assess the association between TyG index quartiles and hypertension risk.Results:The study cohort comprised 9 802 participants with a median age of 48 (39, 57) years, including 3 803 males (38.80%). Participants were distributed across TyG index quartiles as follows: TyG<8.2 group (2 224 individuals), TyG 8.2-8.5 group (2 653 individuals), TyG 8.6-8.9 (2 441 individuals), and TyG≥9.0 (2 484 individuals). Over a follow-up period of (11.1±1.3) years, 3 378 subjects developed hypertension, resulting in a cumulative incidence of 34.46% (3 378/9 802). The risk of hypertension increased with higher TyG index quartiles ( Ptrend<0.05). Compared to the TyG<8.2, the TyG 8.2-8.5 ( RR=1.11, 95% CI 1.01-1.22, P=0.023), TyG 8.6-8.9 ( RR=1.16, 95%CI 1.06-1.27, P=0.023), and TyG≥9.0 ( RR=1.20, 95%CI 1.10-1.31, P=0.023) exhibited increased hypertension risk after adjusting for age, gender, educational level, and other potential confounders. Subgroup analyses based on gender and age at baseline yielded results consistent with the main analysis. Conclusions:The TyG index is positively correlated with the risk of developing hypertension in the rural adult population.

Objective To explore the changes in serum energy metabolites in patients with peripheral T-cell lymphoma,and investigate serum biomarkers for monitoring peripheral T-cell lymphoma from the perspective of energy metabolism.Methods Multiple/selected reaction monitoring(MRM/SRM)was used to detect the energy-related metabolites in the sera of 16 patients with newly diagnosed peripheral T-cell lymphoma admitted in the Hematology Medical Center of the Second Affiliated Hospital of Army Medical University from November 2020 to December 2021,as well as 10 recruited healthy volunteers.The corresponding clinical data including medical history,laboratory results and image data were collected and retrospectively analyzed.Results Significant differences were seen in the contents and expression profiles of serum energy metabolism-related products between the patients and the healthy volunteers.The patients had significantly reduced serum contents of cyclic AMP,succinate,citrate and cis-aconitate(P＜0.05),and elevated D-glucose 6-phosphate content(P＜0.05).The serum contents of citrate and succinate were negatively correlated with the risk stratification(low-,moderate-and high-risk)and clinical stage of the disease(P＜0.05).Meanwhile,there was a negative correlation between the contents of L-malic acid and citrate and the mid-term efficacy evaluation results,such as complete/partial response(CR/PR)or stable disease(SD)(P＜0.05).For patients with extranodal NK/T cell lymphoma(n=10),there were also significant reductions in the contents of cyclic AMP,succinate,citrate,isocitrate and cis-aconitate in the sera of patients compared with healthy volunteers(P＜0.05),and the contents of citrate and succinate were negatively correlated with the clinical stage(P＜0.05)and were rather correlated with mid-term efficacy evaluation results(CR/PR or SD)(P＜0.05).For patients with angioimmunoblastic T-cell lymphoma(n=6),the serum contents of cyclic AMP,citrate and succinate were significantly lower,while the content of D-glucose 6-phosphate was higher when compared with the healthy volunteers(P＜0.05),and the content of succinate was negatively correlated with both clinical stage and risk grade of the patients(P＜0.05).Conclusion There are 5 serum differential metabolites identified between patients with peripheral T-cell lymphoma and healthy controls,and succinate and citrate are expected to be serum biomarkers of peripheral T-cell lymphoma.

Objective This study aimed to comprehensively analyze and compare the clinicopathological features and prognosis of Chinese patients with human epidermal growth factor receptor 2(HER2)-low early breast cancer(BC)and HER2-IHC0 BC. Methods Patients diagnosed with HER2-negative BC(N=999)at our institution between January 2011 and December 2015 formed our study population.Clinicopathological characteristics,association between estrogen receptor(ER)expression and HER2-low,and evolution of HER2 immunohistochemical(IHC)score were assessed.Kaplan-Meier method and log-rank test were used to compare the long-term survival outcomes(5-year follow-up)between the HER2-IHC0 and HER2-low groups. Results HER2-low BC group tended to demonstrate high expression of ER and more progesterone receptor(PgR)positivity than HER2-IHC0 BC group(P<0.001).The rate of HER2-low status increased with increasing ER expression levels(Mantel-Haenszel χ2 test,P<0.001,Pearson's R=0.159,P<0.001).Survival analysis revealed a significantly longer overall survival(OS)in HER2-low BC group than in HER2-IHC0 group(P=0.007)in the whole cohort and the hormone receptor(HR)-negative group.There were no significant differences between the two groups in terms of disease-free survival(DFS).The discordance rate of HER2 IHC scores between primary and metastatic sites was 36.84%. Conclusion HER2-low BC may not be regarded as a unique BC group in this population-based study due to similar clinicopathological features and prognostic roles.