Objective:To analyze the values of platelet transforming growth factor-β (TGF-β) and SMAD family member 2 (Smad2) in patients′ peripheral platelets for CRC diagnosis and staging.Methods:Retrospective case-control study. Tumor tissues, paratumor tissues and peripheral blood samples were collected from 248 CRC patients (147 males, 101 females; age 21-93 years) diagnosed in the First Hospital of Jilin University from October 10th, 2020, to March 10th, 2025. Peripheral blood samples were also collected from 40 colorectal adenomatous polyp patients (21 males, 19 females; age 22-74 years) and 75 healthy individuals (43 males, 32 females; age 18-81 years) during the same period. Tissue homogenates and platelets were isolated using tissue disruption and gradient centrifugation, respectively. Total RNA was respectively extracted from tissues and platelets, and the expression levels of TGF-β and Smad2 were quantified by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) expressed as relative quantity 2 -ΔΔCt. Differences of TGF-β and Smad2 expression were compared between CRC tissues and adjacent tissues, as well as among CRC patients, polyp patients, and healthy controls. The relationship of platelet TGF-β and Smad2 expression with pathological features includingtumor stage, pathological type, and metastasis were analyzed. The efficiency of platelet TGF-β, Smad2, and their combination in diagnosing CRC was evaluated using receiver operating characteristic (ROC) curves. Results:The expression levels of TGF-β and Smad2 in CRC tumor tissues[1.09 (0.45, 2.00), 2.93 (0.78, 6.73)] were significantly higher than those in adjacent tissues[0.81 (0.27, 1.50), 1.29 (0.40, 2.63)] ( Z TGF-β=4.54, Z Smad2=6.67, both P<0.001). The expression levels of TGF-β and Smad2 in platelets of CRC patients[2.73(1.53, 4.38), 3.16 (1.58, 4.38)] were significantly higher than those in the colorectal polyp group[1.23(0.70, 2.54), 1.16(0.78, 2.27)] and the healthy control group[0.96(0.51, 1.88), 0.92 (0.55, 1.88)] ( H TGF-β=59.71, H Smad2=78.74, both P<0.001). Platelet TGF-β expression increased progressively with tumor stage (stage 1-4) ( P<0.05), while platelet Smad2 levels were higher in metastatic CRC compared with non-metastatic cases ( P<0.05). ROC curve analysis showed that the area under the curve (AUC) for diagnosing CRC when combining platelet TGF-β and Smad2 was 0.81[95%Confidence interval( CI) 0.77—0.86], which was 0.90 (95% CI 0.86—0.93) if adding serum carcinoembryonic antigen (CEA). Conclusion:Platelet TGF-β and Smad2 expression correlates with the diagnosis and staging of CRC, demonstrating potential as liquid biopsy biomarkers for colorectal malignancies.

Objective:To analyze the values of platelet transforming growth factor-β (TGF-β) and SMAD family member 2 (Smad2) in patients′ peripheral platelets for CRC diagnosis and staging.Methods:Retrospective case-control study. Tumor tissues, paratumor tissues and peripheral blood samples were collected from 248 CRC patients (147 males, 101 females; age 21-93 years) diagnosed in the First Hospital of Jilin University from October 10th, 2020, to March 10th, 2025. Peripheral blood samples were also collected from 40 colorectal adenomatous polyp patients (21 males, 19 females; age 22-74 years) and 75 healthy individuals (43 males, 32 females; age 18-81 years) during the same period. Tissue homogenates and platelets were isolated using tissue disruption and gradient centrifugation, respectively. Total RNA was respectively extracted from tissues and platelets, and the expression levels of TGF-β and Smad2 were quantified by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) expressed as relative quantity 2 -ΔΔCt. Differences of TGF-β and Smad2 expression were compared between CRC tissues and adjacent tissues, as well as among CRC patients, polyp patients, and healthy controls. The relationship of platelet TGF-β and Smad2 expression with pathological features includingtumor stage, pathological type, and metastasis were analyzed. The efficiency of platelet TGF-β, Smad2, and their combination in diagnosing CRC was evaluated using receiver operating characteristic (ROC) curves. Results:The expression levels of TGF-β and Smad2 in CRC tumor tissues[1.09 (0.45, 2.00), 2.93 (0.78, 6.73)] were significantly higher than those in adjacent tissues[0.81 (0.27, 1.50), 1.29 (0.40, 2.63)] ( Z TGF-β=4.54, Z Smad2=6.67, both P<0.001). The expression levels of TGF-β and Smad2 in platelets of CRC patients[2.73(1.53, 4.38), 3.16 (1.58, 4.38)] were significantly higher than those in the colorectal polyp group[1.23(0.70, 2.54), 1.16(0.78, 2.27)] and the healthy control group[0.96(0.51, 1.88), 0.92 (0.55, 1.88)] ( H TGF-β=59.71, H Smad2=78.74, both P<0.001). Platelet TGF-β expression increased progressively with tumor stage (stage 1-4) ( P<0.05), while platelet Smad2 levels were higher in metastatic CRC compared with non-metastatic cases ( P<0.05). ROC curve analysis showed that the area under the curve (AUC) for diagnosing CRC when combining platelet TGF-β and Smad2 was 0.81[95%Confidence interval( CI) 0.77—0.86], which was 0.90 (95% CI 0.86—0.93) if adding serum carcinoembryonic antigen (CEA). Conclusion:Platelet TGF-β and Smad2 expression correlates with the diagnosis and staging of CRC, demonstrating potential as liquid biopsy biomarkers for colorectal malignancies.

Objective:To explore the pathogenesis of flunarizine-induced parkinsonism from gut-brain axis perspective.Methods:Thirty male C57BL/6 mice were randomly divided into control group and flunarizine group ( n=15). Mice in the control group were given 0.1 mL 50% polyethylene glycol 400+50% saline by gavage once/d for 2 weeks, while mice in the flunarizine group were given 6 mg/mL flunarizine+50% polyethylene glycol 400+50% saline by gavage at a daily dose of 30 mg/kg for 2 weeks. Body mass was recorded 1, 3, 5, 7, 10 and 14 d after drug administration, and motor function was assessed by rotarod test 14 d after drug administration; 16s RNA sequencing was performed in the feces to observe the intestinal flora; intestinal transit function was detected by Evans blue by gavage; and then, the mice were sacrificed and homogenate or frozen sections (brain and intestinal tissues) were prepared; dopamine-ergic neuron expression was detected by Western blotting; RT-qPCR was applied to detect the expressions of inflammatory factors in the substantia nigra, and immunofluorescent staining was used to detect the expressions of ZO-1 and Claudin-5 in the intestinal epithelial tissues. Results:Compared with the control group, the flunarizine group had lower body mass ratio 1, 3, 5, 7, 10 and 14 d after drug administration (ratio to body mass before drug administration). Compared with the control group, the flunarizine group had significantly shortened residence time in rod rotating and lower rotational speed when falling ( P<0.05). Compared with the control group, the flunarizine group had decreased tyrosine hydroxylase protein in the substantia nigra without significant difference ( P>0.05). Compared with the control group, the flunarizine group had significantly increased interleukin-6 and tumor necrosis factor-α in the substantia nigra (1.00±0.00 vs. 2.79±0.83; 1.00±0.00 vs. 3.39±1.37), significantly lower intestinal Evans blue propulsion rate (80.67%±4.51% vs. 50.67%±6.03%), and statistically decreased ZO-1 and Claudin-5 expressions in the colonic epithelial tissues (27.01±1.41 vs. 16.32±2.83; 37.00±2.80 vs. 24.52±2.12, P<0.05). Totally, 576 microorganisms were noted in both control group and flunarizine group, 744 in the control group alone, and 634 in the flunarizine group alone. The intestinal flora β diversity indices in the 2 groups were significantly different based on weighted Unifrac-principle coordinates analysis (PCoA, PCoA1: 39.88%; PCoA2: 30.69%). Compared with the control group, the microbial colony structure of mice in flunarizine group was dominated by phylum thick-walled bacteria and phylum warty microbacteria, and by families Muribaculaceae, Lachnospiraceae and Akkermansiaceae. Compared with the control group, the flunarizine group had significantly decreased relative abundance of Ackermannia spp. and Lactobacillus spp. in the intestinal flora ( P<0.05). Conclusion:Flunarizine may contribute to the pathogenesis of DIP by causing structural disturbances in the intestinal flora and inducing neuroinflammation based on the gut-brain axis.

Objective:To establish and validate the prediction model for bone marrow metastasis (BMM) in malignant tumors by screening out laboratory multiparameters.Methods:This case-control study collected 444 cases of malignant tumor patients who were hospitalized in the First Hospital of Jilin University from March 2018 to March 2024, including 243 cases for model establishment set and 201 cases for model validation set. The model establishment set was divided into BMM positive group (81 cases) and BMM negative group (162 cases), and the model validation set was divided into positive group (67 cases) and a negative group (134 cases). We collected patients′ clinical information such as gender, age, clinical diagnosis, and results of 47 laboratory tests including routine blood analysis, coagulation, liver function, tumor markers, potassium, sodium, chloride, and calcium ion tests, bone marrow morphology, and bone marrow biopsy. BMM was taken as the outcome event, differencial variables were analyzed using inter group comparisons, the correlation among parameters was analyzed using Pearson correlation analysis, the risk factors for BMM were analyzed using multivariate conditional logistic regression analysis, to establish logistic model, followed by efficiency evaluation on BMM predictive model using receiver operating characteristic (ROC) curves.Results:In the model establishment set, Pearson correlation analysis of 28 parameters that differed between the BMM positive and negative groups revealed that the correlation coefficients of 17 parameters, including mean platelet volume (MPV), hematocrit (HCT), hemoglobin (HGB), and prothrombin time (PT), were no more than 0.6 ( P<0.05). Further multivariate conditional logistic regression analysis demonstrated that MPV, HGB, HCT, PT, red cell distribution width (RDW), platelet count (PLT), alkaline phosphatase (ALP), chloride (Cl -), and mean erythrocyte hemoglobin concentration (MCHC) were the risk factors of BMM occurence in malignancy [MPV ( OR=9.929, 95% CI 2.688-71.335), HCT ( OR=8.232, 95% CI 6.223-9.841), HGB ( OR=4.300, 95% CI 1.947-16.577), PT ( OR=3.738, 95% CI 1.359-11.666), RDW ( OR=1.995, 95% CI 1.275-3.807), ALP ( OR=1.025, 95% CI 1.012-1.045), PLT ( OR=1.014, 95% CI 1.002-1.031), MCHC ( OR=0.724, 95% CI 0.523-0.880) and Cl -( OR=0.703, 95% CI 0.472-0.967)]. In the model establishment set, combiation of risk factors provided an AUC of 0.943 (95% CI 0.898-0.987, P<0.001), a sensitivity of 86.3%, and a specificity of 89.2% for BMM prediction. In the model validation set, the AUC was 0.924 (95% CI 0.854-0.960, P<0.001), with a sensitivity and specificity of 86.7% and 83.8%, respectively. Conclusion:This study built and validated a multiple-parameter model for BMM, which may facilitate the timely detection of BMM and provide reference for decision making of bone marrow aspiration.

Purpose To explore the value of the new generation tau PET tracer 18F-Florzolotau in Alzheimer's disease(AD)at different stages.Materials and Methods Twenty-five MCI patients and sixty-one AD patients with positive β-amyloid status in Huashan Hospital,Fudan University from February 2020 to January 2022 were retrospectively enrolled with 18F-Florzolotau PET imaging and demographic and clinical data.The pre-processed PET images were analyzed by SPM two-sample t-test between MCI and AD groups,and the standardized uptake value ratios(SUVR)were extracted from the region of interest defined by SPM analysis(P＜0.001);scaled subprofile model/principal component analysis was used to construct the different tau related patterns(MCItauRP,ADtauRP)and calculate the corresponding expression values.The classification efficiency of SUVR and MCItauRP,ADtauRP expression values was evaluated by receiver operating characteristic curve.Results Compared with MCI patients,tau protein deposition of AD patients was increased mainly in the bilateral temporal,occipital lobe(P＜0.001),and the SUVR of these brain region in the AD group was higher than that in the MCI group(Z=-3.164,P＜0.00l);the expression values of MCItauRP and ADtauRP were significantly different between the AD group and MCI group(t=3.72,Z=-3.51;both P＜0.001),and these expression values of AD patients were higher than those in the MCI group;the accuracy of tauRP expression values and SUVR for the differentiation between the AD and MCI group were 61.63％,65.12％and 65.12％,respectively;the sensitivity was 88.00％,96.00％and 100.00％,respectively;the specificity was 50.82％,52.46％and 50.82％,respectively.Conclusion The new tau PET can identify and distinguish the differences in tau protein deposition between AD and MCI patients.However,the classification and diagnosis efficiency is not high.In the future,it is necessary to find a more ideal analysis method.

Age-dependent loss of skeletal muscle mass and function is a feature of sarcopenia, and increases the risk of many aging-related metabolic diseases. Here, we report phenotypic and single-nucleus transcriptomic analyses of non-human primate skeletal muscle aging. A higher transcriptional fluctuation was observed in myonuclei relative to other interstitial cell types, indicating a higher susceptibility of skeletal muscle fiber to aging. We found a downregulation of FOXO3 in aged primate skeletal muscle, and identified FOXO3 as a hub transcription factor maintaining skeletal muscle homeostasis. Through the establishment of a complementary experimental pipeline based on a human pluripotent stem cell-derived myotube model, we revealed that silence of FOXO3 accelerates human myotube senescence, whereas genetic activation of endogenous FOXO3 alleviates human myotube aging. Altogether, based on a combination of monkey skeletal muscle and human myotube aging research models, we unraveled the pivotal role of the FOXO3 in safeguarding primate skeletal muscle from aging, providing a comprehensive resource for the development of clinical diagnosis and targeted therapeutic interventions against human skeletal muscle aging and the onset of sarcopenia along with aging-related disorders.
Animals ; Humans ; Sarcopenia/metabolism* ; Forkhead Box Protein O3/metabolism* ; Muscle, Skeletal/metabolism* ; Aging/metabolism* ; Primates/metabolism*

Hair loss affects millions of people at some time in their life, and safe and efficient treatments for hair loss are a significant unmet medical need. We report that topical delivery of quercetin (Que) stimulates resting hair follicles to grow with rapid follicular keratinocyte proliferation and replenishes perifollicular microvasculature in mice. We construct dynamic single-cell transcriptome landscape over the course of hair regrowth and find that Que treatment stimulates the differentiation trajectory in the hair follicles and induces an angiogenic signature in dermal endothelial cells by activating HIF-1α in endothelial cells. Skin administration of a HIF-1α agonist partially recapitulates the pro-angiogenesis and hair-growing effects of Que. Together, these findings provide a molecular understanding for the efficacy of Que in hair regrowth, which underscores the translational potential of targeting the hair follicle niche as a strategy for regenerative medicine, and suggest a route of pharmacological intervention that may promote hair regrowth.
Mice ; Animals ; Quercetin/pharmacology* ; Endothelial Cells ; Hair ; Hair Follicle ; Alopecia

Detection and identification standard of hookworm—Hookworm larvae coproculture techniques (WS/T 791—2021) is the first recommended technical standard for hookworm detection and species identification using the hookworm larvae coproculture technique in China. This standard was issued on November 23, 2021, and had been in effect since May 1, 2022. This article provides a detailed interpretation pertaining to the background, drafting process, main contents, and dos and don’ts for better understanding and application of this standard among professionals working in disease control and prevention institutions and medical institutions.

Humans ; RNA, Long Noncoding/genetics* ; MicroRNAs/genetics* ; Cell Line, Tumor ; Inflammation/genetics* ; Apolipoproteins E ; Apoptosis