Effects of the pesticide carbendazim on broiler chicken health and carbendazim residue levels in broiler tissues.

Pesticide residues may enter the human body through the food chain when livestock and poultry consume pesticide-contaminated feed. Therefore, monitoring and limiting pesticide residues in animal feed and animal-origin foods is necessary. Carbendazim is one of the most frequently detected pesticides in food and feed and has various toxic effects on non-target animals. This study investigated the effects of varying concentrations of carbendazim contamination in feed on broiler chicken growth performance, serum biochemical indicators, histopathology, and carbendazim residues in broiler muscles and livers. The results demonstrated that contamination of 5-100 mg/kg carbendazim in feed did not affect broiler growth performance or health. Carbendazim contamination in feed at 200-800 mg/kg slightly reduced growth performance. Broiler kidneys showed minor histopathological alterations after 400 mg/kg carbendazim exposure. Furthermore, when the carbendazim content in feed was less than 25 mg/kg, the residual carbendazim in the muscles and livers of broilers did not exceed the maximum residue level set by the European Union and China. Based on the above findings, carbendazim residues in the feed of less than 25 mg/kg can be considered safe for chicken products.

π-π conjugated PDI supramolecular regulating the photoluminescence of imine-COFs for sensitive smartphone visual detection of levofloxacin.

As the contamination and enrichment in food chain of levofloxacin (LV) antibiotics have caused a significant threat to life safety, the instant detection of LV has become an urgent need. Here, a PDI-functionalized imine-based covalent organic framework (PDI-COF300) was prepared by the electrostatic self-assembly method as fluorescent probe for smartphone visual detection of LV, which exhibited excellent fluorescence quantum yield (82.68%), greater stability, high sensitivity with detection limit of 0.303 µM. Based on the results of molecular docking and Stern-Volmer equation, the LV detection by PDI-COF300 was mainly a static quenching process through π-π stacked hydrophobic interactions and fluorescence resonance energy transfer. Besides, PDI-COF300 was applied to LV detection in environmental medium and milk samples with recoveries from 85.56% to 108.34% and relative standard deviations <2.70%. This work also provided a new general strategy for using PDI-COF in smartphone devices and fluorescent papers for LV fluorescence detection and microanalysis.

A Cascade C(sp3)-H Annulation Involving C(alkyl),C(alkyl)-Palladacycle Intermediates.

C-H bond functionalization involving C,C-palladacycle intermediates provides a unique platform for developing novel reactions. However, the vast majority of studies have been limited to the transformations of C(aryl),C-palladacycles. In sharp contrast, catalytic reactions involving C(alkyl),C(alkyl)-palladacycles have rarely been reported. Herein, we disclose an unprecedented cascade C(sp3)-H annulation involving C(alkyl),C(alkyl)-palladacycles. In this protocol, alkene-tethered cycloalkenyl bromides undergo intramolecular Heck/C(sp3)-H activation to generate C(alkyl),C(alkyl)-palladacycles, which can be captured by α-bromoacrylic acids to afford tricyclic fused pyridinediones. In addition, this strategy can also be applied to indole-tethered cycloalkenyl bromides to construct pentacyclic fused pyridinediones via suquential Heck dearomatization/C(sp3)-H activation/decarboxylative cyclization. Notably, the removal of α-bromoacrylic acids in the reaction of alkene-tethered cycloalkenyl bromides can build an interesting tricyclic skeleton containing a four-membered ring. Preliminary mechanistic experiments indicate that five-membered C(alkyl),C(alkyl)-palladacycles serve as the key intermediates. Meanwhile, density functional theory (DFT) calculations have provided insights into the reaction pathway.

Target next-generation sequencing for the accurate diagnosis of Parvimonas micra lung abscess: a case series and literature review.

Background: Parvimonas micra (P. micra) has been identified as a pathogen capable of causing lung abscesses; however, its identification poses challenges due to the specialized culture conditions for anaerobic bacterial isolation. Only a few cases of lung abscesses caused by P. micra infection have been reported. Therefore, we describe the clinical characteristics of lung abscesses due to P. micra based on our case series. Methods: A retrospective analysis was conducted on eight patients who were diagnosed with lung abscesses attributed to P. micra. Detection of P. micra was accomplished through target next-generation sequencing (tNGS). A systematic search of the PubMed database using keywords "lung abscess" and "Parvimonas micra/Peptostreptococcus micros" was performed to review published literature pertaining to similar cases. Results: Among the eight patients reviewed, all exhibited poor oral hygiene, with four presenting with comorbid diabetes. Chest computed tomography (CT) showed high-density mass shadows with necrosis and small cavities in the middle. Bronchoscopic examination revealed purulent sputum and bronchial mucosal inflammation. Thick secretions obstructed the airway, leading to the poor drainage of pus, and the formation of local abscesses leading to irresponsive to antibiotic therapy, which finally protracted recovery time. P. micra was successfully identified in bronchoalveolar lavage fluid (BALF) samples from all eight patients using tNGS; in contrast, sputum and BALF bacterial cultures yielded negative results, with P. micra cultured from only one empyema sample. Following appropriate antibiotic therapy, seven patients recovered. In previously documented cases, favorable outcomes were observed in 77.8% of individuals treated with antibiotics and 22.2% were cured after surgical interventions for P. micra lung abscesses. Conclusions: This study enriches our understanding of the clinical characteristics associated with lung abscesses attributed to P. micra. Importantly, tNGS has emerged as a rapid and effective diagnostic test in scenarios where traditional sputum cultures are negative. Encouragingly, patients with lung abscesses caused by P. micra infection exhibit a favorable prognosis with effective airway clearance and judicious anti-infective management.

Rescue Therapies for Steroid-Refractory Acute Severe Ulcerative Colitis: A Systemic Review and Network Meta-analysis.

BACKGROUND AND AIMS: Approximately 40% of patients with steroid-refractory acute severe ulcerative colitis (steroid-refractory (SR) ASUC) requires colectomies. Advanced therapies may reduce the short-term colectomy rates in patients with SR ASUC. However, comparative clinical studies evaluating the effectiveness of these rescue therapies are lacking. Therefore, we conducted a network meta-analysis to study the effectiveness of rescue therapies for SR ASUC. METHODS: Six randomized controlled trials and 15 cohort studies including 2,004 patients were analyzed. Rescue drugs included tofacitinib, infliximab with a 5 or 10 mg/kg induction dose at 0, 2, and 6 weeks (IFX and IFX10, respectively), IFX with an accelerated regimen of three 5 mg/kg induction doses timed according to clinical need (accelerated IFX), tacrolimus, cyclosporine (CyA), ustekinumab, and adalimumab. Treatments were compared with a placebo. RESULTS: Tofacitinib (odds ratio [OR]: 0.09 [95% confidence interval [CI]: 0.02-0.52]), accelerated IFX (OR: 0.16 [95% CI: 0.03-0.94]), IFX (OR: 0.2 [95% CI: 0.07-0.58]), and tacrolimus (OR: 0.24 [95% CI: 0.06-0.96]) significantly reduced the short-term colectomy rates compared with placebo. IFX10 and CyA tended to prevent colectomies. However, ustekinumab and adalimumab did not significantly affect the colectomy rates. CONCLUSION: This is the first network meta-analysis to investigate the efficacy of advanced therapies in reducing short-term colectomy rates in patients with SR ASUC. Tofacitinib, accelerated IFX, standard IFX, and tacrolimus significantly reduced the colectomy rates in SR ASUC patients compared with placebo. Thus, advanced therapies should be considered for rescue therapies in patients with SR ASUC.

MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma.

Objectives: To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL). Materials and methods: MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction. Results: The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision. Conclusion: In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task. Clinical relevance statement: Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

Cyclic adenosine 3', 5'-monophosphate (cAMP) signaling is a crucial therapeutic target for ulcerative colitis.

The pathogenesis of ulcerative colitis (UC), a chronic intestine inflammatory disease primarily affecting adolescents, remains uncertain. Contemporary studies suggest that a confluence of elements, including genetic predispositions, environmental catalysts, dysregulated immune responses, and disturbances in the gut microbiome, are instrumental in the initiation and advancement of UC. Among them, inflammatory activation and mucosal barrier damage caused by abnormal immune regulation are essential links in the development of UC. The impairment of the mucosal barrier is intricately linked to the interplay of various cellular mechanisms, including oxidative stress, autophagy, and programmed cell death. An extensive corpus of research has elucidated that level of cyclic adenosine 3',5'-monophosphate (cAMP) undergo modifications in the midst of inflammation and participate in a diverse array of cellular operations that mitigate inflammation and the impairment of the mucosal barrier. Consequently, a plethora of pharmacological agents are currently under development, with some advancing through clinical trials, and are anticipated to garner approval as novel therapeutics. In summary, cAMP exerts a crucial influence on the onset and progression of UC, with fluctuations in its activity being intimately associated with the severity of the disease's manifestation. Significantly, this review unveils the paramount role of cAMP in the advancement of UC, offering a tactical approach for the clinical management of individuals afflicted with UC.

Corrigendum: High-definition transcranial direct current stimulation for upper extremity rehabilitation in moderate-to-severe ischemic stroke: a pilot study.

[This corrects the article DOI: 10.3389/fnhum.2023.1286238.].

The functional and structural alterations in brain regions related to the fear network model in panic disorder: A resting-state fMRI and T1-weighted imaging study.

Abnormal functional connectivity (FC) within the fear network model (FNM) has been identified in panic disorder (PD) patients, but the specific local structural and functional properties, as well as effective connectivity (EC), remain poorly understood in PD. The purpose of this study was to investigate the structural and functional patterns of the FNM in PD. Magnetic resonance imaging data were collected from 33 PD patients and 35 healthy controls (HCs). Gray matter volume (GMV), degree centrality (DC), regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) were used to identify the structural and functional characteristics of brain regions within the FNM in PD. Subsequently, FC and EC of abnormal regions, based on local structural and functional features, and their correlation with clinical features were further examined. PD patients exhibited preserved GMV, ReHo, and ALFF in the brain regions of the FNM compared with HCs. However, increased DC in the bilateral amygdala was observed in PD patients. The amygdala and its subnuclei exhibited altered EC with rolandic operculum, insula, medial superior frontal gyrus, supramarginal gyrus, opercular part of inferior frontal gyrus, and superior temporal gyrus. Additionally, Hamilton Anxiety Scale score was positively correlated with EC from left lateral nuclei (dorsal portion) of amygdala to right rolandic operculum and left superior temporal gyrus. Our findings revealed a reorganized functional network in PD involving brain regions regulating exteroceptive-interoceptive signals, mood, and somatic symptoms. These results enhance our understanding of the neurobiological underpinnings of PD, suggesting potential biomarkers for diagnosis and targets for therapeutic intervention.

Identification of lignans as selective cyclooxygenase-2 inhibitors from the extract of Acanthopanacis cortex.

Acanthopanacis cortex (the dried root bark of Acanthopanax gracilistylus W. W. Smith) has been used for the treatment of rheumatic diseases in China for over 2000 years. Four previously undescribed lignans (1-4) and 12 known lignans (5-16) were isolated from Acanthopanacis cortex. In this study, the inhibitory activities of compounds 1-16 against neutrophil elastase (NE), cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2) are reported. The results show that compounds 1-16 exhibit weak inhibitory activities against NE and COX-1. However, compounds 2, 6-8 and 13-16 demonstrate better COX-2 inhibitory effects with IC50 values from 0.75 to 8.17 µΜ. These findings provide useful information for the search for natural selective COX-2 inhibitors.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study.

OBJECTIVES: To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset. MATERIALS AND METHODS: Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions. RESULTS: The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort. CONCLUSION: The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model. CLINICAL RELEVANCE STATEMENT: Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task. Specifically, AMCF employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models, aiming to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of AMCF, it is applied to seven public visual classification datasets commonly used in transfer learning, and compared with the most widely used single-source domain fine-tuning methods. Experimental results demonstrate that, in comparison with the existing fine-tuning methods, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, thereby significantly improving the fine-tuning performance.

Collagen in hepatocellular carcinoma: A novel biomarker and therapeutic target.

HCC is globally recognized as a major health threat. Despite significant progress in the development of treatment strategies for liver cancer, recurrence, metastasis, and drug resistance remain key factors leading to a poor prognosis for the majority of liver cancer patients. Thus, there is an urgent need to develop effective biomarkers and therapeutic targets for HCC. Collagen, the most abundant and diverse protein in the tumor microenvironment, is highly expressed in various solid tumors and plays a crucial role in the initiation and progression of tumors. Recent studies have shown that abnormal expression of collagen in the tumor microenvironment is closely related to the occurrence, development, invasion, metastasis, drug resistance, and treatment of liver cancer, making it a potential therapeutic target and a possible diagnostic and prognostic biomarker for HCC. This article provides a comprehensive review of the structure, classification, and origin of collagen, as well as its role in the progression and treatment of HCC and its potential clinical value, offering new insights into the diagnosis, treatment, and prognosis assessment of liver cancer.

The cuproptosis-related signature predicts the prognosis and immune microenvironments of primary diffuse gliomas: a comprehensive analysis.

BACKGROUND: Evidence has revealed a connection between cuproptosis and the inhibition of tumor angiogenesis. While the efficacy of a model based on cuproptosis-related genes (CRGs) in predicting the prognosis of peripheral organ tumors has been demonstrated, the impact of CRGs on the prognosis and the immunological landscape of gliomas remains unexplored. METHODS: We screened CRGs to construct a novel scoring tool and developed a prognostic model for gliomas within the various cohorts. Afterward, a comprehensive exploration of the relationship between the CRG risk signature and the immunological landscape of gliomas was undertaken from multiple perspectives. RESULTS: Five genes (NLRP3, ATP7B, SLC31A1, FDX1, and GCSH) were identified to build a CRG scoring system. The nomogram, based on CRG risk and other signatures, demonstrated a superior predictive performance (AUC of 0.89, 0.92, and 0.93 at 1, 2, and 3 years, respectively) in the training cohort. Furthermore, the CRG score was closely associated with various aspects of the immune landscape in gliomas, including immune cell infiltration, tumor mutations, tumor immune dysfunction and exclusion, immune checkpoints, cytotoxic T lymphocyte and immune exhaustion-related markers, as well as cancer signaling pathway biomarkers and cytokines. CONCLUSION: The CRG risk signature may serve as a robust biomarker for predicting the prognosis and the potential viability of immunotherapy responses. Moreover, the key candidate CRGs might be promising targets to explore the underlying biological background and novel therapeutic interventions in gliomas.

Non-target discovery and risk prediction of per- and polyfluoroalkyl substances (PFAS) and transformation products in wastewater treatment systems.

Wastewater treatment plants (WWTPs) serve as the main destination of many wastes containing per- and polyfluoroalkyl substances (PFAS). Here, we investigated the occurrence and transformation of PFAS and their transformation products (TPs) in wastewater treatment systems using high-resolution mass spectrometry-based target, suspect, and non-target screening approaches. The results revealed the presence of 896 PFAS and TPs in aqueous and sludge phases, of which 687 were assigned confidence levels 1-3 (46 PFAS and 641 TPs). Cyp450 metabolism and environmental microbial degradation were found to be the primary metabolic transformation pathways for PFAS within WWTPs. An estimated 52.3 %, 89.5 %, and 13.6 % of TPs were believed to exhibit persistence, bioaccumulation, and toxicity effects, respectively, with a substantial number of TPs posing potential health risks. Notably, the length of the fluorinated carbon chain in PFAS and TPs was likely associated with increased hazard, primarily due to the influence of biodegradability. Ultimately, two high riskcompounds were identified in the effluent, including one PFAS (Perfluorobutane sulfonic acid) and one enzymatically metabolized TP (23-(Perfluorobutyl)tricosanoic acid@BTM0024_cyp450). It is noteworthy that the toxicity of some TPs exceeded that of their parent compounds. The results from this study underscores the importance of PFAS TPs and associated environmental risks.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.

A retrospective analysis of transudative pleural effusion due to fibrosing mediastinitis.

BACKGROUND: Pleural effusion caused by fibrosing mediastinitis is rarely reported. This study aimed to summarize the clinical manifestations, diagnosis and treatment of transudative pleural effusion due to fibrosing mediastinitis. METHODS: Medical records and follow-up data of 7 patients with transudative pleural effusion due to fibrosing mediastinitis in Beijing Chaoyang Hospital between May 2014 and Feb 2018 were retrospectively analyzed. RESULTS: These patients included 4 males and 3 females, with an average age of (64 ± 9) years. There were 3 left-sided effusions, 2 right-sided effusions and 2 bilateral effusions. Previous or latent tuberculosis was found in 6 patients. Pulmonary hypertension was indicated by echocardiography in all the 7 patients. Computed tomography pulmonary angiography (CTPA) of all the 7 cases showed increased soft tissue images visible in the mediastinum and bilateral hilus, different degrees of stenosis or occlusion in the pulmonary artery and pulmonary vein. In addition, 4 cases were found of right middle lobe atelectasis with a mediastinal window setting. There was interstitial pulmonary edema on the side of pleural effusion with a lung window setting. All the 7 patients were treated with intermittent drainage of pleural effusion combined with diuretic therapy. Five patients were treated with antituberculosis therapy. Up to now, two patients died of right heart failure and respiratory failure after 2 and 16 months respectively; The remaining 5 patients were still in follow up. CONCLUSION: Fibrosing mediastinitis can lead to pulmonary vein stenosis or occlusion, and thus cause transudative pleural effusion, which can be detected by CTPA. Pulmonary hypertension, long time of cough, and a history of tuberculosis are common in these patients. The common therapy is intermittent drainage of pleural effusion combined with diuretic therapy.

Functional variation among mesenchymal stem cells derived from different tissue sources.

Background: Mesenchymal stem cells (MSCs) are increasingly recognized for their regenerative potential. However, their clinical application is hindered by their inherent variability, which is influenced by various factors, such as the tissue source, culture conditions, and passage number. Methods: MSCs were sourced from clinically relevant tissues, including adipose tissue-derived MSCs (ADMSCs, n = 2), chorionic villi-derived MSCs (CMMSCs, n = 2), amniotic membrane-derived MSCs (AMMSCs, n = 3), and umbilical cord-derived MSCs (UCMSCs, n = 3). Passages included the umbilical cord at P0 (UCMSCP0, n = 2), P3 (UCMSCP3, n = 2), and P5 (UCMSCP5, n = 2) as well as the umbilical cord at P5 cultured under low-oxygen conditions (UCMSCP5L, n = 2). Results: We observed that MSCs from different tissue origins clustered into six distinct functional subpopulations, each with varying proportions. Notably, ADMSCs exhibited a higher proportion of subpopulations associated with vascular regeneration, suggesting that they are beneficial for applications in vascular regeneration. Additionally, CMMSCs had a high proportion of subpopulations associated with reproductive processes. UCMSCP5 and UCMSCP5L had higher proportions of subpopulations related to female reproductive function than those for earlier passages. Furthermore, UCMSCP5L, cultured under low-oxygen (hypoxic) conditions, had a high proportion of subpopulations associated with pro-angiogenic characteristics, with implications for optimizing vascular regeneration. Conclusions: This study revealed variation in the distribution of MSC subpopulations among different tissue sources, passages, and culture conditions, including differences in functions related to vascular and reproductive system regeneration. These findings hold promise for personalized regenerative medicine and may lead to more effective clinical treatments across a spectrum of medical conditions.