BackgroundLow medication compliance among patients with severe mental disorders increases the disease burden on both the patients' families and the society. Medication adherence is influenced by numerous factors. Traditional methods such as Logistic regression struggle to quantify the importance of these factors. By introducing Extreme Gradient Boosting （XGBoost） combined with Shapley Additive Explanations （SHAP）， enables the quantification of the relative contribution weights of each factor， providing support for identifying the core influencing factors. ObjectiveTo explore the influencing factors of medication adherence among patients with severe mental disorders in Zhuhai， aiming to provide references for optimizing patient management strategies. MethodsExtract the data of patients with severe mental disorders who were registered on the mental health system platform in Zhuhai City from January 1， 2023 to March 31， 2025. A total of 9 329 patients were finally included for analysis. Influencing factors were screened using univariate analysis and multivariate logistic regression analysis， and an XGBoost model combined with the SHAP algorithm was constructed to quantify the importance of each influencing factor. ResultsAmong 9 329 patients， 8 446 demonstrated medication adherence， yielding an adherence rate of 90.53%. Multivariable analysis identified several risk factors significantly associated with medication non-adherence， being unmarried （OR=1.237， 95% CI： 1.019–1.502） or divorced （OR=1.389， 95% CI： 1.038–1.832）， a diagnosis of mental retardation with psychiatric disorders （OR=3.025， 95% CI： 2.402–3.796） or paranoid psychosis （OR=5.117， 95% CI： 3.086–8.299）， a disease duration of 2–4 years （OR=1.355， 95% CI： 1.085–1.696）， 4–6 years （OR=2.143， 95% CI： 1.671–2.747）， or >6 years （OR=1.681， 95% CI： 1.365–2.079）， lack of guardian subsidies （OR=1.412， 95% CI： 1.099–1.801）， absence of a disability certificate （OR=1.900， 95% CI： 1.588–2.282）， not being enrolled in care and support groups （OR=1.384， 95% CI： 1.183–1.617） or community services （OR=1.313， 95% CI： 1.042–1.645）， and not cohabiting with a guardian （OR=1.257， 95% CI： 1.048–1.501）. Conversely， the enrollment in special outpatient disease programs （OR=0.716， 95% CI： 0.609–0.842） and a family history of mental illness （OR=0.713， 95% CI： 0.503–0.982） were identified as protective factors. The XGBoost model exhibited robust predictive performance， with a sensitivity of 0.433， specificity of 0.944， accuracy of 0.891， Area Under the Curve （AUC） of 0.837， and F1 value of 0.449. Feature importance ranking indicated that the top three factors were disease duration， diagnosis， and the acquisition of disability certificates. ConclusionPolicy-based support （acquisition of disability certificates， special outpatient disease enrollment） and clinical disease characteristics （disease duration， diagnosis type） are key factors affecting medication adherence among patients with severe mental disorders in Zhuhai City. ［Funded by Zhuhai Medical Research Project （number， 2220009000281）］

ObjectiveTo observe the effect of Yifei Jianpi prescription on the of signal transducer and activator of transcription protein 1 （STAT1）/interferon regulatory factor 3 （IRF3） signaling pathway in a pneumonia model induced by lipopolysaccharide （LPS） and to explore the mechanism of Yifei Jianpi prescription in improving lung immune and inflammatory responses. MethodsSixty male SPF SD rats were used in this study. Ten rats were randomly assigned to the normal control group， and the remaining 50 were instilled with LPS in the trachea to establish a pneumonia model. After successful modeling， the rats were randomly divided into the model group， dexamethasone group （0.5 mg·kg^-1）， and Yifei Jianpi prescription high-dose （12 mg·kg^-1）， medium-dose （6 mg·kg^-1）， and low-dose （3 mg·kg^-1） groups， with 10 rats in each group. Treatment was administered once daily， and the normal control and model groups received the same volume of normal saline. After 14 days， flow cytometry was used to detect the classification of whole blood lymphocytes. Enzyme-linked immunosorbent assay （ELISA） was used to measure serum levels of immunoglobulin G （IgG）， immunoglobulin A （IgA）， immunoglobulin M （IgM）， and the content of tumor necrosis factor-α （TNF-α）， interleukin-8 （IL-8）， interleukin-6 （IL-6）， and interleukin-10 （IL-10） in alveolar lavage fluid （BALF）. Hematoxylin-eosin （HE） staining was used to observe lung tissue pathology and score the damage. Thymus weight， spleen weight， and wet-to-dry weight ratio （W/D） were recorded. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of STAT1， IRF3， IL-6， and interferon-alpha （IFN-α） in lung tissues， while Western blot was performed to assess the protein expression of STAT1， IRF3， IL-6， and IFN-α. ResultsCompared with the normal control group， the model group showed significantly increased proportion of B lymphocytes in peripheral blood， decreased proportions of NK cells and CD4⁺/CD8⁺ （P<0.05， P<0.01）， decreased serum levels of IgG and IgA， significantly increased IgM levels （P<0.01）， significantly elevated content of TNF-α， IL-6， and IL-8 in BALF， and significantly decreased IL-10 levels （P<0.01）. Lung tissue damage was evident， with significant increases in thymus and spleen weights and a higher W/D ratio （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly upregulated （P<0.05，P<0.01）. Compared with the model group， the Yifei Jianpi prescription groups showed significantly reduced proportions of B lymphocytes in peripheral blood， increased proportions of NK cells and CD4⁺/CD8⁺ ratios （P<0.05， P<0.01）， significantly increased serum levels of IgG and IgA， significantly decreased IgM levels （P<0.05， P<0.01）， significantly reduced levels of TNF-α， IL-6， and IL-8 in BALF， and significantly increased IL-10 levels （P<0.01）. Lung tissue damage was alleviated， thymus and spleen weights were significantly reduced， and the W/D ratio was markedly decreased （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly downregulated （P<0.05， P<0.01）. ConclusionYifei Jianpi prescription can alleviate lung tissue damage and improve immune and inflammatory responses in LPS-induced pneumonia rats. The mechanism may be related to the inhibition of STAT1/IRF3 signaling pathway activation.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To analyze the effects of blue light on the formation of kidney stones. Methods: A total of 40 rats were randomly divided into 4 groups：A，B，C，and D，with 10 rats in each group.Rats in groups C and D were administered with a mixture of 10 g/L ethylene glycol，20 g/L ammonium chloride，and 100 g/L calcium gluconate via gavage (2 mL per mouse)，while rats in groups A and B received an equal volume of physiological saline via gavage.From the second day after gavage，rats in groups A and C were subjected to twice-daily blue light irradiation (one hour per session) as an intervention，while rats in groups B and D were subjected to fluorescent lamp irradiation using the same method.After 4 weeks of intervention，the 24-hour urine samples were collected，and the rats were then euthanized for the collection of blood and kidney tissue samples.Serum levels of antidiuretic hormone (ADH)，urinary Ca ，and urinary oxalate (Oxa) were measured.Levels of malondialdehyde (MDA) and superoxide dismutase (SOD) in kidney tissues were detected using ELISA.Von Kossa staining was performed to observe pathological changes in kidney tissues and the presence of calcium salt crystals in the kidneys. Results: Compared with groups A and B，groups C and D showed higher accumulation of calcium salt crystals in renal tissues，as well as elevated levels of ADH，urinary Ca ，urinary Oxa，and MDA in renal tissues, additionally，the SOD level in renal tissues was lower (P<0.05).Compared with group D，group C exhibited higher accumulation of calcium salt crystals in renal tissues，along with increased levels of ADH，urinary Ca ，urinary Oxa，and MDA in renal tissues；conversely，the SOD level in renal tissues was lower (P<0.05). Conclusion: Blue light may increase the formation of kidney stones in rats by promoting the secretion of ADH in serum and oxidative stress in kidney tissues through the brain-kidney axis.

Mitochondria, the primary energy-producing organelles of the cell, also serve as signaling hubs and participate in diverse physiological and pathological processes, including apoptosis, inflammation, oxidative stress, neurodegeneration, and tumorigenesis. As semi-autonomous organelles, mitochondrial functionality relies on nuclear support, with mitochondrial biogenesis and homeostasis being stringently regulated by the nuclear genome. This interdependency forms a bidirectional signaling network that coordinates cellular energy metabolism, gene expression, and functional states. During mitochondrial damage or dysfunction, retrograde signals are transmitted to the nucleus, activating adaptive transcriptional programs that modulate nuclear transcription factors, reshape nuclear gene expression, and reprogram cellular metabolism. This mitochondrion-to-nucleus communication, termed “mitochondrial retrograde signaling”, fundamentally represents a mitochondrial “request” to the nucleus to maintain organellar health, rooted in the semi-autonomous nature of mitochondria. Despite possessing their own genome, the “fragmented” mitochondrial genome necessitates reliance on nuclear regulation. This genomic incompleteness enables mitochondria to sense and respond to cellular and environmental stressors, generating signals that modulate the functions of other organelles, including the nucleus. Evolutionary transfer of mitochondrial genes to the nuclear genome has established mitochondrial control over nuclear activities via retrograde communication. When mitochondrial dysfunction or environmental stress compromises cellular demands, mitochondria issue retrograde signals to solicit nuclear support. Studies demonstrate that mitochondrial retrograde signaling pathways operate in pathological contexts such as oxidative stress, electron transport chain (ETC) impairment, apoptosis, autophagy, vascular tension, and inflammatory responses. Mitochondria-related diseases exhibit marked heterogeneity but invariably result in energy deficits, preferentially affecting high-energy-demand tissues like muscles and the nervous system. Consequently, mitochondrial dysfunction underlies myopathies, neurodegenerative disorders, metabolic diseases, and malignancies. Dysregulated retrograde signaling triggers proliferative and metabolic reprogramming, driving pathological cascades. Mitochondrial retrograde signaling critically influences tumorigenesis and progression. Tumor cells with mitochondrial dysfunction exhibit compensatory upregulation of mitochondrial biogenesis, excessive superoxide production, and ETC overload, collectively promoting metastatic tumor development. Recent studies reveal that mitochondrial retrograde signaling—mediated by altered metabolite levels or stress signals—induces epigenetic modifications and is intricately linked to tumor initiation, malignant progression, and therapeutic resistance. For instance, mitochondrial dysfunction promotes oncogenesis through mechanisms such as epigenetic dysregulation, accumulation of mitochondrial metabolic intermediates, and mitochondrial DNA (mtDNA) release, which activates the cytosolic cGAS-STING signaling pathway. In normal cells, miR-663 mediates mitochondrion-to-nucleus retrograde signaling under reactive oxygen species (ROS) regulation. Mitochondria modulate miR-663 promoter methylation, which governs the expression and supercomplex stability of nuclear-encoded oxidative phosphorylation (OXPHOS) subunits and assembly factors. However, dysfunctional mitochondria induce oxidative stress, elevate methyltransferase activity, and cause miR-663 promoter hypermethylation, suppressing miR-663 expression. Mitochondrial dysfunction also triggers retrograde signaling in primary mitochondrial diseases and contributes to neurodegenerative disorders such as Parkinson’s disease (PD) and Alzheimer’s disease (AD). Current therapeutic strategies targeting mitochondria in neurological diseases focus on 5 main approaches: alleviating oxidative stress, inhibiting mitochondrial fission, enhancing mitochondrial biogenesis, mitochondrial protection, and insulin sensitization. In AD patients, mitochondrial morphological abnormalities and enzymatic defects, such as reduced pyruvate dehydrogenase and α-ketoglutarate dehydrogenase activity, are observed. Platelets and brains of AD patients exhibit diminished cytochrome c oxidase (COX) activity, correlating with mitochondrial dysfunction. To model AD-associated mitochondrial pathology, researchers employ cybrid technology, transferring mtDNA from AD patients into enucleated cells. These cybrids recapitulate AD-related mitochondrial phenotypes, including reduced COX activity, elevated ROS production, oxidative stress markers, disrupted calcium homeostasis, activated stress signaling pathways, diminished mitochondrial membrane potential, apoptotic pathway activation, and increased Aβ42 levels. Furthermore, studies indicate that Aβ aggregates in AD and α‑synuclein aggregates in PD trigger mtDNA release from damaged microglial mitochondria, activating the cGAS-STING pathway. This induces a reactive microglial transcriptional state, exacerbating neurodegeneration and cognitive decline. Targeting the cGAS-STING pathway may yield novel therapeutics for neurodegenerative diseases like AD, though translation from bench to bedside remains challenging. Such research not only deepens our understanding of disease mechanisms but also informs future therapeutic strategies. Investigating the triggers, core molecular pathways, and regulatory networks of mitochondrial retrograde signaling advances our comprehension of intracellular communication and unveils novel pathogenic mechanisms underlying malignancies, neurodegenerative diseases, and type 2 diabetes mellitus. This review summarizes established mitochondrial-nuclear retrograde signaling axes, their roles in interorganellar crosstalk, and pathological consequences of dysregulated communication. Targeted modulation of key molecules and proteins within these signaling networks may provide innovative therapeutic avenues for these diseases.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Through a comprehensive analysis combining network pharmacology prediction and transcriptomics, this study systematically explained the multi-target mechanism of Cyanotis arachnoidea(CA) Gel in improving melasma. A melasma model was induced in female SD rats by progesterone injection combined with ultraviolet B(UVB) irradiation for 40 consecutive days, while the blank control group was only fed routinely. After successful model establishment, the rats were randomly divided into five groups and administered different doses of CA ethanol extract gel(high, medium, and low doses) or arbutin Gel(positive control), which were applied once daily for 28 consecutive days. Subsequently, the levels of superoxide dismutase(SOD), malondialdehyde(MDA), and tyrosinase(TYR) in the skin, serum, and liver tissues were measured. Hematoxylin-eosin(HE) staining and Masson-Fontana staining were used to observe the pathological changes in the tissues. Network pharmacology combined with transcriptomics was employed to identify core targets and pathways, and the differential gene expression was validated by quantitative real-time PCR(qPCR). Pharmacodynamic experiments showed that CA Gel significantly increased SOD activity and decreased MDA and TYR levels in the skin, serum, and liver of model rats. It also improved epidermal thickening, inflammatory infiltration, collagen loss, and melanin deposition. Network pharmacology analysis showed that CA mainly regulated core targets such as signal transducer and activator of transcription 3(STAT3), epidermal growth factor receptor(EGFR), and interleukin-6(IL-6), and modulated the phosphatidylinositol 3-kinase(PI3K)-protein kinase B(AKT) and interleukin-17(IL-17) signaling pathways. Transcriptomic analysis showed that CA Gel significantly downregulated the gene expression of heat shock protein 90β family member 1(Hsp90b1), heat shock protein 90α family member 1(Hsp90aa1), and the key steroid synthesis enzyme cytochrome P450 family 17 subfamily A member 1(Cyp17a1), while upregulating thioredoxin 1(Txn1). qPCR results confirmed that CA Gel regulated oxidative stress and inflammatory response by inhibiting the IL-17 signaling pathway and steroid hormone synthesis. This study, for the first time, reveals the molecular mechanism of CA Gel in improving melasma through multi-target synergistic regulation of oxidative stress, inflammatory response, and hormone metabolism pathways, providing a scientific basis for the treatment of pigmentation diseases with traditional Chinese medicine.
Animals ; Rats ; Female ; Rats, Sprague-Dawley ; Network Pharmacology ; Drugs, Chinese Herbal/administration & dosage* ; Melanosis/metabolism* ; Transcriptome/drug effects* ; Humans ; Superoxide Dismutase/genetics* ; Signal Transduction/drug effects* ; Malondialdehyde/metabolism*

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Retrieving keyframes most relevant to text from small intestine videos with given labels can efficiently and accurately locate pathological regions. However, training directly on raw video data is extremely slow, while learning visual representations from image-text datasets leads to computational inconsistency. To tackle this challenge, a small bowel video keyframe retrieval based on multi-modal contrastive learning (KRCL) is proposed. This framework fully utilizes textual information from video category labels to learn video features closely related to text, while modeling temporal information within a pretrained image-text model. It transfers knowledge learned from image-text multimodal models to the video domain, enabling interaction among medical videos, images, and text data. Experimental results on the hyper-spectral and Kvasir dataset for gastrointestinal disease detection (Hyper-Kvasir) and the Microsoft Research video-to-text (MSR-VTT) retrieval dataset demonstrate the effectiveness and robustness of KRCL, with the proposed method achieving state-of-the-art performance across nearly all evaluation metrics.
Humans ; Video Recording ; Intestine, Small/diagnostic imaging* ; Machine Learning ; Image Processing, Computer-Assisted/methods* ; Algorithms

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins