Objective To explore the differences in heartbeat-evoked response (HER) under drug-related cues and neutral cues in individuals with heroin use disorder (HUD), and analyze the correlation between HER potentials and immediate cue-induced craving scores. Methods Fifty HUD participants were recruited from the Chang’an Compulsory Isolation Drug Rehabilitation Center in Shaanxi Province from June to September 2024. Simultaneous acquisition of 64-channel electroencephalography (EEG) and electrocardiogram signals was performed. Twenty alternating segments of drug-related and neutral cue videos were presented, and participants rated their subjective craving after each segment using visual analogue scale (VAS) scores. Scalp EEG data were source analyzed to obtain cortical EEG signals and corresponding HER. Short-time Fourier transform was used to calculate the power spectral density (PSD) of EEG within a time window from 100 ms before the R-peak to 500 ms after it, using the R-peak as the time zero point. Cluster-based permutation testing was used to analyze PSD differences between drug-related and neutral cues in the HUD individuals. Pearson correlation analysis was performed to evaluate the correlation between HER potentials and VAS scores. Results In the 350–420 ms time window, HER potentials in the left posterior parietal, temporal, and posterior cingulate cortices were significantly lower under drug-related cues compared to neutral cues (P＜0.01); in the 140–210 ms time window, HER potentials in the right prefrontal cortex were significantly higher under drug-related cues compared to neutral cues (P＜0.01). Correlation analysis showed that HER potentials in the left temporal and left posterior cingulate cortices were significantly negatively correlated with VAS scores (P＜0.05). Drug-related cues enhanced PSD of γ power (30–100 Hz) in salience network (fronto-insular), parietal and occipital regions (P＜0.05). PSD integrations of low-γ power (40–60 Hz) in parietal region (350–400 ms) and high-γ power (70–100 Hz) in left salience network (fronto-parietal) and occipital regions (300–350 ms) were positively correlated with VAS scores (P＜0.05). Conclusions Drug-related cues may modulate cortical activity related to heartbeat perception in HUD individuals, and such dynamic changes in both time and frequency domains are stably associated with subjective craving.

Purpose: Metastatic rectal cancer (mRC) is a highly lethal and complex disease that demands a multidisciplinary treatment approach. However, the clinical effectiveness of radiotherapy (RT) for de novo mRC remains controversial and uncertain. Methods: This retrospective cohort study examined medical records from Kaohsiung Chang Gung Memorial Hospital for patients with histologically confirmed de novo mRC diagnosed between January 2015 and December 2020. All patients received standard systemic therapy and radical surgery when feasible. The primary outcome, overall survival (OS), was assessed using the Kaplan-Meier method. Multivariable analysis was performed using a Cox regression model. Results: Among 271 patients included in the analysis, 117 received RT and 154 did not. The median OS was significantly longer in the RT group compared with the non-RT group (27.8 months vs. 21.9 months; P=0.046). Multivariate analysis identified several independent predictors of OS: age ≥65 years (hazard ratio [HR], 1.69; 95% confidence interval [CI], 1.26–2.27; P=0.001), primary tumor resection (HR, 2.62; 95% CI, 1.90–3.61; P<0.001), M1b or M1c disease (HR, 1.97; 95% CI, 1.44–2.69; P<0.001), and receipt of RT (HR, 1.41; 95% CI, 1.02–1.94; P=0.036). Conclusion RT significantly improves OS in patients with mRC, underscoring its role in treatment strategies. These findings support its inclusion in therapeutic protocols and highlight the need for larger, multicenter trials to confirm and extend these results.

Background: Cerebral hypoxia-ischemia impairs brain development in extremely preterm infants and is associated with poor neurological outcomes. Near-infrared spectroscopy (NIRS) is a noninvasive continuous monitoring method for regional cerebral oxygen saturation (rcSO2).Purpose: This study evaluated the clinical feasibility and neurological impact of a neurocritical care bundle that incorporates prolonged multidisciplinary hemodynamic monitoring and a stepwise management algorithm. Methods: Preterm infants with a gestational age (GA) ≤28 weeks or birth weight (BW) ≤1,000 g were prospectively enrolled in a bundle group subjected to NIRS for rcSO2, electrical cardiometry for cardiac output, and daily brain and cardiac echography during the first 72 hours of life. Monitoring was repeated weekly in the first month and then monthly until discharge or the term-equivalent age (TEA) was reached. We implemented a stepwise management algorithm for treating cerebral hypoxia. The primary outcome was a composite of mortality and adverse neurological events (structural abnormalities or electroencephalogram-confirmed seizures) before discharge. The secondary outcomes were the physiological pattern of rcSO2 within the initial 72 hours and up to discharge or TEA. Results: Thirty preterm infants (GA, 27.1±2.0 weeks; BW, 830±225 g) were enrolled in the bundle group. The mean time-averaged rcSO2 (66.8%±10.3%) was not associated with GA or BW. However, postnatal age appeared to influence physiological rcSO2 changes, given that rcSO2 values were higher during the initial 72 hours than at subsequent intervals. Seven infants (23.3%) had poor outcomes and significantly lower time-averaged rcSO2 (51.1% [50.0%–65.2%] vs. 71.8% [67.1%–73.1%], P=0.002). Multivariate regression indicated that a lower rcSO2 was an independent risk factor, and a 65% threshold showed an optimal predictive value for poor outcomes. Conclusion The neurocritical care bundle helped identify preterm infants at risk of cerebral hypoxia, and lower rcSO2 was an independent risk factor for composite mortality and adverse neurological outcomes.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Network pharmacology and molecular docking were employed to predict the mechanism of Zhizhu Decoction in regulating macrophage polarization to reduce adipose tissue inflammation in obese children, and animal experiments were then carried out to validate the prediction results. The active ingredients and targets of Zhizhu Decoction were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The inflammation related targets in the adipose tissue of obese children were searched against GeneCards, OMIM, and DisGeNET, and a drug-disease-target network was established. STRING was used to construct a protein-protein interaction(PPI) network and screen for core targets. R language was used to carry out Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses. AutoDock was used for the molecular docking between core targets and active ingredients. 24 SPF grade 6-week C57B/6J male mice were adaptively fed for 1 week, and 8 mice were randomly selected as the blank group. The remaining 16 mice were fed with high-fat diet for 8 weeks to onstruct a high-fat diet induced mouse obesity model. After successful modeling, the 16 mice were randomly divided into model group and Zhizhu Decoction group, with 8 mice in each group. Zhizhu Decoction group was intervened by gavage for 14 days, once a day. Blank group and model group were given an equal amount of sterile double distilled water(ddH_2O) by gavage daily. After the last gavage, serum and inguinal adipose tissue were collected from mice for testing. The morphology of inguinal adipose tissue was observed by hematoxylin-eosin(HE) staining, the levels of inflammatory factors interleukin-6(IL-6) and tumor necrosis factor-α(TNF-α)were detected by enzyme-linked immunosorbent assay(ELISA), and the protein expression of macrophage marker molecule nitric oxide synthase(iNOS) and epidermal growth factor like hormone receptor 1(F4/80) was detected by immunofluorescence staining. Network pharmacology predicted luteolin, naringenin, and nobiletin as the main active ingredients in Zhizhu Decoction and 15 core targets. KEGG pathway enrichment analysis revealed involvement in the key signaling pathway of nuclear factor κB(NF-κB). Molecular docking showed that the active ingredients of Zhizhu Decoction bound well to the core targets. Animal experiment showed that compared with the model group, Zhizhu Decoction reduced the distribution of inflammatory cytokines in the inguinal adipose tissue of mice, lowered the levels of TNF-α and IL-6 in the serum(P<0.05, P<0.01), and down-regulated the expression of iNOS and F4/80(P<0.05). The results showed that the active ingredients in Zhizhu Decoction, such as luteolin, naringenin, and nobiletin, inhibit the aggregation of macrophages in adipose tissue, downregulate their classic activated macrophage(M1) polarization, reduce the expression of inflammatory factors IL-6 and TNF-α, and thus improve adipose tissue inflammation in obese mice.
Animals ; Drugs, Chinese Herbal/pharmacology* ; Molecular Docking Simulation ; Adipose Tissue/immunology* ; Mice ; Male ; Humans ; Network Pharmacology ; Macrophages/immunology* ; Mice, Inbred C57BL ; Child ; Protein Interaction Maps/drug effects* ; Obesity/genetics* ; Inflammation/drug therapy*

This study aims to investigate the molecular mechanism by which naringin alleviates cerebral ischemia/reperfusion(CI/R) injury through DRP1/LRRK2/MCU signaling axis. A total of 60 SD rats were randomly divided into the sham group, the model group, the sodium Danshensu group, and low-, medium-, and high-dose(50, 100, and 200 mg·kg~(-1)) naringin groups, with 10 rats in each group. Except for the sham group, a transient middle cerebral artery occlusion/reperfusion(tMCAO/R) model was established in SD rats using the suture method. Longa 5-point scale was used to assess neurological deficits. 2,3,5-Triphenyl tetrazolium chloride(TTC) staining was used to detect the volume percentage of cerebral infarction in rats. Hematoxylin-eosin(HE) staining and Nissl staining were employed to assess neuronal structural alterations and the number of Nissl bodies in cortex, respectively. Western blot was used to determine the protein expression levels of B-cell lymphoma-2 gene(Bcl-2), Bcl-2-associated X protein(Bax), cleaved cysteine-aspartate protease-3(cleaved caspase-3), mitochondrial calcium uniporter(MCU), microtubule-associated protein 1 light chain 3(LC3), and P62. Mitochondrial structure and autophagy in cortical neurons were observed by transmission electron microscopy. Immunofluorescence assay was used to quantify the fluorescence intensities of MCU and mitochondrial calcium ion, as well as the co-localization of dynamin-related protein 1(DRP1) with leucine-rich repeat kinase 2(LRRK2) and translocase of outer mitochondrial membrane 20(TOMM20) with LC3 in cortical mitochondria. The results showed that compared with the model group, naringin significantly decreased the volume percentage of cerebral infarction and neurological deficit score in tMCAO/R rats, alleviated the structural damage and Nissl body loss of cortical neurons in tMCAO/R rats, inhibited autophagosomes in cortical neurons, and increased the average diameter of cortical mitochondria. The Western blot results showed that compared to the sham group, the model group exhibited increased levels of cleaved caspase-3, Bax, MCU, and the LC3Ⅱ/LC3Ⅰ ratio in the cortex and reduced protein levels of Bcl-2 and P62. However, naringin down-regulated the protein expression of cleaved caspase-3, Bax, MCU and the ratio of LC3Ⅱ/LC3Ⅰ ratio and up-regulated the expression of Bcl-2 and P62 proteins in cortical area. In addition, immunofluorescence analysis showed that compared with the model group, naringin and positive drug treatments significantly decreased the fluorescence intensities of MCU and mitochondrial calcium ion. Meanwhile, the co-localization of DRP1 with LRRK2 and TOMM20 with LC3 in cortical mitochondria was also decreased significantly after the intervention. These findings suggest that naringin can alleviate cortical neuronal damage in tMCAO/R rats by inhibiting DRP1/LRRK2/MCU-mediated mitochondrial fragmentation and the resultant excessive mitophagy.
Animals ; Rats, Sprague-Dawley ; Reperfusion Injury/genetics* ; Flavanones/administration & dosage* ; Rats ; Dynamins/genetics* ; Male ; Brain Ischemia/genetics* ; Protein Serine-Threonine Kinases/genetics* ; Signal Transduction/drug effects* ; Humans ; Drugs, Chinese Herbal/administration & dosage*

To investigate the effects and mechanisms of the water extract from Sorbus tianschanica(STE) on asthmatic airway inflammation, the mice were randomly divided into six groups, including a control group, a model group, a positive drug dexamethasone group(2 mg·kg~(-1)), a low-dose STE group(1 g·kg~(-1)), a medium-dose STE group(2 g·kg~(-1)), and a high-dose STE group(4 g·kg~(-1)). Except for the control group, all groups were subjected to ovalbumin induction to establish an asthma mouse model. The anti-inflammatory effects of STE were evaluated by examining pathological changes in lung tissue and measuring the levels of interleukin(IL)-4 and IL-5 in bronchoalveolar lavage fluid(BALF). Transcriptomic and proteomic methods were further employed to analyze differentially expressed genes and proteins, as well as their associated signaling pathways in lung tissue. Subsequently, the expression changes of key genes were verified by reverse transcription-quantitative polymerase chain reaction(RT-qPCR), and immunohistochemistry and Western blot methods were used to explore the regulatory mechanisms of STE in the pathogenesis of asthma in mice. Molecular docking was performed by using AutoDock Vina software to evaluate the binding affinity of the main active components in STE with the target proteins, including phosphatidylinositol-3-kinase catalytic subunit α(PIK3CA), Toll-like receptor 4(TLR4), protein kinase B1(Akt1), and matrix metallopeptidase 9(MMP9). The results showed significant inflammatory cell infiltration and fibrous tissue proliferation in the lung tissue of mice in the model group. However, these pathological changes were markedly reduced following STE intervention. Compared with those of the control group, the expression levels of IL-4 and IL-5 in the BALF of the model group were significantly increased but notably decreased following STE intervention. Transcriptomic and proteomic analyses identified key genes and proteins associated with allergic asthma, including tumor necrosis factor(TNF), IL-6, TLR4, PIK3CA, and MMP9. RT-qPCR validation revealed that high-dose STE intervention significantly downregulated the expressions of PIK3CA, IL-6, Akt1, MMP9, IL-13, nuclear factor-kappa B(NF-κB), TNF, CXC motif chemokine ligand 1(CXCL1), and TLR4 mRNAs and significantly upregulated the expression of signal transducer and activator of transcription 1(STAT1) mRNA. Western blot and immunohistochemical analyses confirmed that STE significantly downregulated the expressions of MMP9, TLR4, PIK3CA, and phosphorylated protein kinase B(p-Akt) in lung tissue of asthmatic mice. Moreover, molecular docking demonstrated that kaempferol-3,7-diglucoside, isoquercitrin, quercetin-3-gentiobioside, and hyperoside in STE exhibited stable binding affinities with PIK3CA, TLR4, Akt1, and MMP9, suggesting that the active components may exert anti-inflammatory effects by targeting and modulating asthma-related signaling pathways. In summary, STE exerts anti-asthmatic effects by inhibiting the expressions of PIK3CA, MMP9, p-Akt, and TLR4 and regulating the TLR4/PI3K/Akt/MMP9 signaling pathway.
Animals ; Asthma/metabolism* ; Toll-Like Receptor 4/metabolism* ; Signal Transduction/drug effects* ; Mice ; Phosphatidylinositol 3-Kinases/metabolism* ; Proto-Oncogene Proteins c-akt/metabolism* ; Matrix Metalloproteinase 9/metabolism* ; Mice, Inbred BALB C ; Drugs, Chinese Herbal/administration & dosage* ; Female ; Humans ; Lung/immunology* ; Male