Water-Stable Ln-MOF as a multi-emitting luminescent sensor for the detection of metal ions and pharmaceuticals.

The development of innovative multi-emission sensors for the rapid and accurate detection of contaminants is both vital and challenging. In this study, utilizing two rigid ligands (H3ICA and H4BTEC), a series of water-stable bimetallic organic frameworks (EuTb-MOFs) were synthesized. Luminescent investigations have revealed that EuTb-MOF-1 exhibits prominent multiple emission peaks, attributed to the distinctive fluorescence characteristics of Eu(III) and Tb(III) ions. Therefore, EuTb-MOF-1 efficiently recognized various metal ions and pharmaceutical compounds through 2D decoded maps. Fe3+ and Pb2+ exhibited significant quenching effects on the luminescence of EuTb-MOF-1, which were attributed to the internal filtering effect and the interaction between Lewis basic sites within EuTb-MOF-1 and Pb2+ ions, respectively. Furthermore, EuTb-MOF-1 demonstrated high sensitivity to sulfonamide antibiotics, with detection limits of 0.037 µM for SMZ and 0.041 µM for SDZ, respectively. In addition, EuTb-MOF-1 was immobilized to prepare MOF-based test strips, enabling direct visual detection of sulfonamides as a portable sensor. With excellent water stability, multi-responsive recognition capabilities, and high sensitivity to specific analytes, EuTb-MOF-1 is a promising candidate for environmental contaminant detection in aquatic systems.

Far-infrared radiation and its therapeutic parameters: A superior alternative for future regenerative medicine?

In future regenerative medicine, far-infrared radiation (FIR) may be an essential component of optical therapy. Many studies have confirmed or validated the efficacy and safety of FIR in various diseases, benefiting from new insights into FIR mechanisms and the excellent performance of many applications. However, the lack of consensus on the biological effects and therapeutic parameters of FIR limits its practical applications in the clinic. In this review, the definition, characteristics, and underlying principles of the FIR are systematically illustrated. We outline the therapeutic parameters of FIR, including the wavelength range, power density, irradiation time, and distance. In addition, the biological effects, potential molecular mechanisms, and preclinical and clinical applications of FIR are discussed. Furthermore, the future development and applications of FIR are described in this review. By applying optimal therapeutic parameters, FIR can influence various cells, animal models, and patients, eliciting diverse underlying mechanisms and offering therapeutic potential for many diseases. FIR could represent a superior alternative with broad prospects for application in future regenerative medicine.

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.

Transcranial direct current stimulation over the right parietal cortex improves the depressive disorder: A preliminary study.

OBJECTIVE: The right posterior parietal cortex is the core brain region of emotional processing and executive control network in the human brain, and the function of the right posterior parietal cortex is decreased in patients with major depressive disorder. This study aims to preliminarily investigate whether the excitation of the right posterior parietal cortex by transcranial direct current stimulation (tDCS) could improve their clinical symptoms. METHODS: In this study, 12 patients with major depressive disorder were given tDCS treatment at Xuanwu Hospital of Capital Medical University and the First Hospital of Hebei Medical University. The stimulating electrode (anode) was placed on the patients' right parietal cortex, whereas the reference electrode (cathode) was placed on the patients' left mastoid. The stimulation intensity was set as 2.0 mA. The patients with depressive disorder were treated for 20 min at a time twice a day for 14 consecutive days. The severity of the clinical symptoms was evaluated using the Hamilton Depression Rating Scale-17 (HDRS-17) and the Hamilton Anxiety Rating Scale (HARS) at before and right after treatment. RESULTS: The HDRS-17 scores of patients with depressive disorder decreased significantly following the tDCS treatment compared with those before treatment (p < .001). Further analysis revealed that the patients' anxiety/somatization, cognitive deficit, retardation, and sleep disorder scores all decreased significantly after the tDCS treatment (p < .05), although there was no significant change in their weight. Moreover, the patients' HARS scores decreased significantly after the tDCS treatment when compared with those before treatment (p < .01). CONCLUSION: The right parietal cortex may be another key stimulation targets to improving the efficacy of tDCS treatment to the patients with major depressive disorder.

Isocitrate dehydrogenases 2-mediated dysfunctional metabolic reprogramming promotes intestinal cancer progression via regulating HIF-1A signaling pathway.

Changes in isocitrate dehydrogenases (IDH) lead to the production of the cancer-causing metabolite 2-hydroxyglutarate, making them a cause of cancer. However, the specific role of IDH in the progression of colon cancer is still not well understood. Our current study provides evidence that IDH2 is significantly increased in colorectal cancer (CRC) cells and actively promotes cell growth in vitro and the development of tumors in vivo. Inhibiting the activity of IDH2, either through genetic silencing or pharmacological inhibition, results in a significant increase in α-ketoglutarate (α-KG), indicating a decrease in the reductive citric acid cycle. The excessive accumulation of α-KG caused by the inactivation of IDH2 obstructs the generation of ATP in mitochondria and promotes the downregulation of HIF-1A, eventually inhibiting glycolysis. This dual metabolic impact results in a reduction in ATP levels and the suppression of tumor growth. Our study reveals a metabolic trait of colorectal cancer cells, which involves the active utilization of glutamine through reductive citric acid cycle metabolism. The data suggests that IDH2 plays a crucial role in this metabolic process and has the potential to be a valuable target for the advancement of treatments for colorectal cancer.

Evaluating drug withdrawal syndrome risks through food and drug administration adverse event reporting system: a comprehensive disproportionality analysis.

Objective: The study aims to identify the drugs associated with drug withdrawal syndrome in the Food and Drug Administration Adverse Event Reporting System (FAERS) and estimate their risks of causing withdrawal syndrome. Methods: All the data were collected from FAERS from the first quarter of 2004 to the third quarter of 2023. Disproportionality analyses of odds ratio (ROR) and proportional reported ratio were conducted to identify potential adverse effects signal of drug withdrawal syndrome. Results: A total of 94,370 reports related to withdrawal syndrome from the data. The top 50 drugs with most frequency reported were analyzed, and 29 exhibited a positive signal based on the number of reports. The top three categories of drugs with positive signals included opioids, antidepressant drugs and antianxiety drugs. Other classifications included opioid antagonist, muscle relaxant, antiepileptic drugs, analgesics, hypnotic sedative drugs and antipsychotic drugs. Conclusion: Our analysis of FAERS data yielded a comprehensive list of drugs associated with withdrawal syndrome. This information is vital for healthcare professionals, including doctors and pharmacists, as it aids in better recognition and management of withdrawal symptoms in patients undergoing treatment with these medications.

Risks of anxiety disorders, depressive disorders, and sleep disorders in patients with dengue fever: A nationwide, population-based cohort study.

BACKGROUND: Dengue virus (DENV) infection, a common mosquito-borne disease, has been linked to several mental disorders like depression and anxiety. However, the temporal risk of these disorders after DENV infection is not well studied. METHODS: This population-based cohort study encompassed 45,334 recently lab-confirmed dengue patients in Taiwan spanning 2002 to 2015, matched at a 1:5 ratio with non-dengue individuals based on age, gender, and residence (n = 226,670). Employing subdistribution hazard regression analysis, we assessed the immediate (<3 months), intermediate (3-12 months), and prolonged (>12 months) risks of anxiety disorders, depressive disorders, and sleep disorders post DENV infection. Corrections for multiple comparisons were carried out using the Benjamini-Hochberg procedure. RESULTS: A significant increase in depressive disorder risk across all timeframes post-infection was observed (<3 months [aSHR 1.90, 95% CI 1.20-2.99], 3-12 months [aSHR 1.68, 95% CI 1.32-2.14], and >12 months [aSHR 1.14, 95% CI 1.03-1.25]). Sleep disorder risk was higher only during 3-12 months (aSHR 1.55, 95% CI 1.18-2.04). No elevated anxiety disorder risk was found. Subgroup analysis of hospitalized dengue patients showed increased risk of anxiety disorders within 3 months (aSHR 2.14, 95% CI 1.19-3.85) and persistent risk of depressive disorders across all periods. Hospitalized dengue patients also had elevated sleep disorder risk within the first year. CONCLUSION: Dengue patients exhibited significantly elevated risks of depressive disorders in both the short and long term. However, dengue's impact on sleep disorders and anxiety seems to be short-lived. Further research is essential to elucidate the underlying mechanisms.

A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity during Brain Development.

OBJECTIVE: Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored. METHODS: Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data. RESULTS: Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them. CONCLUSION: This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability. SIGNIFICANCE: The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.

Effects of endogenous testosterone on oscillatory activity during verbal working memory in youth.

Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6-14 years-old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole-brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone-related effects in theta (4-7 Hz) and alpha (8-14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left-lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex-specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age.

CircCFL1 Promotes TNBC Stemness and Immunoescape via Deacetylation-Mediated c-Myc Deubiquitylation to Facilitate Mutant TP53 Transcription.

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer. TP53, which has a mutation rate of ≈70%-80% in TNBC patients, plays oncogenic roles when mutated. However, whether circRNAs can exert their effects on TNBC through regulating mutant TP53 has not been well evaluated. In this study, circCFL1, which is highly expressed in TNBC cells and tissues and has prognostic potential is identified. Functionally, circCFL1 promoted the proliferation, metastasis and stemness of TNBC cells. Mechanistically, circCFL1 acted as a scaffold to enhance the interaction between HDAC1 and c-Myc, further promoting the stability of c-Myc via deacetylation-mediated inhibition of K48-linked ubiquitylation. Stably expressed c-Myc further enhanced the expression of mutp53 in TNBC cells with TP53 mutations by directly binding to the promoter of TP53, which promoted the stemness of TNBC cells via activation of the p-AKT/WIP/YAP/TAZ pathway. Moreover, circCFL1 can facilitate the immune escape of TNBC cells by promoting the expression of PD-L1 and suppressing the antitumor immunity of CD8+ T cells. In conclusion, the results revealed that circCFL1 plays an oncogenic role by promoting the HDAC1/c-Myc/mutp53 axis, which can serve as a potential diagnostic biomarker and therapeutic target for TNBC patients with TP53 mutations.

KPNA2 promotes the progression of gastric cancer by regulating the alternative splicing of related genes.

RNA-binding proteins (RBPs) play critical roles in genome regulation. In this study, we explored the latent function of KPNA2, which is an essential member of the RBP family, in the regulation of alternative splicing (AS) in gastric cancer (GC). We analyzed the role of KPNA2 in regulating differential expression and AS via RNA sequencing (RNA-seq) and improved RNA immunoprecipitation sequencing (iRIP-seq). Clinical specimens were used to analyze the associations between KPNA2 expression and clinicopathological characteristics. CCK8 assays, transwell assays and wound healing assays were performed to explore the effect of KPNA2/WDR62 on GC cell progression. KPNA2 was shown to be highly expressed in GC cells and tissues and associated with lymph node metastases. KPNA2 promoted the proliferation, migration and invasion of GC cells and primarily regulated exon skipping, alternative 3's splice sites (A3SSs), alternative 5' splice sites (A5SSs), and cassette exons. We further revealed that KPNA2 participated in biological processes related to cell proliferation, and the immune response in GC via the regulation of transcription. In addition, KPNA2 preferentially bound to intron regions. Notably, KPNA2 regulated the A3SS AS mode of WDR62, and upregulation of WDR62 reversed the KPNA2 downregulation-induced inhibition of GC cell proliferation, migration and invasion. Finally, we discovered that the AS of immune-related molecules could be regulated by KPNA2. Overall, our results demonstrated for the first time that KPNA2 functions as an oncogenic splicing factor in GC that regulated the AS and differential expression of GC-related genes, and KPNA2 may be a potential target for GC treatment.

Risk of severe dengue during secondary infection: A population-based cohort study in Taiwan.

BACKGROUND: Dengue poses a significant public health concern. Secondary dengue infections with different dengue virus (DENV) serotypes have been linked to an increased risk of severe dengue. This study aimed to assess the risk of severe dengue during secondary infection in Taiwan. METHODS: A retrospective cohort study was conducted using Taiwan's National Health Insurance Research Database to identify dengue cases with secondary dengue infection born after 1944 from 2014 to 2015. Ten matched patients with primary infection were selected as controls using propensity score matching for each secondary dengue infection case. The odds ratio (OR) for severe dengue in secondary versus primary infections was calculated using conditional logistic regression. RESULTS: This study included 357 cases with secondary dengue infection and 3570 matched controls. The risk of severe dengue was found to be 7.8% in individuals with secondary infection, compared to 3.8% in those with primary dengue infection. Secondary infection significantly increased the risk of severe dengue (OR 2.13, 95% CI: 1.40-3.25, P = 0.0004). Notably, a significant association between secondary infection and severe dengue was observed only when the interval between the first and secondary infection was greater than two years (OR 3.19, 95% CI 2.04-5.00, P < 0.0001). CONCLUSION: Secondary dengue infection significantly increases the risk of severe disease in Taiwan, particularly when the interval between infections is over two years. Healthcare professionals should maintain heightened vigilance for individuals with a history of previous dengue infection, particularly if their initial diagnosis was more than two years prior.

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia.

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu-Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu-Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

Metabolic score and its components are associated with carotid plaque prevalence in young adults.

PURPOSE: No study has comprehensively assessed the relationship of metabolic factors including insulin resistance, hypertension, hyperuricemia, and hypercholesterolemia with the development of carotid plaque. Therefore, we constructed metabolic scores based on the above metabolic factors and examined its association with carotid plaque in young and older Chinese adults. METHODS: This study included 17,396 participants who underwent carotid ultrasound examinations, including 14,173 young adults (<65 years) and 3,223 older adults (≥65 years). Individual metabolic score was calculated using triglyceride-glucose (TyG) index, mean arterial pressure (MAP), uric acid, and total cholesterol (TC). Logistic regression models were conducted to examine the role of metabolic score and its components in the prevalence of carotid plaque. The nonlinear relationship was examined using restricted cubic spline regression. Meanwhile, subgroup, interaction, and sensitivity analyses were conducted. RESULTS: The multivariate logistic regression analysis showed that TyG (OR: 1.088; 95%CI: 1.046-1.132), MAP (OR: 1.121; 95%CI: 1.077-1.168), TC (OR: 1.137; 95%CI: 1.094-1.182) and metabolic score (OR: 1.064; 95%CI: 1.046-1.082) were associated with carotid plaque prevalence in young adults rather than older adults. The nonlinear association was not observed for metabolic scores and carotid plaque. Subgroup analyses showed significant associations between metabolic scores and carotid plaque prevalence in men, women, normal-weight, and overweight young adults. No interaction of metabolic score with sex and BMI were observed. CONCLUSIONS: The results support that control of TyG, MAP, TC, and metabolic scores is a key point in preventing the prevalence of carotid plaque in the young adults.

Temporal-spatial deciphering mental subtraction in the human brain.

Mental subtraction, involving numerical processing and operation, requires a complex interplay among several brain regions. Diverse studies have utilized scalp electroencephalograph, electrocorticogram, or functional magnetic resonance imaging to resolve the structure pattern and functional activity during subtraction operation. However, a high resolution of the spatial-temporal understanding of the neural mechanisms involved in mental subtraction is unavailable. Thus, this study obtained intracranial stereoelectroencephalography recordings from 20 patients with pharmacologically resistant epilepsy. Specifically, two sample-delayed mismatch paradigms of numeric comparison and subtracting results comparison were used to help reveal the time frame of mental subtraction. The brain sub-regions were chronologically screened using the stereoelectroencephalography recording for mental subtraction. The results indicated that the anterior cortex, containing the frontal, insular, and parahippocampous, worked for preparing for mental subtraction; moreover, the posterior cortex, such as parietal, occipital, limbic, and temporal regions, cooperated during subtraction. Especially, the gamma band activities in core regions within the parietal-cingulate-temporal cortices mediated the critical mental subtraction. Overall, this research is the first to describe the spatiotemporal activities underlying mental subtraction in the human brain. It provides a comprehensive insight into the cognitive control activity underlying mental arithmetic. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-023-09937-z.

Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis.

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy.

PURPOSE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.