Mass Production of Carbon Nanotube Transistor Biosensors for Point-of-Care Tests.

Low-dimensional semiconductor-based field-effect transistor (FET) biosensors are promising for label-free detection of biotargets while facing challenges in mass fabrication of devices and reliable reading of small signals. Here, we construct a reliable technology for mass production of semiconducting carbon nanotube (CNT) film and FET biosensors. High-uniformity randomly oriented CNT films were prepared through an improved immersion coating technique, and then, CNT FETs were fabricated with coefficient of performance variations within 6% on 4-in. wafers (within 9% interwafer) based on an industrial standard-level process. The CNT FET-based ion sensors demonstrated threshold voltage standard deviations within 5.1 mV at each ion concentration, enabling direct reading of the concentration information based on the drain current. By integrating bioprobes, we achieved detection of biosignals as low as 100 aM through a plug-and-play portable detection system. The reliable technology will contribute to commercial applications of CNT FET biosensors, especially in point-of-care tests.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Precise Differentiation of Wobble-Type Allele via Ratiometric Design of a Ligase Chain Reaction-Based Electrochemical Biosensor for CYP2C19*2 Genotyping of Clinical Samples.

Due to the comparable stability between the perfect-base pair and the wobble-base pair, a precise differentiation of the wobble-type allele has remained a challenge, often leading to false results. Herein, we proposed a ligase chain reaction (LCR)-based ratiometric electrochemical DNA sensor, namely, R-eLCR, for a precise typing of the wobble-type allele, in which the traditionally recognized "negative" signal of wobble-base pair-mediated amplification was fully utilized as a "positive" one and a ratiometric readout mode was employed to ameliorated the underlying potential external influence and improved its detection accuracy in the typing of the wobble-type allele. The results showed that the ratio between current of methylene blue (IMB) and current of ferrocene (IFc) was partitioned in three regions and three types of wobble-type allele were thus precisely differentiated (AA homozygote: IMB/IFc > 2; GG homozygote: IMB/IFc < 1; GA heterozygote: 1 < IMB/IFc < 2); the proposed R-eLCR successfully discriminated the three types of CYP2C19*2 allele in nine cases of human whole blood samples, which was consistent with those of the sequencing method. These results evidence that the proposed R-eLCR can serve as an accurate and robust alternative for the identification of wobble-type allele, which lays a solid foundation and holds great potential for precision medicine.

Amplification-Free Detection of SARS-CoV-2 Down to Single Virus Level by Portable Carbon Nanotube Biosensors.

The rapid and sensitive detection of trace-level viruses in a simple and reliable way is of great importance for epidemic prevention and control. Here, a multi-functionalized floating gate carbon nanotube field effect transistor (FG-CNT FET) based biosensor is reported for the single virus level detection of SARS-CoV-2 virus antigen and RNA rapidly with a portable sensing platform. The aptamers functionalized sensors can detect SARS-CoV-2 antigens from unprocessed nasopharyngeal swab samples within 1 min. Meanwhile, enhanced by a multi-probe strategy, the FG-CNT FET-based biosensor can detect the long chain RNA directly without amplification down to single virus level within 1 min. The device, constructed with packaged sensor chips and a portable sensing terminal, can distinguish 10 COVID-19 patients from 10 healthy individuals in clinical tests both by the RNAs and antigens by a combination detection strategy with an combined overall percent agreement (OPA) close to 100%. The results provide a general and simple method to enhance the sensitivity of FET-based biochemical sensors for the detection of nucleic acid molecules and demonstrate that the CNT FG FET biosensor is a versatile and reliable integrated platform for ultrasensitive multibiomarker detection without amplification and has great potential for point-of-care (POC) clinical tests.

Cortex-Wide Dynamics of Intrinsic Electrical Activities: Propagating Waves and Their Interactions.

Cortical circuits generate patterned activities that reflect intrinsic brain dynamics that lay the foundation for any, including stimuli-evoked, cognition and behavior. However, the spatiotemporal organization properties and principles of this intrinsic activity have only been partially elucidated because of previous poor resolution of experimental data and limited analysis methods. Here we investigated continuous wave patterns in the 0.5-4 Hz (delta band) frequency range on data from high-spatiotemporal resolution optical voltage imaging of the upper cortical layers in anesthetized mice. Waves of population activities propagate in heterogeneous directions to coordinate neuronal activities between different brain regions. The complex wave patterns show characteristics of both stereotypy and variety. The location and type of wave patterns determine the dynamical evolution when different waves interact with each other. Local wave patterns of source, sink, or saddle emerge at preferred spatial locations. Specifically, "source" patterns are predominantly found in cortical regions with low multimodal hierarchy such as the primary somatosensory cortex. Our findings reveal principles that govern the spatiotemporal dynamics of spontaneous cortical activities and associate them with the structural architecture across the cortex.SIGNIFICANCE STATEMENT Intrinsic brain activities, as opposed to external stimulus-evoked responses, have increasingly gained attention, but it remains unclear how these intrinsic activities are spatiotemporally organized at the cortex-wide scale. By taking advantage of the high spatiotemporal resolution of optical voltage imaging, we identified five wave pattern types, and revealed the organization properties of different wave patterns and the dynamical mechanisms when they interact with each other. Moreover, we found a relationship between the emergence probability of local wave patterns and the multimodal structure hierarchy across cortical areas. Our findings reveal the principles of spatiotemporal wave dynamics of spontaneous activities and associate them with the underlying hierarchical architecture across the cortex.

A novel fluorescence ratio probe based on dual-emission carbon dots for highly selective and sensitive detection of chlortetracycline and cell imaging.

The novel dual-emission carbon dots (DECDs) for highly selective and sensitive recognition of chlortetracycline (CTC) and cell imaging were synthesized successfully by one-step synthesis. The obtained DECDs possessed two fluorescence peaks (345 nm and 450 nm) and showed specific response to CTC, resulting in a decrease in fluorescence intensity at 345 nm, a blue shift, and an increase in fluorescence intensity at 450 nm. The obtained DECDs exhibited highly selective response to CTC and not to its analogues, such as tetracycline, doxycycline, and oxytetracycline. Thus, an excellent ratiometric probe for the detection of CTC was fabricated successfully and used for the detection of CTC in real samples with the detection limit (LOD) of 16.45 nM. More importantly, the DECDs were used for quantitative detection of CTC in living cells, which demonstrated excellent biocompatibility and broad prospects in biomedicine application. Finally, the excellent selectivity of DECDs toward CTC was attributed to the FRET mechanism and the formation of complexes.

Carbon Nanotube Field-Effect Transistor Biosensor for Ultrasensitive and Label-Free Detection of Breast Cancer Exosomal miRNA21.

Tumor-derived exosomal miRNAs may have important functions in the onset and progression of cancers and are potential biomarkers for early diagnosis and prognosis monitoring. Yet, simple, sensitive, and label-free detection of exosomal miRNAs remains challenging. Herein, an ultrasensitive, label-free, and stable field-effect transistor (FET) biosensor based on a polymer-sorted high-purity semiconducting carbon nanotube (CNT) film is reported to detect exosomal miRNA. Different from conventional CNT FETs, the CNT FET biosensors employed a floating gate structure using an ultrathin Y2O3 as an insulating layer, and assembled Au nanoparticles (AuNPs) on Y2O3 as linkers to anchor probe molecules. A thiolated oligonucleotide probe was immobilized on the AuNP surface of the sensing area, after which miRNA21 was detectable by monitoring the current change before and after hybridization between the immobilized DNA probe and target miRNA. This method achieved both high sensitivity (LOD: 0.87 aM) and high specificity. Furthermore, the FET biosensor was employed to test clinical plasma samples, showing significant differences between healthy people and breast cancer patients. The CNT FET biosensor shows the potential applications in the clinical diagnosis of breast cancer.

Association of chemotherapy with survival in stage II colon cancer patients who received radical surgery: a retrospective cohort study.

BACKGROUND: It remains controversial whether patients with Stage II colon cancer would benefit from chemotherapy after radical surgery. This study aims to assess the real effectiveness of chemotherapy in patients with stage II colon cancer undergoing radical surgery and to construct survival prediction models to predict the survival benefits of chemotherapy. METHODS: Data for stage II colon cancer patients with radical surgery were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (1:1) was performed according to receive or not receive chemotherapy. Competitive risk regression models were used to assess colon cancer cause-specific death (CSD) and non-colon cancer cause-specific death (NCSD). Survival prediction nomograms were constructed to predict overall survival (OS) and colon cancer cause-specific survival (CSS). The predictive abilities of the constructed models were evaluated by the concordance indexes (C-indexes) and calibration curves. RESULTS: A total of 25,110 patients were identified, 21.7% received chemotherapy, and 78.3% were without chemotherapy. A total of 10,916 patients were extracted after propensity score matching. The estimated 3-year overall survival rates of chemotherapy were 0.7% higher than non- chemotherapy. The estimated 5-year and 10-year overall survival rates of non-chemotherapy were 1.3 and 2.1% higher than chemotherapy, respectively. Survival prediction models showed good discrimination (the C-indexes between 0.582 and 0.757) and excellent calibration. CONCLUSIONS: Chemotherapy improves the short-term (43 months) survival benefit of stage II colon cancer patients who received radical surgery. Survival prediction models can be used to predict OS and CSS of patients receiving chemotherapy as well as OS and CSS of patients not receiving chemotherapy and to make individualized treatment recommendations for stage II colon cancer patients who received radical surgery.

Prediction of oocyte quality using mRNA transcripts screened by RNA sequencing of human granulosa cells.

RESEARCH QUESTION: Can RNA transcripts of granulosa cells be used to assess oocyte quality? The possibility of predicting the developmental competence of oocytes by RNA sequencing analysis of granulosa cells was evaluated. DESIGN: Granulosa cell samples were collected from 29 women undergoing assisted reproductive technology treatment and divided into two groups: 14 samples from the high blastocyst rate group and 15 from the low blastocyst rate group. Ten samples from each group were selected for RNA sequencing. RESULTS: A total of 129 differentially expressed genes associated with high developmental competence of oocytes were identified. COL1A2, renin and COL1A1 were selected and further examined by quantitative real-time polymerase chain reaction (qRT-PCR). Expression levels of COL1A2 and renin by qRT-PCR were consistent with the results of RNA sequencing. CONCLUSION: RNA sequencing data could provide novel marker genes for the non-invasive evaluation of oocyte quality and embryo developmental competence.

Biological nitrogen removal capability and pathways analysis of a novel low C/N ratio heterotrophic nitrifying and aerobic denitrifying bacterium (Bacillus thuringiensis strain WXN-23).

A novel heterotrophic nitrification and aerobic denitrification (HNAD) bacteria, identified as Bacillus thuringiensis strain WXN-23, was isolated from husk feed filtrate of a pig farm. It was the first report of Bacillus thuringiensis with the capability for HNAD and could adapt to the condition of low Carbon/Nitrogen (C/N) ratio. Nitrogen could be efficiently removed by the strain WXN-23 in simulated wastewater, be it in single or mixed form nitrogen sources. The nitrogen balance revealed that 63.5% of the initial nitrogen (5.32 mg) was lost in the form of N2. The conditions for maximum total nitrogen (TN) removal efficiency (95.996%) were shaking speed of 126.89 r/min, a carbon C/N ratio of 5.91, the temperature of 32.81 °C, and a pH value of 8.17. The nitrification-denitrification metabolic pathway (NH4+-N→NH2OH→NO2--N→NO3--N→NO2--N→NO→N2O→N2) under aerobic conditions was determined on the basic of characteristic of N removal, N balance analysis, enzyme assay and functional genes amplification results. Strain WXN-23 was effective at wastewater treatment, with TN, NH4+-N, NO3--N and NO2--N removal efficiencies of 82.12%, 86.74%, 90.74% and 100%, respectively.

Evaluation of the effect of Dahuang-Mudan decoction on TNBS-induced colitis using UPLC-QTOF/MS-based metabolomic analysis.

Dahuang-Mudan decoction (DMD) is a formula that has been widely used as a complementary treatment for inflammatory bowel disease (IBD). However, the mechanism of action of DMD in IBD has not been clearly elucidated. Therefore, we developed a metabolomics-based method to evaluate the effects and potential mechanisms of DMD in a 2,4,6-trinitobenzene sulfonic acid (TNBS)-induced colitis model. The ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/QTOF-MS) method combined with multiple analysis approaches including principal component analysis, partial least square discriminant analysis and orthogonal partial least square discriminant analysis were used to investigate the different urinary metabolites. We identified 29 potential biomarkers of TNBS-induced colitis that returned to normal conditions after DMD administration. Pathway analysis indicated that changes in these metabolites were associated with cysteine and methionine metabolism, citric acid cycle, glycolysis and glycolic regeneration, pyruvate metabolism, biosynthesis of valine, leucine and isoleucine, biosynthesis of primary bile acids, glycine, serine and threonine metabolism, caffeine metabolism, arginine and proline metabolism and phenylalanine metabolism. It is worth noting that DMD has potential therapeutic effects on TNBS-induced colitis, which functions by restoring the balance of multiple disturbed pathways to a normal condition. This study suggests the reliability of metabolomics-based approaches to identifying biomarkers and pathways, which facilitate further investigation of the potential mechanisms of DMD.

Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity.

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.

Tracking control via time-varying feedback for an uncertain robotic system with both output constraint and dead-zone input.

This paper is devoted to the tracking control for an uncertain robotic system with both output constraint and dead-zone input. Remarkably, the distinctive characters of the system are reflected by system uncertainties and output constraint. First, more serious uncertainties are involved since unknown nonlinear dynamic matrices, external disturbance and the dead-zone input (see unknown slopes and break points therein) are simultaneously considered, but those of the related literature are not. Second, weaker conditions on the output constraint are allowed since the constraint functions considered are only first but not more order continuously differentiable while any their time derivatives are not necessarily available for feedback. This leads to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel control framework is proposed based on time-varying feedback which overcomes the serious system uncertainties while relaxes the conditions on output constraints. Specifically, a state transformation with a time-varying gain is first introduced to derive a new system. Then, by using the traditional backstepping method with the introduction of the time-varying gain in the estimations of some uncertain terms, a time-varying feedback controller is explicitly designed, which ensures that all the states of the resulting closed-loop system are bounded while system output asymptotically tracks the reference signal without any violation of the output constraint. Finally, simulation results for two practical examples are provided to validate the effectiveness of the proposed theoretical results, and moreover, a comparison with PID method is given to show the superiority of the proposed method on tracking accuracy and robustness.

Gestational Interrelationships among Gut-Metabolism-Transcriptome in Regulating Early Embryo Implantation and Placental Development in Mice.

Decidualization of the uterine endometrium is a critical process for embryo implantation in mammals, primarily occurring on gestational day 8 in pregnant mice. However, the interplay between the maternal gut microbiome, metabolism, and the uterus at this specific time point remains poorly understood. This study employed a multi-omics approach to investigate the metabolic, gut microbiome, and transcriptomic changes associated with early pregnancy (gestational day 8 (E8)) in mice. Serum metabolomics revealed a distinct metabolic profile at E8 compared to controls, with the differential metabolites primarily enriched in amino acid metabolism pathways. The gut microbial composition showed that E8 mice exhibited higher alpha-diversity and a significant shift in beta-diversity. Specifically, the E8 group displayed a decrease in pathogenic Proteobacteria and an increase in beneficial Bacteroidetes and S24-7 taxa. Transcriptomics identified myriads of distinct genes between the E8 and control mice. The differentially expressed genes were enriched in pathways involved in alanine, aspartate, and glutamate metabolism, PI3K-Akt signaling, and the PPAR signaling pathway. Integrative analysis of the multi-omics data uncovered potential mechanistic relationships among the differential metabolites, gut microbiota, and uterine gene expression changes. Notably, the gene Asns showed strong correlations with specific gut S24-7 and metabolite L-Aspartatic acid, suggesting its potential role in mediating the crosstalk between the maternal environment and embryo development during early pregnancy. These findings provide valuable insights into the complex interplay between the maternal metabolome, the gut microbiome, and the uterine transcriptome in the context of early pregnancy, which may contribute to our understanding of the underlying mechanisms of embryo implantation and development.

[Ecosystem Service Trade-off Synergy Strength and Spatial Pattern Optimization Based on Bayesian Network： A Case Study of the Fenhe River Basin].

Understanding the strength of trade-off and synergistic relationships among ecosystem services (ESs) is crucial for ecological management and restoration in the Fenhe River Basin. However, there is still a lack of sufficient research on the driving variables and spatial pattern optimization of the strength of ESs relationships in this area. Based on the quantitative assessment of six ESs in the Fenhe River Basin in 2000 and 2020, the ecosystem services trade-off synergy index (TSI) was introduced to quantitatively measure the strength of trade-off and synergistic relationships between each pair of ESs. A Bayesian network was constructed to identify the driving variables of trade-off and synergistic relationships, and sensitivity analysis was conducted to determine the degree of influence of key variables on the strength of these relationships. The optimization area of the strength of ESs trade-off and synergistic relationships was characterized in spatial patterns. The results showed that:① There were significant spatiotemporal differences in the six ESs in the Fenhe River Basin in 2000 and 2020. In terms of time scale, water yield, net primary productivity, crop productivity, soil conservation, and carbon storage all showed a trend of fluctuating increase. In terms of spatial scale, the spatial distribution changes in the six ESs were relatively small over the 20 years. ② The TSI of carbon storage was high in the surrounding area and low in the middle, showing a four-high and four-low pattern. The areas with the highest TSI between grain supply and other services were distributed from north to south. ③ Sensitivity analysis found that the strength of water yield, soil conservation, and habitat quality were significantly affected by precipitation, plant root depth restriction, and rainfall erosion. According to the conditional probability of different states of key variables, Wenshui County, Qingxu County, and Qi County in the central part of the Fenhe River Basin were identified as high-value areas for trade-off and synergistic relationships, which could be used as key areas for ecological restoration. These findings have important theoretical and practical significance for understanding the complex relationship between multiple ESs trade-off and synergistic relationships and their driving variables and for proposing sustainable ecological environment management policies.

Power and Sensitivity Management of Carbon Nanotube Transistor Glucose Biosensors.

Continuous glucose monitoring (CGM), which is significant for the daily management of diabetes, requires a low-power-consumption sensor system that can track low nanomolar levels of glucose in physiological fluids, such as sweat and tears. However, traditional electrochemical methods are limited to analytes in micromolar to millimolar ranges and entail high power consumption. Carbon nanotube (CNT) film field-effect transistors (FETs) are promising for constructing extremely sensitive biosensors, but their wide applications in CGM are limited by the strong screening effect of physiological fluids and the zero charge of glucose molecules. In this study, we demonstrate a glucose aptamer-modified CNT FET biosensor to realize a highly sensitive CGM system with sub-nW power consumption by applying a suitable gate voltage. A positive gate voltage can enlarge the effective Debye screening length at the double layer to reduce the local ion population nearby and then improve the sensitivity of the FET-based biosensors by 5 times. We construct CNT FET sensors for CGM with a limit of detection of 0.5 fM, a record dynamic range up to 109, and a power consumption down to ∼100 pW. The proposed field-modulated sensing performance scheme is applicable to other aptamer-based FET biosensors for detecting neutral or less charged molecules and opens opportunities to develop facilely modulated, highly sensitive, low-power, and noninvasive CGM systems.

Modification strategies for semiconductor metal oxide nanomaterials applied to chemiresistive NOx gas sensors: A review.

Semiconductor metal oxides (SMOs) nanomaterials are a category of sensing materials that are widely applied to chemiresistive NOx gas sensors. However, there is much space to improve the sensing performance of SMOs nanomaterials. Therefore, how to improve the sensing performance of SMOs nanomaterials for NOx gases has always attracted the interest of researchers. Up to now, there are few reviews focus on the modification strategies of SMOs which applied to NOx gas sensors. In order to compensate for the limitation, this review summarizes the existing modification strategies of SMOs, hoping to provide researchers a view of the research progress in this filed as comprehensive as possible. This review focuses on the progress of the modification of SMOs nanomaterials for chemiresistive NOx (NO, NO2) gas sensors, including the morphology modulation of SMOs, compositing SMOs, loading noble metals, doping metal ions, compositing with carbon nanomaterials, compositing with biomass template, and compositing with MXene, MOFs, conducting polymers. The mechanism of each strategy to enhance the NOx sensing performance of SMOs-based nanomaterials is also discussed and summarized. In addition, the limitations of some of the modification strategies and ways to address them are discussed. Finally, future perspectives for SMOs-based NOx gas sensors are also discussed.

Targeting lipid reprogramming in the tumor microenvironment by traditional Chinese medicines as a potential cancer treatment.

In the last ten years, there has been a notable rise in the study of metabolic abnormalities in cancer cells. However, compared to glucose or glutamine metabolism, less attention has been paid to the importance of lipid metabolism in tumorigenesis. Recent developments in lipidomics technologies have allowed for detailed analysis of lipid profiles within cancer cells and other cellular players present within the tumor microenvironment (TME). Traditional Chinese medicine (TCM) and its bioactive components have a long history of use in cancer treatments and are also being studied for their potential role in regulating metabolic reprogramming within TME. This review focuses on four core abnormalities altered by lipid reprogramming in cancer cells: de novo synthesis and exogenous uptake of fatty acids (FAs), upregulated fatty acid oxidation (FAO), cholesterol accumulation, which offer benefits for tumor growth and metastasis. The review also discusses how altered lipid metabolism impacts infiltrating immune cell function and phenotype as these interactions between cancer-stromal become more pronounced during tumor progression. Finally, recent literature is highlighted regarding how cancer cells can be metabolically reprogrammed by specific Chinese herbal components with potential therapeutic benefits related to lipid metabolic and signaling pathways.

Decreased resting-state functional connectivity and brain network abnormalities in the prefrontal cortex of elderly patients with Parkinson's disease accompanied by depressive symptoms.

This study aimed to explore the brain network characteristics in elderly patients with Parkinson's disease (PD) with depressive symptoms. Thirty elderly PD patients with depressive symptoms (PD-D) and 26 matched PD patients without depressive symptoms (PD-NOD) were recruited based on HAMD-24 with a cut-off of 7. The resting-state functional connectivity (RSFC) was conducted by 53-channel functional near-infrared spectroscopy (fNIRS). There were no statistically significant differences in MMSE scores, disease duration, Hoehn-Yahr stage, daily levodopa equivalent dose, and MDS-UPDRS III between the two groups. However, compared to the PD-NOD group, the PD-D group showed significantly higher MDS-UPDRS II, HAMA-14, and HAMD-24. The interhemispheric FC strength and the FC strength between the left dorsolateral prefrontal cortex (DLPFC-L) and the left frontal polar area (FPA-L) was significantly lower in the PD-D group (FDR p < 0.05). As for graph theoretic metrics, the PD-D group had significantly lower degree centrality (aDc) and node efficiency (aNe) in the DLPFC-L and the FPA-L (FDR, p < 0.05), as well as decreased global efficiency (aEg). Pearson correlation analysis indicated moderate negative correlations between HAMD-24 scores and the interhemispheric FC strength, FC between DLPFC-L and FPA-L, aEg, aDc in FPA-L, aNe in DLPFC-L and FPA-L. In conclusion, PD-D patients show decreased integration and efficiency in their brain networks. Furthermore, RSFC between DLPFC-L and FPA-L regions is negatively correlated with depressive symptoms. These findings propose that targeting DLPFC-L and FPA-L regions via non-invasive brain stimulation may be a potential intervention for alleviating depressive symptoms in elderly PD patients.

Gas-propelled nanomotors alleviate colitis through the regulation of intestinal immunoenvironment-hematopexis-microbiota circuits.

The progression of ulcerative colitis (UC) is associated with immunologic derangement, intestinal hemorrhage, and microbiota imbalance. While traditional medications mainly focus on mitigating inflammation, it remains challenging to address multiple symptoms. Here, a versatile gas-propelled nanomotor was constructed by mild fusion of post-ultrasonic CaO2 nanospheres with Cu2O nanoblocks. The resulting CaO2-Cu2O possessed a desirable diameter (291.3 nm) and a uniform size distribution. It could be efficiently internalized by colonic epithelial cells and macrophages, scavenge intracellular reactive oxygen/nitrogen species, and alleviate immune reactions by pro-polarizing macrophages to the anti-inflammatory M2 phenotype. This nanomotor was found to penetrate through the mucus barrier and accumulate in the colitis mucosa due to the driving force of the generated oxygen bubbles. Rectal administration of CaO2-Cu2O could stanch the bleeding, repair the disrupted colonic epithelial layer, and reduce the inflammatory responses through its interaction with the genes relevant to blood coagulation, anti-oxidation, wound healing, and anti-inflammation. Impressively, it restored intestinal microbiota balance by elevating the proportions of beneficial bacteria (e.g., Odoribacter and Bifidobacterium) and decreasing the abundances of harmful bacteria (e.g., Prevotellaceae and Helicobacter). Our gas-driven CaO2-Cu2O offers a promising therapeutic platform for robust treatment of UC via the rectal route.