Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics.

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

Study on Thermomechanical Properties and Morphology of an Epoxy Resin Thermally Conductive Adhesive under Different Curing Conditions.

An epoxy resin thermally conductive adhesive is a type of thermosetting polymer encapsulation material that exhibits comprehensive performance, and the thermomechanical properties of this adhesive vary significantly under different curing conditions. In this paper, spherical alumina was used as a filler for thermal conductivity to prepare an epoxy resin thermal conductivity adhesive using a multistage freezing mixing method. The effects of various curing conditions on the thermal-mechanical properties and fracture morphology of the epoxy resin thermal conductivity adhesive were studied. The results showed that the curing condition of 150 °C/2.5 h significantly improved the performance of the epoxy resin thermally conductive adhesive. Through the shear test of the composite material, the influence of the curing agent on the adhesion of the thermally conductive adhesive under fixed conditions was explored. It was found that the curing agent with a superbranched structure exhibited latent properties and greatly enhanced the toughness of the cured epoxy resin product. Altering the curing conditions increases the shear strength by up to 307%. With the increase in curing temperature and the extension of curing temperature, the glass transition temperature gradually increased from 103.9 to 159.8 °C. The initial decomposition temperature TIDT gradually increased from 295.4 to 310.1 °C, and the temperature at which the fastest decomposition rate occurs (Tmax) gradually increased from 312.48 to 330.33 °C. The thermal stability of the substance increased with both temperature and time. The curing time and curing temperature were increased, and the morphology of the fracture of the epoxy resin thermally conductive adhesive cured sample gradually showed a ductile fracture from a typical brittle fracture. The research results reveal the influence of curing conditions on the thermal conductivity and thermal stability of the epoxy resin thermally conductive adhesive, which has a specific reference value for improving the performance of the epoxy resin thermally conductive adhesive, optimizing its usage conditions, and improving production efficiency.

Using Cleavage Under Targets and Tagmentation (CUT&Tag) Assay in Mouse Myoblast Research.

This protocol paper aims to provide the new researchers with the full details of using Cleavage Under Targets and Tagmentation (CUT&Tag) to profile the genomic locations of chromatin binding factors, histone marks, and histone variants. CUT&Tag protocols function very well with mouse myoblasts and freshly isolated muscle stem cells (MuSCs). They can easily be applied to many other cell types as long as the cells can be immobilized by Concanavalin-A beads. Compared to CUT&Tag, chromatin immunoprecipitation (ChIP) assays are time-consuming experiments. ChIP assays require the pre-treatment of chromatin before the chromatic material can be used for immunoprecipitation. In cross-linking ChIP (X-ChIP), pre-treatment of chromatin involves cross-linking and sonication to fragment the chromatin. In the case of native ChIP (N-ChIP), the fragmented chromatins are normally achieved by Micrococcal nuclease (MNase) digestion. Both sonication and MNase digestion introduce some bias to the ChIP experiments. CUT&Tag assays can be finished within fewer steps and require much fewer cells compared to ChIPs but provide more unbiased information on transcription factors or histone marks at various genomic locations. CUT&Tag can function with as few as 5,000 cells. Due to its higher sensitivity and lower background signal than ChIPs, researchers can expect to obtain reliable peak data from merely several millions of reads after sequencing.

[Research progress in Astragali Radix and its active ingredients in treatment of lung cancer].

Lung cancer is the leading cause of cancer death, and its effective treatment is a difficult medical problem. Lung cancer belongs to the traditional Chinese medicine(TCM) disease categories of lung accumulation, lung amassment, and overstrain cough. Rich theoretical basis and practical experience have been accumulated in the TCM treatment of lung cancer. Astragali Radix is one of the representatives of Qi-tonifying drugs. It mainly treat the lung cancer with the syndrome of Qi deficiency and pathogen stagnation, following the principle of reinforcing healthy Qi and eliminating patgogenic Qi. Astragali Radix exerts a variety of pharmacological activities in the treatment of lung cancer, including inhibiting tumor cell proliferation and promoting tumor cell apoptosis, inhibiting tumor invasion and migration, regulating the tumor microenvironment, suppressing tumor angiogenesis, modulating autophagy, inducing macrophage polarization, enhancing immunity, inhibiting immune escape, and reversing cisplatin resistance. The active ingredients of Astragali Radix in treating lung cancer include polysaccharides, saponins, and flavonoids. This study reviewed the pharmacological activities and active ingredients of Astragali Radix in the treatment of lung cancer, providing a basis for the development and utilization of Astragali Radix resources and active ingredients and the research and development of anti-tumor drugs.

Associations among birthweight, adrenarche, brain morphometry and cognitive function in preterm children aged 9-11 years.

BACKGROUND: Preterm infants with low birthweight are at heightened risk of developmental sequelae, including neurological and cognitive dysfunction that can persist into adolescence or adulthood. In addition, preterm birth and low birthweight can provoke changes in endocrine and metabolic processes that likely impact brain health throughout development. However, few studies have examined associations among birthweight, pubertal endocrine process, long-term neurological and cognitive development. METHODS: We investigated the associations between birthweight and brain morphometry, cognitive function, and onset of adrenarche assessed 9 to 11 years later in 3571 preterm and full-term children using the Adolescent Brain Cognitive Development dataset. RESULTS: The preterm children showed lower birthweight and early adrenarche, as expected. Birthweight was positively associated with cognitive function (all︱Cohen's d︱> 0.154, P < 0.005), global brain volumes (all︱Cohen's d︱> 0.170, P < 0.008) and regional volumes in frontal, temporal, and parietal cortices in preterm and full-term children (all︱Cohen's d︱> 0.170, P < 0.0007); and cortical volume in the lateral orbitofrontal cortex (lOFC) partially mediated the effect of low birthweight on cognitive function in preterm children. In addition, adrenal score and cortical volume in the lOFC mediated the associations between birthweight and cognitive function only in preterm children. CONCLUSIONS: These findings highlight the impact of low birthweight on long-term brain structural and cognitive function development, and showed important associations with early onset of adrenarche during the puberty. This understanding may help with prevention and treatment.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

A phrase-based questionnaire-answering approach for automatic initial frailty assessment based on clinical notes.

Frailty stands out as a particularly challenging multidimensional geriatric syndrome in the elderly population, often resulting in diminished quality of life and heightened mortality risk. Negative consequences encompass a heightened likelihood of hospitalization and institutionalization, as well as suboptimal post-hospitalization outcomes and elevated mortality rates. Using a questionnaire-based approach for assessing frailty has been shown to be an effective method for early diagnosis of frailty. Nonetheless, the majority of current frailty assessment tools necessitate in-person consultations. This poses a significant challenge for elderly patients residing in rural areas, who often encounter difficulties in accessing healthcare compared to their urban or suburban counterparts. Additionally, elderly patients face an elevated risk of contracting diseases as a result of frequent hospital visits, given that many of them are immunocompromised. An automated initial frailty assessment approach can help mitigate the challenges mentioned above and conserve clinical resources by circumventing the need for extensive manual assessments. The primary aim of this paper is to introduce an automatic initial frailty assessment method. This method efficiently identifies individuals who may necessitate further frailty evaluation by automatically extracting relevant information from a patient's clinical notes and using it to complete the Tillburg Frailty Indicator (TFI) questionnaire. The introduced phrase-based query expansion technique is designed to identify the most pertinent phrases related to the frailty assessment questionnaire using Unified Medical Language System (UMLS) ontology and incorporates information from clinical notes to enhance its accuracy. Additionally, a method for retrieving pertinent clinical notes to automatically facilitate the frailty assessment process based on the identified phrases was also proposed. The proposed approaches are evaluated using a dataset containing a collection of clinical notes from elderly patients, assessing their effectiveness in terms of automating frailty assessment and question-answering tasks. This research underscores the significance of incorporating phrases as features in the automated frailty assessment process using clinical notes. The research empowers clinicians to conduct automatic frailty assessments utilizing medical data, thereby reducing the need for frequent hospital visits and in-patient consultations. This becomes particularly valuable during unusual or unexpected situations, such as the COVID-19 pandemic, where minimizing in-person interactions is crucial.

A review on the role of RNA methylation in aging-related diseases.

Senescence is the underlying mechanism of organism aging and is robustly regulated at the post-transcriptional level. This regulation involves the chemical modifications, of which the RNA methylation is the most common. Recently, a rapidly growing number of studies have demonstrated that methylation is relevant to aging and aging-associated diseases. Owing to the rapid development of detection methods, the understanding on RNA methylation has gone deeper. In this review, we summarize the current understanding on the influence of RNA modification on cellular senescence, with a focus on mRNA methylation in aging-related diseases, and discuss the emerging potential of RNA modification in diagnosis and therapy.

Preterm birth associated alterations in brain structure, cognitive functioning and behavior in children from the ABCD dataset.

BACKGROUND: Preterm birth is a global health problem and associated with increased risk of long-term developmental impairments, but findings on the adverse outcomes of prematurity have been inconsistent. METHODS: Data were obtained from the baseline session of the ongoing longitudinal Adolescent Brain and Cognitive Development (ABCD) Study. We identified 1706 preterm children and 1865 matched individuals as Control group and compared brain structure (MRI data), cognitive function and mental health symptoms. RESULTS: Results showed that preterm children had higher psychopathological risk and lower cognitive function scores compared to controls. Structural MRI analysis indicated that preterm children had higher cortical thickness in the medial orbitofrontal cortex, parahippocampal gyrus, temporal and occipital gyrus; smaller volumes in the temporal and parietal gyrus, cerebellum, insula and thalamus; and smaller fiber tract volumes in the fornix and parahippocampal-cingulum bundle. Partial correlation analyses showed that gestational age and birth weight were associated with ADHD symptoms, picvocab, flanker, reading, fluid cognition composite, crystallized cognition composite and total cognition composite scores, and measures of brain structure in regions involved with emotional regulation, attention and cognition. CONCLUSIONS: These findings suggest a complex interplay between psychopathological risk and cognitive deficits in preterm children that is associated with changes in regional brain volumes, cortical thickness, and structural connectivity among cortical and limbic brain regions critical for cognition and emotional well-being.

Incidence and risk factors for hypotension after carotid artery stenting: Systematic review and meta-analysis.

OBJECTIVE: Hypotension is recognized as a common complication after carotid artery stenting, but its incidence and the risk factors associated with it are uncertain. Therefore, we performed a systematic review and meta-analysis to investigate and identify risk factors for hypotension after surgery. METHODS: We retrieved risk factors from eight databases for case-control and cross-sectional studies of hypotension after carotid artery stenting according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines on 28 November 2022. Data were analyzed by using R4.2.1 and Review Manager 5.3. RESULTS: A total of 2843 samples were searched, and 17 publications were included in the analysis. The meta-analysis results showed that the incidence of hypotension after surgery was 28.6% (95% confidence interval [CI] (0.225, 0.347)). Age ⩾ 65 years (odds ratio [OR] = 4.55, 95% CI (2.50, 8.29), P < 0.00001), stenosis site (bulb) (OR = 4.41, 95% CI (2.50, 7.79), P < 0.00001), severe stenosis (OR = 3.56, 95% CI (1.62, 7.85), P = 0.002), stenosis proximity (⩽ 10 mm) to bifurcation (OR = 2.69, 95% CI (1.74, 4.15), P < 0.00001), calcified plaques (OR = 4.64, 95% CI (1.93, 11.14), P = 0.0006), post-balloon dilation (OR = 5.95, 95% CI (2.31, 15.31), P = 0.0002), bilateral carotid stenting (OR = 30.51, 95% CI (2.33, 399.89), P = 0.009), and intravenous fluid intake/mL on the first postoperative day (mean difference = 444.99, 95% CI (141.40, 748.59), P = 0.004) were risk factors for hypotension after surgery. CONCLUSIONS: A high incidence of hypotension was observed after carotid artery stenting. Age, stenosis site, severe stenosis, stenosis proximity to bifurcation, calcified plaques, post-balloon dilation, type of surgery, and intravenous fluid intake were identified as risk factors.

Network pharmacology and experimental verification reveal the mechanism of Hedysari Radix and Curcumae Rhizoma with the optimal compatibility ratio against colitis-associated colorectal cancer.

ETHNOPHARMACOLOGICAL RELEVANCE: The herb pair Astragali Radix (AR) and Curcumae Rhizoma (vinegar-processed, VPCR), derived from the traditional Chinese medicine (TCM) text 'Yixuezhongzhongcanxilu', have long been used to treat gastrointestinal diseases, notably colitis-associated colorectal cancer (CAC). Hedysari Radix (HR), belonging to the same Leguminosae family as AR but from a different genus, is traditionally used as a substitute for AR when paired with VPCR in the treatment of CAC. However, the optimal compatibility ratio for HR-VPCR against CAC and the underlying mechanisms remain unclear. AIM OF THE STUDY: To investigate the optimal compatibility ratio and underlying mechanisms of HR-VPCR against CAC using a combination of comparative pharmacodynamics, network pharmacology, and experimental verification. MATERIALS AND METHODS: The efficacy of different compatibility ratios of HR-VPCR against CAC was evaluated using various indicators, including the body weight, colon length, tumor count, survival rate, disease activity index (DAI) score, Haemotoxylin and Eosin (H&E) pathological sections, inflammation cytokines (IL-1ß, IL-6, IL-10, TNF-α), tumor markers (K-Ras, p53), and intestinal permeability proteins (claudin-1, E-cadherin, mucin-2). Then, the optimal compatibility ratio of HR-VPCR against CAC was determined based on the fuzzy matter-element analysis by integrating the above indicators. After high-performance liquid chromatography (HPLC) analysis for the optimal compatibility ratio of HR-VPCR, potential active components of HR-VPCR were identified by TCMSP and the previous bibliographies. Swiss Targets and GeneCards were adopted to predict the targets of the active components and the targets of CAC, respectively. Then, the common targets of HR-VPCR against CAC were obtained by Venn analysis. PPI networks were constructed in STRING. GO and KEGG enrichments were visualized by the David database. Finally, the predicted pathway was experimentally validated via Western blot. RESULTS: Various compatibility ratios of HR-VPCR demonstrated notable therapeutic effects to some extent, evidenced by improvements in body weight, colon length, tumor count, pathological symptoms (DAI score), colon and organ indexes, survival rate, and modulation of inflammation factors (IL-1ß, IL-6, IL-10, TNF-α), as well as tumor markers (K-Ras, p53), and down-regulation of intestinal permeability proteins (claudin-1, E-cadherin, mucin-2) in CAC mice. Among these ratios, the ratio 4:1 represents the optimal compatibility ratio by the fuzzy matter-element analysis. Thirty active components of HR-VPCR were carefully selected, targeting 553 specific genes. Simultaneously, 2022 targets associated with CAC were identified. 88 common targets were identified after generating a Venn plot. Following PPI network analysis, 29 core targets were established, with AKT1 ranking highest among them. Further analysis via GO and KEGG enrichment identified the PI3K-AKT signaling pathway as a potential mechanism. Experimental validation confirmed that HR-VPCR intervention effectively reversed the activated PI3K-AKT signaling pathway. CONCLUSIONS: The optimal compatibility ratio for the HR-VPCR herb pair in alleviating CAC is 4:1. HR-VPCR exerts its effects by alleviating intestinal inflammation, improving intestinal permeability, and regulating the PI3K-AKT signaling pathway.

Meiosis in an asymmetric dikaryotic genome of Tremella fuciformis Tr01 facilitates new chromosome formation.

BACKGROUND: The dikaryotic stage dominates most of the life cycle in basidiomycetes, and each cell carries two different haploid nuclei. Accurate phasing of these two nuclear genomes and their interactions have long been of interest. RESULTS: We combine PacBio HiFi reads, Nanopore ultra-long reads, and Hi-C data to generate a complete, high-quality asymmetric dikaryotic genome of Tremella fuciformis Tr01, including Haplotypes A and B genomes. We assemble a meiotic haploid DBZ04 genome and detect three recombination events in these two haplotypes. We identify several chromosomal rearrangements that lead to differences in chromosome number, length, content, and sequence arrangement between these two haplotypes. Each nucleus contains a two-speed genome, harboring three accessory chromosomes and two accessory compartments that affect horizontal chromatin transfer between nuclei. We find few basidiospores are ejected from fruiting bodies of Tr01. Most monospore isolates sequenced belong to Tr01-Haplotype A genome architecture. More than one-third of monospore isolates carry one or two extra chromosomes including Chr12B and two new chromosomes ChrN1 and ChrN2. We hypothesize that homologous regions of seven sister chromatids pair into a large complex during meiosis, followed by inter-chromosomal recombination at physical contact sites and formation of new chromosomes. CONCLUSION: We assemble two haplotype genomes of T. fuciformis Tr01 and provide the first overview of basidiomycetous genomes with discrete genomic architecture. Meiotic activities of asymmetric dikaryotic genomes result in formation of new chromosomes, aneuploidy of some daughter cells, and inviability of most other daughter cells. We propose a new approach for breeding of sporeless mushroom.

Solution-grown millimeter-scale Mn-doped CsPbBr3/Cs4PbBr6 crystals with enhanced photoluminescence and stability for light-emitting applications.

Metal halide perovskites are particularly emerging for optoelectronic applications in light-emitting diodes, photodetectors, and solar cells due to their flourishing photophysical properties. However, the poor stability of three-dimensional (3D) lead halide perovskite nanocrystals (NCs) significantly hampers their optoelectronics and photovoltaics applications. Embedding 3D perovskites into zero-dimensional (0D) perovskite crystals and doping ions of appropriate elements into host lattices provide effective approaches to improve the stability and optical-electronic performance. In this study, millimeter-scale Mn-doped and undoped CsPbBr3/Cs4PbBr6 perovskite crystals were successfully fabricated by a one-step slow cooling method. We systematically investigated the effects of Mn2+ ion doping on the PL performance and stability of CsPbBr3/Cs4PbBr6 crystals. Compared with undoped crystals, the existence of Mn2+ ions not only blue-shifted the PL peak but also improved the luminescence performance and stability of the prepared millimeter-sized crystals. Moreover, doping Mn2+ ions can increase the proportion of radiative recombination at low temperature, which may be because Mn2+ ions can effectively accelerate the decay of a dark exciton by the magnetic mixing of bright and dark excitons. In addition, green LED devices with high efficiency packaged as-grown crystals are explored, which promises further application in display backlights.

Effects of different light intensity on the growth of tomato seedlings in a plant factory.

Light-emitting diodes (LEDs) were the best artificial light source for plant factories. Red light-emitting diodes (LEDs, R) and blue light-emitting diodes (LEDs, B) were used to obtain different light intensities of uniform spectra, and the greenhouse environment was considered as a comparison. The results showed that root dry weight, shoot dry weight and stem diameter were superior in plant growth under 240 µmolm-2s-1, additionally, the Dixon Quality Index (DQI) was also best. Under 240 µmolm-2s-1, the net photosynthesis rate (Pn) was consistent with the greenhouse's treatment, superior to other experimental groups. The results implied that the PPFD was more suitable for the cultivation of tomato seedlings under the condition of 240 µmolm-2s-1, and can replace the greenhouse conditions so as to save energy and reduce emissions.

Effect of the alumina micro-particle sizes on the thermal conductivity and dynamic mechanical property of epoxy resin.

Epoxy thermal conductive adhesives with high thermal conductivity and dynamic mechanical properties are important thermally conductive materials for fabricating highly integrated electronic devices. In this paper, micro-Al2O3 is used as a thermally conductive filler for the epoxy resin composite and investigated the effect of micron-sized alumina particle size on the thermal conductivity and dynamic mechanical property of epoxy resin by the transient planar hot plate method and DMA (Dynamic mechanical analysis). The experimental results show that with the same amount of alumina filling, the thermal conductivity and Tg (glass transition temperature) of epoxy/Al2O3 composite material decrease with the increase of alumina particle size. The maximum thermal conductivity of the composite material is 0.679 (W/mK), while the energy storage modulus of epoxy/Al2O3 composite material increases with the increase of alumina particle size, and the maximum energy storage modulus of the composite material is 160MPa. Compared with pure epoxy resin, the thermal conductivity and energy storage modulus have increased by 2.7 and 3.2 times, respectively. The epoxy/Al2O3 composite was applied to the COB (Chips On Board) type LED package, and the substrate temperature of the LED dropped to the lowest after 1.5 hours of operation using EP-A5 composite, and the temperature was stabilized at 38.2°C, indicating that the addition of 5-micron alumina composite has the best heat dissipation in the COB type LED package. These results are critical for the implementation of particulate-filled polymer composites in practical applications because relaxed material specifications and handling procedures can be incorporated in production environments to improve efficiency.

A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance.

Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.

Identifications of metabolic differences between Hedysari Radix Praeparata Cum Melle and Astragali Radix Praeparata Cum Melle for spleen-qi deficiency rats: A comparative study.

Hedysari Radix Praeparata Cum Melle (HRPCM) and Astragali Radix Praeparata Cum Melle (ARPCM) are capable of improving spleen-qi deficiency (SQD) syndrome especially in the gastrointestinal dysfunction and decreased immunity in traditional Chinese medicine clinically. This study aims to compare and reveal the metabolic differences between HRPCM and ARPCM for SQD rats. Firstly, HRPCM (12.6 g/kg) and ARPCM (12.6 g/kg) were used to intervene SQD rats to further evaluate the effect. The results showed that HRPCM and ARPCM were able to improve the spleen pathology, increase the body weight, the rectal temperature, the spleen index, the thymus index, the levels of GAS and D-xylose in serum, and decrease the levels of IL-2, IL-6 and TNF-α in serum for SQD rats. Then, the studies of metabolic differences in serum and spleen were carried out using UPLC-Q-TOF-MS. The findings emphasized that HRPCM and ARPCM not only regulated metabolic profiling of serum and spleen in SQD rats, but also existed differences. HRPCM and ARPCM regulated metabolic pathways mainly including lipid metabolism, energy metabolism, amino acid metabolism, nucleotide metabolism, sugar metabolism and other types of metabolism for SQD rats. However, the metabolite profiles in SQD rats changed significantly, mainly involving abnormal glycine synthesis occurred in SQD rats. The expression trends of metabolites in HRPCM and ARPCM intervention for SQD rats were partly the same. Interestingly, there are similarities and differences in metabolic profiling between HRPCM and ARPCM for SQD rats. The differences were mainly in the synthesis of L-glutamine in amino acid metabolism.

Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability.

Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries.

Geriatric nutritional risk index was associated with in-hospital mortality among cardiac intensive care unit patients.

Background: Identifying risk factors associated with cardiac intensive care unit (CICU) patients' prognosis can help clinicians intervene earlier and thus improve their prognosis. The correlation between the geriatric nutrition risk index (GNRI), which reflects nutritional status, and in-hospital mortality among CICU patients has yet to be established. Method: The present study retrospectively enrolled 4,698 CICU patients. Based on the nutritional status, the participants were categorized into four groups. The primary endpoint was in-hospital mortality. The length of hospital stay and length of CICU stay were the secondary endpoints. To explore the correlation between nutritional status and in-hospital mortality, a logistic regression analysis was conducted. The nonlinear associations of GNRI with in-hospital mortality were evaluated using restricted cubic spline (RCS). Furthermore, subgroup analyses were conducted to evaluate the effect of the GNRI on in-hospital mortality across different subgroups, with calculation of the p for interaction. Result: A higher risk of malnutrition was significantly linked to an increased incidence of in-hospital mortality (High risk vs. No risk: 26.2% vs. 4.6%, p < 0.001), as well as a longer length of hospital stay (High risk vs. No risk: 15.7, 9.1-25.1 vs. 8.9, 6.9-12.9, p < 0.001) and CICU stay (High risk vs. No risk: 6.4, 3.8-11.9 vs. 3.2, 2.3-5.1, p < 0.001). An elevated GNRI was significantly associated with an increased risk of in-hospital mortality even after controlling for pertinent confounding factors (High risk vs. No risk: OR, 95% CI: 2.37, 1.67-3.37, p < 0.001, p for trend <0.001). Additionally, the RCS model showed a linear relationship between GNRI and in-hospital mortality, with the risk of in-hospital mortality significantly decreasing as GNRI increased (non-linear p = 0.596). Furthermore, in the subgroups of hypertension, ventricular arrhythmias, cardiac arrest, shock, and chronic kidney disease, there was a significant interaction between nutritional status and in-hospital mortality. Conclusion: Among CICU patients, a low GNRI was a significant predictor of in-hospital mortality. Furthermore, patients with a higher risk of malnutrition, as indicated by low GNRI values, experienced significantly longer hospital and CICU stays.