Schwann cell-secreted PGE2 promotes sensory neuron excitability during development.

Electrical excitability-the ability to fire and propagate action potentials-is a signature feature of neurons. How neurons become excitable during development and whether excitability is an intrinsic property of neurons remain unclear. Here, we demonstrate that Schwann cells, the most abundant glia in the peripheral nervous system, promote somatosensory neuron excitability during development. We find that Schwann cells secrete prostaglandin E2, which is necessary and sufficient to induce developing somatosensory neurons to express normal levels of genes required for neuronal function, including voltage-gated sodium channels, and to fire action potential trains. Inactivating this signaling pathway in Schwann cells impairs somatosensory neuron maturation, causing multimodal sensory defects that persist into adulthood. Collectively, our studies uncover a neurodevelopmental role for prostaglandin E2 distinct from its established role in inflammation, revealing a cell non-autonomous mechanism by which glia regulate neuronal excitability to enable the development of normal sensory functions.

Omentin-1 inhibits the development of benign prostatic hyperplasia by attenuating local inflammation.

BACKGROUND: Benign prostatic hyperplasia (BPH) is a prevalent disease affecting elderly men, with chronic inflammation being a critical factor in its development. Omentin-1, also known as intelectin-1 (ITLN-1), is an anti-inflammatory protein primarily found in the epithelial cells of the small intestine. This study aimed to investigate the potential of ITLN-1 in mitigating BPH by modulating local inflammation in the prostate gland. METHODS: Our investigation involved two in vivo experimental models. Firstly, ITLN-1 knockout mice (Itln-1-/-) were used to study the absence of ITLN-1 in BPH development. Secondly, a testosterone propionate (TP)-induced BPH mouse model was treated with an ITLN-1 overexpressing adenovirus. We assessed BPH severity using prostate weight index and histological analysis, including H&E staining, immunohistochemistry, and enzyme-linked immunosorbent assay. In vitro, the impact of ITLN-1 on BPH-1 cell proliferation and inflammatory response was evaluated using cell proliferation assays and enzyme-linked immunosorbent assay. RESULTS: In vivo, Itln-1-/- mice exhibited elevated prostate weight index, enlarged lumen area, and higher TNF-α levels compared to wild-type littermates. In contrast, ITLN-1 overexpression in TP-induced BPH mice resulted in reduced prostate weight index, lumen area, and TNF-α levels. In vitro studies indicated that ITLN-1 suppressed the proliferation of prostate epithelial cells and reduced TNF-α production in macrophages, suggesting a mechanism involving the inhibition of macrophage-mediated inflammation. CONCLUSION: The study demonstrates that ITLN-1 plays a significant role in inhibiting the development of BPH by reducing local inflammation in the prostate gland. These findings highlight the potential of ITLN-1 as a therapeutic target in the management of BPH.

Androgen action in cell fate and communication during prostate development at single-cell resolution.

Androgens/androgen receptor (AR)-mediated signaling pathways are essential for prostate development, morphogenesis and regeneration. Specifically, stromal AR signaling has been shown to be essential for prostatic initiation. However, the molecular mechanisms underlying AR-initiated mesenchymal-epithelial interactions in prostate development remain unclear. Here, using a newly generated mouse model, we have directly addressed the fate and role of genetically marked AR-expressing cells during embryonic prostate development. Androgen signaling-initiated signaling pathways were identified in mesenchymal niche populations at single-cell transcriptomic resolution. The dynamic cell-signaling networks regulated by stromal AR were additionally characterized in relation to prostatic epithelial bud formation. Pseudotime analyses further revealed the differentiation trajectory and fate of AR-expressing cells in both prostatic mesenchymal and epithelial cell populations. Specifically, the cellular properties of Zeb1-expressing progenitors were assessed. Selective deletion of AR signaling in a subpopulation of mesenchymal rather than epithelial cells dysregulated the expression of the master regulators and significantly impaired prostatic bud formation. These data provide novel, high-resolution evidence demonstrating the important role of mesenchymal androgen signaling in the cellular niche controlling prostate early development by initiating dynamic mesenchyme-epithelia cell interactions.

Demonstration of 651†nm InGaN-based red light-emitting diode with an external quantum efficiency over 6% by InGaN/AlN strain release interlayer.

This work reports a high-performance InGaN-based red-emitting LED with a strain-release interlayer (SRI) consisting of an InGaN stress-release layer (SRL) and an AlN dislocation confinement layer (DCL) in unintentionally doped GaN (u-GaN). The SRL introduces a tensile strain which could decrease the in-plane compressive stress of the u-GaN layer, while the DCL could reduce the dislocation density and thus improve the crystal quality of the u-GaN layer. Consequently, a high-efficiency InGaN-based red-emitting LED with a peak wavelength of 651 nm and an external quantum efficiency of 6.04% is realized. In addition, the room-temperature photoluminescence (PL) mapping emission wavelength is uniform across a 4-inch wafer with a standard deviation of 3.3 nm. Therefore, the proposed SRI offers good potential for mass-producing high-performance and long-wavelength InGaN-based red-emitting LEDs.

Quantum Light Generation Based on GaN Microring toward Fully On-Chip Source.

An integrated quantum light source is increasingly desirable in large-scale quantum information processing. Despite recent remarkable advances, a new material platform is constantly being explored for the fully on-chip integration of quantum light generation, active and passive manipulation, and detection. Here, for the first time, we demonstrate a gallium nitride (GaN) microring based quantum light generation in the telecom C-band, which has potential toward the monolithic integration of quantum light source. In our demonstration, the GaN microring has a free spectral range of 330 GHz and a near-zero anomalous dispersion region of over 100 nm. The generation of energy-time entangled photon pair is demonstrated with a typical raw two-photon interference visibility of 95.5±6.5%, which is further configured to generate a heralded single photon with a typical heralded second-order autocorrelation g_{H}^{(2)}(0) of 0.045±0.001. Our results pave the way for developing a chip-scale quantum photonic circuit.

Flux balance analysis-based metabolic modeling of microbial secondary metabolism: Current status and outlook.

In microorganisms, different from primary metabolism for cellular growth, secondary metabolism is for ecological interactions and stress responses and an important source of natural products widely used in various areas such as pharmaceutics and food additives. With advancements of sequencing technologies and bioinformatics tools, a large number of biosynthetic gene clusters of secondary metabolites have been discovered from microbial genomes. However, due to challenges from the difficulty of genome-scale pathway reconstruction and the limitation of conventional flux balance analysis (FBA) on secondary metabolism, the quantitative modeling of secondary metabolism is poorly established, in contrast to that of primary metabolism. This review first discusses current efforts on the reconstruction of secondary metabolic pathways in genome-scale metabolic models (GSMMs), as well as related FBA-based modeling techniques. Additionally, potential extensions of FBA are suggested to improve the prediction accuracy of secondary metabolite production. As this review posits, biosynthetic pathway reconstruction for various secondary metabolites will become automated and a modeling framework capturing secondary metabolism onset will enhance the predictive power. Expectedly, an improved FBA-based modeling workflow will facilitate quantitative study of secondary metabolism and in silico design of engineering strategies for natural product production.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma.

OBJECTIVES: It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC. METHODS: A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model. RESULTS: This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000). CONCLUSION: The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC. CLINICAL RELEVANCE STATEMENT: This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making. KEY POINTS: • It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2.

We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.

Amorphous/polycrystalline NiMn selenide for high-performance supercapacitors.

Transition-metal selenides have been extensively studied as promising electrode materials for supercapacitors. Engineering amorphous/crystalline heterostructures is an effective strategy to improve rich active sites for accelerating redox reaction kinetics but still lacks exploration. In this study, an amorphous/crystalline heterostructure was designed and constructed by selenizing the self-sacrificial template NiMnS to generate amorphous Mn/polycrystalline Ni0.85Se-NiSe2 heterophase via the phase transformation from metal sulfide into metal selenide. The synergy of the complementary multi-components and amorphous/polycrystalline heterophase could enrich electron/ion-transport channels and expose abundant active sites, which accelerated electron/ion transfer and Faradaic reaction kinetics during charging/discharging. As expected, the optimal NiMnSe exhibited a high specific charge (1389.1 C g-1 at 1 A g-1), a good rate capability, and an excellent lifespan (88.9% retention). Moreover, the fabricated NiMnSe//activated carbon device achieved a long cycle life and energy density of 48.0 W h kg-1 at 800 W kg-1, shedding light on the potential for use in practical applications, such as electrochemical energy-storage devices.

Specific plasma microRNA profiles could be potential non-invasive biomarkers for biochemical pregnancy loss following embryo transfer.

BACKGROUND: Plasma microRNAs act as biomarkers for predicting and diagnosing diseases. Reliable non-invasive biomarkers for biochemical pregnancy loss have not been established. We aim to analyze the dynamic microRNA profiles during the peri-implantation period and investigate if plasma microRNAs could be non-invasive biomarkers predicting BPL. METHODS: In this study, we collected plasma samples from patients undergoing embryo transfer (ET) on ET day (ET0), 11 days after ET (ET11), and 14 days after ET (ET14). Patients were divided into the NP (negative pregnancy), BPL (biochemical pregnancy loss), and CP (clinical pregnancy) groups according to serum hCG levels at day11~14 and ultrasound at day28~35 following ET. MicroRNA profiles at different time-points were detected by miRNA-sequencing. We analyzed plasma microRNA signatures for BPL at the peri-implantation stage, we characterized the dynamic microRNA changes during the implantation period, constructed a microRNA co-expression network, and established predictive models for BPL. Finally, the sequencing results were confirmed by Taqman RT-qPCR. RESULTS: BPL patients have distinct plasma microRNA profiles compared to CP patients at multiple time-points during the peri-implantation period. Machine learning models revealed that plasma microRNAs could predict BPL. RT-qPCR confirmed that miR-181a-2-3p, miR-9-5p, miR-150-3p, miR-150-5p, and miR-98-5p, miR-363-3p were significantly differentially expressed between patients with different reproductive outcomes. CONCLUSION: Our study highlights the non-invasive value of plasma microRNAs in predicting BPL.

NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma.

BACKGROUND: Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD. METHODS: NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis. RESULTS: The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin. CONCLUSION: NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

Mental health and jaw function of patients with anterior disc displacement with reduction.

BACKGROUND: The mental health of patients with temporomandibular disorder or other jaw dysfunction is a primary concern in clinical practice, but the extent of these symptoms in this patient subset is not yet well understood. OBJECTIVES: This cross-sectional study aimed to compare the mental health and jaw function between patients with anterior disc displacement with reduction (ADDWR) and healthy individuals. METHODS: In total, 170 patients with ADDWR and 163 healthy participants enrolled in this study from March 2020 to December 2021. All participants completed a single assessment, including a pain rating and several questionnaires to assess jaw dysfunction, depression, and anxiety. All scores and the grade distribution of somatization, depression and anxiety were analysed between groups. RESULTS: Significant differences were found in measures of pain, jaw function and somatization; the ADDWR group had significantly higher pain and functional jaw limitations than the healthy group. The grade distribution of somatic symptoms also differed between groups: the distribution of patients who reported mild and above scores in the ADDWR group was significantly higher than that of the healthy group. Depression and anxiety scores or grade distributions were not significantly different by group. CONCLUSION: The jaw function of patients seeking treatment for ADDWR was lower than that of non-TMD individuals. They did not show high anxiety and depression symptoms, but their somatic symptoms were more apparent.

Analysis of risk factors and interactions for pain in temporomandibular disorder: A cross-sectional study.

BACKGROUND: Risk factors for temporomandibular disorder (TMD) pain remain unclear. OBJECTIVES: This study aimed to identify risk factors for TMD pain using a biopsychosocial model and to investigate interactions between potential risk factors-oral behaviours (OBs), psychological factors and sleep quality-and their direct and indirect effects on TMD pain. METHODS: This was a cross-sectional study of 488 patients with TMDs (422 women; 30.8 ± 9.4 years). Pain was assessed using the Numerical Rating Scale. Demographic, behavioural, psychological and biomedical data were collected through clinical examination, face-to-face interviews and questionnaires. Multiple linear regression analysis was used to identify factors associated with TMD pain. Mediation and moderation analysis were used to evaluate interactions between variables. Significant mediation ('0' not included in the 95% confidence interval (CI)) and moderation (p < .05) effects on TMD pain were identified. RESULTS: Marital status, diagnosis subgroup, previous medication use, depression and sleep quality were significant risk factors for TMD pain (p < .05). Significant mediation effects were observed as follows: depression and sleep quality mediated the association between OBs and pain; sleep quality mediated the association between somatization, depression, anxiety and pain; and depression mediated the association between sleep quality and pain (all 95% CI did not contain '0'). CONCLUSIONS: (1) Marital status, diagnosis subgroup, previous medication use, depression and sleep quality were associated with TMD pain. (2) OBs can exacerbate pain by promoting depression and reducing sleep quality. Psychological factors and sleep quality can interact to exacerbate pain.

Artificial intelligence-based model for lymph node metastases detection on whole slide images in bladder cancer: a retrospective, multicentre, diagnostic study.

BACKGROUND: Accurate lymph node staging is important for the diagnosis and treatment of patients with bladder cancer. We aimed to develop a lymph node metastases diagnostic model (LNMDM) on whole slide images and to assess the clinical effect of an artificial intelligence-assisted (AI) workflow. METHODS: In this retrospective, multicentre, diagnostic study in China, we included consecutive patients with bladder cancer who had radical cystectomy and pelvic lymph node dissection, and from whom whole slide images of lymph node sections were available, for model development. We excluded patients with non-bladder cancer and concurrent surgery, or low-quality images. Patients from two hospitals (Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China) were assigned before a cutoff date to a training set and after the date to internal validation sets for each hospital. Patients from three other hospitals (the Third Affiliated Hospital of Sun Yat-sen University, Nanfang Hospital of Southern Medical University, and the Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong, China) were included as external validation sets. A validation subset of challenging cases from the five validation sets was used to compare performance between the LNMDM and pathologists, and two other datasets (breast cancer from the CAMELYON16 dataset and prostate cancer from the Sun Yat-sen Memorial Hospital of Sun Yat-sen University) were collected for a multi-cancer test. The primary endpoint was diagnostic sensitivity in the four prespecified groups (ie, the five validation sets, a single-lymph-node test set, the multi-cancer test set, and the subset for a performance comparison between the LNMDM and pathologists). FINDINGS: Between Jan 1, 2013 and Dec 31, 2021, 1012 patients with bladder cancer had radical cystectomy and pelvic lymph node dissection and were included (8177 images and 20 954 lymph nodes). We excluded 14 patients (165 images) with concurrent non-bladder cancer and also excluded 21 low-quality images. We included 998 patients and 7991 images (881 [88%] men; 117 [12%] women; median age 64 years [IQR 56-72]; ethnicity data not available; 268 [27%] with lymph node metastases) to develop the LNMDM. The area under the curve (AUC) for accurate diagnosis of the LNMDM ranged from 0·978 (95% CI 0·960-0·996) to 0·998 (0·996-1·000) in the five validation sets. Performance comparisons between the LNMDM and pathologists showed that the diagnostic sensitivity of the model (0·983 [95% CI 0·941-0·998]) substantially exceeded that of both junior pathologists (0·906 [0·871-0·934]) and senior pathologists (0·947 [0·919-0·968]), and that AI assistance improved sensitivity for both junior (from 0·906 without AI to 0·953 with AI) and senior (from 0·947 to 0·986) pathologists. In the multi-cancer test, the LNMDM maintained an AUC of 0·943 (95% CI 0·918-0·969) in breast cancer images and 0·922 (0·884-0·960) in prostate cancer images. In 13 patients, the LNMDM detected tumour micrometastases that had been missed by pathologists who had previously classified these patients' results as negative. Receiver operating characteristic curves showed that the LNMDM would enable pathologists to exclude 80-92% of negative slides while maintaining 100% sensitivity in clinical application. INTERPRETATION: We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work. FUNDING: National Natural Science Foundation of China, the Science and Technology Planning Project of Guangdong Province, the National Key Research and Development Programme of China, and the Guangdong Provincial Clinical Research Centre for Urological Diseases.

Statistical genetics in and out of quasi-linkage equilibrium.

This review is about statistical genetics, an interdisciplinary topic between statistical physics and population biology. The focus is on the phase ofquasi-linkage equilibrium(QLE). Our goals here are to clarify under which conditions the QLE phase can be expected to hold in population biology and how the stability of the QLE phase is lost. The QLE state, which has many similarities to a thermal equilibrium state in statistical mechanics, was discovered by M Kimura for a two-locus two-allele model, and was extended and generalized to the global genome scale byNeher&Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman theory describes a population evolving due to the mutations, recombination, natural selection and possibly genetic drift. A QLE phase exists at sufficiently high recombination rate (r) and/or mutation ratesµwith respect to selection strength. We show how in QLE it is possible to infer the epistatic parameters of the fitness function from the knowledge of the (dynamical) distribution of genotypes in a population. We further consider the breakdown of the QLE regime for high enough selection strength. We review recent results for the selection-mutation and selection-recombination dynamics. Finally, we identify and characterize a new phase which we call the non-random coexistence where variability persists in the population without either fixating or disappearing.

Unbiased multitissue transcriptomic analysis reveals complex neuroendocrine regulatory networks mediated by spinal cord injury-induced immunodeficiency.

BACKGROUND: Spinal cord injury (SCI), which causes loss of sensory and motor function in the body below the level of injury, is a devastating disease of the central nervous system. SCI leads to severe secondary immunosuppression, called SCI-induced immunodeficiency syndrome (SCI-IDS), which is characterized by increased susceptibility to infection and further exacerbates neurological dysfunction. Several studies have suggested that SCI-IDS is an independent risk factor for poor neurological prognosis. SCI-IDS predominantly occurs following injury above the T5 levels and eventually leads to systemic immune failure, possibly via the sympathetic-adrenal medullary axis and the hypothalamic‒pituitary‒adrenal (HPA) axis. However, the mechanism remains unclear. METHODS AND OBJECTIVES: The concentrations of adrenocorticotropic hormone and cortisol in plasma, as well as changes in sympathetic activity (blood pressure and catecholamine levels in plasma), were assessed in rats in the high-level (T3) spinal cord injury (T3-SCI) group and the low-level (T10) spinal cord injury (T10-SCI) group. Second, the differential regulation of the gene network between the sympathetic-adrenal medullary axis and the HPA axis was explored by histology and multitissue transcriptomics, and the neuroendocrine-immune network associated with SCI-IDS was further elucidated. RESULTS: The spleen and thymus gland, which are secondary immune organs, were significantly atrophied in rats in the T3-SCI group, and the white pulp of the spleen was significantly atrophied. The level of cortisol, which is mediated by the adrenal glands, was markedly elevated, but norepinephrine levels were markedly decreased. There was no difference in adrenocorticotropic hormone expression between any of the groups. The transcriptome analysis results showed that the downregulated differentially expressed genes (DEGs) in the T3-SCI group were enriched in the GO term immunoregulation, indicating that splenic immune function was markedly impaired after high-level SCI. The upregulated DEGs in the hypothalamus (hub genes: Nod2, Serpine1, Cebpb, Nfkbil1, Ripk2, Zfp36, Traf6, Akap8, Gfer, Cxcl10, Tnfaip3, Icam1, Fcgr2b, Ager, Dusp10, and Mapkapk2) were significantly enriched in inflammatory pathways, and the downregulated genes (hub genes: Grm4, Nmu, P2ry12, rt1-bb1, Oprm1, Zfhx2, Gpr83, and Chrm2) were enriched in pathways related to inhibitory Gi-mediated G protein-coupled receptor (Gi-GPCR) neurons and neuropeptide changes. The upregulated genes in the adrenal glands (hub genes: Ciart, per2, per3, cry1, and cry2) were enriched in cortisol secretion and circadian rhythm changes, and the downregulated genes (hub genes: IL7r, rt1-bb, rt1-bb1, rt1-da, rt1-ba, cd74, cxcr3, vcam1, ccl5, bin1, and IL8) were significantly enriched in MHC-mediated immune responses. CONCLUSIONS: To explore the possible mechanism underlying SCI-IDS, this study assessed the differential regulation of the gene network associated with neuroendocrine immunity after SCI. Progressive neuroinflammation spreads after injury, and neurotransmission through Gi-mediated G protein-coupled receptors in the HPA axis and neuropeptide production by the hypothalamus are inhibited. Disruption of the connection between the hypothalamus and the adrenal glands causes autonomous regulation of the adrenal glands, disturbance of circadian rhythm and finally hypercortisolemia, leading to general suppression of peripheral adaptive immunity. Neuraxial nerve inflammation caused by SCI persists indefinitely, blocking nerve repair; persistent system-wide immunosuppression in the periphery results in increased susceptibility to infection, leading to poor neurological prognosis.

Wnt/ß-catenin signalling pathway in breast cancer cells and its effect on reversing tumour drug resistance by alkaloids extracted from traditional Chinese medicine.

Breast cancer is a high-risk disease with a high mortality rate among women. Chemotherapy plays an important role in the treatment of breast cancer. However, chemotherapy eventually results in tumours that are resistant to drugs. In recent years, many studies have revealed that the activation of Wnt/ß-catenin signalling is crucial for the emergence and growth of breast tumours as well as the development of drug resistance. Additionally, drugs that target this pathway can reverse drug resistance in breast cancer therapy. Traditional Chinese medicine has the properties of multi-target and tenderness. Therefore, integrating traditional Chinese medicine and modern medicine into chemotherapy provides a new strategy for reversing the drug resistance of breast tumours. This paper mainly reviews the possible mechanism of Wnt/ß-catenin in promoting the process of breast tumour drug resistance, and the progress of alkaloids extracted from traditional Chinese medicine in the targeting of this pathway in order to reverse the drug resistance of breast cancer.

An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.

BACKGROUND: Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis. METHODS: A total of 854 whole slide images (WSIs) from 692 patients were included and divided into training and validation sets. The PAIDM was developed using the training set based on the deep learning algorithm ScanNet, and the performance was verified at the patch level in validation set 1 and at the WSI level in validation set 2. An independent validation cohort (validation set 3) was employed to compare the PAIDM and pathologists. Model performance was evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUCs of the PAIDM were 0.878 (95% CI 0.875-0.881) at the patch level in validation set 1 and 0.870 (95% CI 0.805-0.923) at the WSI level in validation set 2. In comparing the PAIDM and pathologists, the PAIDM achieved an AUC of 0.847 (95% CI 0.779-0.905), which was non-inferior to the average diagnostic level of pathologists. There was high consistency between the model-predicted and manually annotated areas, improving the PAIDM's interpretability. CONCLUSIONS: We reported an artificial intelligence-based diagnostic model for BCa that performed well in identifying invasion depth and histologic grade. Importantly, the PAIDM performed admirably in patch-level recognition, with a promising application for transurethral resection specimens.

Washed microbiota transplantation improves renal function in patients with renal dysfunction: a retrospective cohort study.

BACKGROUND: Changes in the gut microbiota composition is a hallmark of chronic kidney disease (CKD), and interventions targeting the gut microbiota present a potent approach for CKD treatment. This study aimed to evaluate the efficacy and safety of washed microbiota transplantation (WMT), a modified faecal microbiota transplantation method, on the renal activity of patients with renal dysfunction. METHODS: A comparative analysis of gut microbiota profiles was conducted in patients with renal dysfunction and healthy controls. Furthermore, the efficacy of WMT on renal parameters in patients with renal dysfunction was evaluated, and the changes in gut microbiota and urinary metabolites after WMT treatment were analysed. RESULTS: Principal coordinate analysis revealed a significant difference in microbial community structure between patients with renal dysfunction and healthy controls (P = 0.01). Patients with renal dysfunction who underwent WMT exhibited significant improvement in serum creatinine, estimated glomerular filtration rate, and blood urea nitrogen (all P < 0.05) compared with those who did not undergo WMT. The incidence of adverse events associated with WMT treatment was low (2.91%). After WMT, the Shannon index of gut microbiota and the abundance of several probiotic bacteria significantly increased in patients with renal dysfunction, aligning their gut microbiome profiles more closely with those of healthy donors (all P < 0.05). Additionally, the urine of patients after WMT demonstrated relatively higher levels of three toxic metabolites, namely hippuric acid, cinnamoylglycine, and indole (all P < 0.05). CONCLUSIONS: WMT is a safe and effective method for improving renal function in patients with renal dysfunction by modulating the gut microbiota and promoting toxic metabolite excretion.