Effect of cytochrome c release on the mitochondrial-dependent apoptosis and quality deterioration of black rockfish (Sebastes schlegelii) postmortem storage.

Apoptosis was associated with decreased sensory quality attributes of fish during postmortem storage. Based on cytochrome c (cyt-c) release plays a crucial role in apoptosis, the study aims to investigate the factors regulating cyt-c release and whether cyt-c acts as an endogenous pro-oxidant to trigger lipid oxidation. Within 12 h postmortem, dramatic changes in the intramuscular environment (glycogen from 1.57 mg/g to 0.65 mg/g; ATP reduced by 92.91%; pH value reaching the lowest (pH = 7.14)) and the mitochondrial environment (accumulation of mitochondrial ROS and Ca2+ levels) are induced mitochondrial swelling and opening of the MPTP (increased 34.35% and 31.91%), leading to the release of cyt-c from the mitochondria into the cytoplasm and the activation of caspase-3. This leads to lipid oxidation and degradation of myofibrillar proteins, accelerating quality deterioration in color and texture. The results suggest that cyt-c is involved in lipid oxidation during postmortem through the apoptotic mitochondrial pathway.

Morphology, immunophenotype, and suggested diagnostic criteria of TCL1 family-negative T-prolymphocytic leukemia.

OBJECTIVES: We sought to investigate the morphologic and immunophenotypic characteristics of TCL1 family-negative T-cell prolymphocytic leukemia (T-PLL). METHODS: Twenty cases of TCL1 family-negative T-PLL were studied. RESULTS: The doubling time of leukemic cells ranged from less than 2 days to more than 5 years, with a median of 5.5 months. Leukemic cells were small to medium-sized, with round to irregular nuclei, variably condensed chromatin, and small amounts of agranular cytoplasm. A visible nucleolus was identified in 11 (55%) cases. Cytoplasmic blebs/protrusions were identified in all cases, but their occurrence was highly variable from case to case. Bone marrow biopsy showed an interstitial pattern in 90% of cases and a diffuse pattern in the remaining 10% of cases. Flow cytometric immunophenotypic analysis showed that the leukemic cells in all cases were CD4 positive; 3 (15%) also showed concurrent CD8 expression. All cases were positive for CD2 and CD5. Surface CD3 and CD7 were positive in 19 of 20 (95%) cases, and all CD3-positive cases expressed the T-cell receptor αß. Compared with prototypic T-PLL cases, these 2 groups shared many immunophenotypic findings, except CD8 and CD26, both of which were more commonly expressed in prototypic T-PLL cases. CONCLUSIONS: TCL1 family-negative T-PLL cases have morphologic and immunophenotypic features that are similar to prototypic T-PLL. They are characterized by neoplastic proliferation of small to medium-sized mature T cells with CD4-positive T-cell receptor αß phenotype. Tumor cells frequently maintain pan-T antigen expression. Recognizing these morphologic and immunophenotypic features will aid in accurately diagnosing this rare subset of T-PLL.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

INTRODUCTION: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UTHealth) and the University of Minnesota (UMN). RESULTS: Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.

Anti-hepatoma immunotherapy of Pholiota adiposa polysaccharide-coated selenium nanoparticles by reversing M2-like tumor-associated macrophage polarization.

Targeting macrophages to regulate the tumor microenvironment is a promising strategy for treating cancer. This study developed a stable nano drug (PAP-SeNPs) using Se nanoparticles (SeNPs) and the Pholiota adiposa polysaccharide component (PAP-1a) and reported their physical stability, M2-like macrophages targeting efficacy and anti-hepatoma immunotherapy potential, as well as their molecular mechanisms. Furthermore, the zero-valent and well-dispersed spherical PAP-SeNPs were also successfully synthesized with an average size of 55.84 nm and a negative ζ-potential of -51.45 mV. Moreover, it was observed that the prepared PAP-SeNPs were stable for 28 days at 4 °C. Intravital imaging highlighted that PAP-SeNPs had the dual effect of targeting desirable immune organs and tumors. In vitro analyses showed that the PAP-SeNPs polarized M2-like macrophages towards the M1 phenotype to induce hepatoma cell death, triggered by the time-dependent lysosomal endocytosis in macrophages. Mechanistically, PAP-SeNPs significantly activated the Tlr4/Myd88/NF-κB axis to transform tumor-promoting macrophages into tumor-inhibiting macrophages and successfully initiated antitumor immunotherapy. Furthermore, PAP-SeNPs also enhanced CD3+CD4+ T cells and CD3+CD8+ T cells, thereby further stimulating anti-hepatoma immune responses. These results suggest that the developed PAP-SeNPs is a promising immunostimulant that can assist hepatoma therapy.

Radiotherapy in the treatment of primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder.

BACKGROUND: Primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder (PCSM-LPD) is an increasingly recognized entity with heterogeneous management strategies that may include radiotherapy. OBJECTIVE: Our aim was to characterize treatment options for PCSM-LPD, with a focus on the role of radiotherapy. METHODS: This is a retrospective review of 46 patients seen in the Cutaneous Lymphoma Program at the University of Texas MD Anderson Cancer Center, with a clinicopathologic review consistent with PCSM-LPD. All patients were biopsied and underwent observation, topical/intralesional steroids, and/or radiotherapy. Patients were confirmed to have residual disease prior to radiotherapy. RESULTS: All patients achieved a complete response (CR). Sixteen patients (35%) received focal radiotherapy, with a CR in 15 (94%). The CR rate following ultra-low-dose radiotherapy (4 Gy in 1-2 fractions) was 92%. There was no grade 3 toxicity after radiotherapy. Thirty patients were managed without radiotherapy, with excision and observation or steroids. CONCLUSION: Primary cutaneous CD4+ small/medium T-cell lymphoproliferative disorder has excellent outcomes, and management strategies may include observation following biopsy, steroids, or radiation. Ultra-low-dose radiotherapy results in excellent outcomes with limited toxicity and is effective for persistent lesions after steroidal therapy.

Deep Clustering: A Comprehensive Survey.

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), which can learn clustering-friendly representations using deep neural networks (DNNs), has been broadly applied in a wide range of clustering tasks. Existing surveys for DC mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this article, we provide a comprehensive survey for DC in views of data sources. With different data sources, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, DC methods are introduced according to four categories, i.e., traditional single-view DC, semi-supervised DC, deep multiview clustering (MVC), and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of DC.

A fine-grained dataset for sewage outfalls objective detection in natural environments.

Pollution sources release contaminants into water bodies via sewage outfalls (SOs). Using high-resolution images to interpret SOs is laborious and expensive because it needs specific knowledge and must be done by hand. Integrating unmanned aerial vehicles (UAVs) and deep learning technology could assist in constructing an automated effluent SOs detection tool by gaining specialized knowledge. Achieving this objective requires high-quality image datasets for model training and testing. However, there is no satisfactory dataset of SOs. This study presents a high-quality dataset named the images for sewage outfalls objective detection (iSOOD). The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. This study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv10 series objective detection model. Our study could provide high-quality SOs datasets for enhancing deep-learning models with UAVs to achieve efficient and intelligent river basin management.

Curcumin ameliorates pyroptosis in diabetic seminal vesicles by upregulating TRPV6.

BACKGROUND: Diabetes damages the seminal vesicle tissues leading to a decrease in seminal fluid secretion, so investigations are ongoing to identify specific therapeutic approaches to address diabetes-induced damage to seminal vesicles. OBJECTIVE: This study investigated the secretory dysfunction of seminal vesicles and how curcumin can ameliorate this dysfunction. MATERIALS AND METHODS: First, 40 diabetic males (DM group) and 40 nondiabetic males (control group) underwent seminal vesicle ultrasound evaluation and ejaculate volume measurements. Then, the effects of curcumin on seminal vesicle function were investigated in a diabetic rat model. Fifty 8-week-old SPF-grade SD rats were categorized into five groups: control, DM (diabetes mellitus), low-dose CUR (curcumin 50 mg/kg/d), medium-dose CUR (curcumin 100 mg/kg/d), and high-dose CUR (curcumin 150 mg/kg/d). After a month-long diet with varying curcumin doses, key parameters such as body weight, blood glucose levels, seminal vesicle volume, and seminal fluid secretion were measured. Transcriptome sequencing was performed to assess differences in gene expression and structural changes in rat seminal vesicle tissues were examined by HE staining. Finally, human seminal vesicle cell lines were cultured and divided into five groups (HG-CON, HG-CUR-5 µM, HG-CUR-10 µM, HG-CUR-20 µM, and HG-CUR-50 µM) to measure the fructose levels in the seminal vesicle cell culture fluids and evaluate the expression of CASP1, GSDMD, and TRPV6. Post TRPV6 interference, variations in the gene expression of CASP1, GSDMD, and TRPV6 were monitored. RESULTS: Diabetic patients exhibited a notable reduction in seminal vesicle volume and ejaculate volume compared with the control group, with a direct correlation between the decrease in ejaculate and seminal vesicle volume. Animal studies demonstrated that curcumin supplementation significantly augmented seminal vesicle volume in diabetic rats and notably improved their seminal vesicle secretory dysfunction, particularly in the high-dose curcumin group. Transcriptome sequencing and experimental verification pinpointed the differential expression of TPRV6 and pyroptosis-associated genes (CASP1, GSDMD), with reduced TRPV6 expression but increased markers of pyroptosis (CASP1 and GSDMD) in diabetic rats. Curcumin treatment reversed these effects with an increase in TRPV6 and a decrease in GSDMD and CASP1. Cell transfection experiments indicated that TRPV6 downregulation increased GSDMD and CASP1 gene expression. CONCLUSION: Curcumin effectively activates TRPV6, thereby diminishing pyroptosis in the seminal vesicle tissues of diabetic rats. This activation not only leads to an increase in the seminal vesicle volume but also significantly ameliorates the seminal vesicle secretory dysfunction in diabetic rats.

Positive and Negative Associations of Myopia with Ocular Diseases in Population-Based Studies.

OBJECTIVE: Prevalence of myopia and vision impairment due to myopic macular degeneration and myopia-related optic neuropathies have markedly increased worldwide. We evaluated whether myopia is associated with the prevalence of other ocular disorders in a positive or negative sense. DESIGN: Population-based studies conducted in Russia, China and India. PARTICIPANTS: The Russian Ural Eye and Medical Study (UEMS) and the Beijing Eye Study (BES) included 5899 individuals and 4439 individuals (all aged 40+ years), respectively, and the Central India Eye and Medical Study (CIEMS) consisted of 4711 individuals, aged 30+ years. The studies were conducted in rural and urban regions in Bashkortostan/Russia, Nagpur/India, and Beijing/China. METHODS: The participants underwent a series of ophthalmological and general medical examinations. MAIN OUTCOME MEASURES: Axial length as surrogate for myopia, and prevalence of diabetic retinopathy (DR), age-related macular degeneration (AMD), angle-closure glaucoma (ACG), and open-angle glaucoma (OAG). RESULTS: In the UEMS, DR prevalence (OR:0.73;95%CI:0.56,0.96), AMD prevalence (OR:0.85;95%CI:0.74,0.98) and ACG prevalence (OR:0.72;95%CI:0.55,0.95) decreased, and OAG prevalence (OR:1.65;95%CI:1.45,1.88) increased with longer axial length in multivariable analyses. In the CIEMS, lower AMD prevalence (OR:0.81;95%CI:0.69,0.95) and lower ACG prevalence (OR:0.55;95%CI:0.36,0.83), and higher OAG prevalence (OR:1.45;95%CI:1.15,1.83) were associated with longer axial length. DR prevalence (0.33%;95%CI:0.16,0.50) was too low for statistical analysis in the CIEMS. In the BES, prevalence (OR:0.64;95%CI:0.50,0.81) and 10-year incidence of DR (OR:0.48;95%CI:0.33,0.71) and prevalence (OR:0.83;95%CI:0.77,0.89) and 5-year incidence of AMD (OR:0.996;95%CI:0.993,0.999) decreased, and prevalence (OR:1.35;95% CI:1.17,1.56) and 10-year incidence of OAG (OR:1.40;95%CI:1.22,1.61) increased with longer axial length. In all three studies, the association between higher OAG prevalence and longer axial length was nonlinear with a slight increase for the moderate myopia range, and a steep increase in the highly myopic range. CONCLUSIONS AND RELEVANCE: Myopia is associated with a lower prevalence of DR, AMD and ACG and lower incidence of DR and AMD, while high myopia more than moderate myopia is associated with a higher prevalence and incidence of OAG. Future studies may assess whether in myopia, in particular in moderate myopia, the myopia-related advantages, i.e., lower prevalence of DR, AMD and ACG, may outweigh the increased risks for OAG and other myopia-related disorders.

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

BACKGROUND: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes. However, the traditional approach for subtype analysis requires large amounts of EHR data, which may contain protected health information causing potential concerns regarding patient privacy. Federated learning is the key technology to address privacy concerns while preserving the accuracy and performance of ML algorithms. Federated learning could enable multisite development and implementation of ML algorithms to facilitate the translation of artificial intelligence into clinical practice. OBJECTIVE: The aim of this study is to develop a research protocol for implementation of federated ML across a large clinical research network to identify and discover pediatric asthma subtypes and their progression over time. METHODS: This mixed methods study uses data and clinicians from the OneFlorida+ clinical research network, which is a large regional network covering linked and longitudinal patient-level real-world data (RWD) of over 20 million patients from Florida, Georgia, and Alabama in the United States. To characterize the subtypes, we will use OneFlorida+ data from 2011 to 2023 and develop a research-grade pediatric asthma computable phenotype and clinical natural language processing pipeline to identify pediatric patients with asthma aged 2-18 years. We will then apply federated learning to characterize pediatric asthma subtypes and their temporal progression. Using the Promoting Action on Research Implementation in Health Services framework, we will conduct focus groups with practicing pediatric asthma clinicians within the OneFlorida+ network to investigate the clinical utility of the subtypes. With a user-centered design, we will create prototypes to visualize the subtypes in the EHR to best assist with the clinical management of children with asthma. RESULTS: OneFlorida+ data from 2011 to 2023 have been collected for 411,628 patients aged 2-18 years along with 11,156,148 clinical notes. We expect to complete the computable phenotyping within the first year of the project, followed by subtyping during the second and third years, and then will perform the focus groups and establish the user-centered design in the fourth and fifth years of the project. CONCLUSIONS: Pediatric asthma subtypes incorporating RWD from diverse populations could improve patient outcomes by moving the field closer to precision pediatric asthma care. Our privacy-preserving federated learning methodology and qualitative implementation work will address several challenges of applying ML to large, multicenter RWD data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57981.

Data Set and Benchmark (MedGPTEval) to Evaluate Responses From Large Language Models in Medicine: Evaluation Development and Validation.

Background: Large language models (LLMs) have achieved great progress in natural language processing tasks and demonstrated the potential for use in clinical applications. Despite their capabilities, LLMs in the medical domain are prone to generating hallucinations (not fully reliable responses). Hallucinations in LLMs' responses create substantial risks, potentially threatening patients' physical safety. Thus, to perceive and prevent this safety risk, it is essential to evaluate LLMs in the medical domain and build a systematic evaluation. Objective: We developed a comprehensive evaluation system, MedGPTEval, composed of criteria, medical data sets in Chinese, and publicly available benchmarks. Methods: First, a set of evaluation criteria was designed based on a comprehensive literature review. Second, existing candidate criteria were optimized by using a Delphi method with 5 experts in medicine and engineering. Third, 3 clinical experts designed medical data sets to interact with LLMs. Finally, benchmarking experiments were conducted on the data sets. The responses generated by chatbots based on LLMs were recorded for blind evaluations by 5 licensed medical experts. The evaluation criteria that were obtained covered medical professional capabilities, social comprehensive capabilities, contextual capabilities, and computational robustness, with 16 detailed indicators. The medical data sets include 27 medical dialogues and 7 case reports in Chinese. Three chatbots were evaluated: ChatGPT by OpenAI; ERNIE Bot by Baidu, Inc; and Doctor PuJiang (Dr PJ) by Shanghai Artificial Intelligence Laboratory. Results: Dr PJ outperformed ChatGPT and ERNIE Bot in the multiple-turn medical dialogues and case report scenarios. Dr PJ also outperformed ChatGPT in the semantic consistency rate and complete error rate category, indicating better robustness. However, Dr PJ had slightly lower scores in medical professional capabilities compared with ChatGPT in the multiple-turn dialogue scenario. Conclusions: MedGPTEval provides comprehensive criteria to evaluate chatbots by LLMs in the medical domain, open-source data sets, and benchmarks assessing 3 LLMs. Experimental results demonstrate that Dr PJ outperforms ChatGPT and ERNIE Bot in social and professional contexts. Therefore, such an assessment system can be easily adopted by researchers in this community to augment an open-source data set.

Global research hotspots and trends in constraint-induced movement therapy in rehabilitation over the past 30 years: a bibliometric and visualization study.

Background: Stroke is a cerebrovascular disease with high prevalence and mortality, and upper limb hemiparesis is a major factor limiting functional recovery in stroke patients. Improvement of motor function in stroke patients through various forms of constraint-induced movement therapy (CITM) has been recognized as safe and effective in recent years. This research field lacks a comprehensive systematic and clear vein combing analysis, analyzing the literature research of CIMT in the field of rehabilitation in the past three decades, summarizing the research hotspots and cutting-edge trends in this field, in an effort to offer ideas and references for subsequent researchers. Methods: Relevant literature on CIMT in rehabilitation was collected from 1996 to 2024 within the Web of Science database's core dataset by using CiteSpace6.1, VOSviewer1.6.18, R-bibliometrix4.6.1, Pajek5.16, Scimago Graphica 1.0.26 software for visualization and analysis. Results: There were 970 papers in all United States was ranked first with 401 papers. Alabama Univ was ranked first for institutions with 53 papers. Neurorehabilitation and Neural Repair was ranked first for journals with 78 papers, and Taub E was ranked first for author publications with 64 papers. Research keywords were CIMT, stroke rehabilitation, upper extremity function, lower extremity gait balance, randomized controlled trials, physical therapy techniques (transcranial magnetic stimulation and sensory amplitude electrical stimulation), primary motor cortex plasticity, lateral dominance (spatial behaviors), cerebral vascular accidents, activities of daily living, hand function, disability, functional restoration, bimanual training, aphasia, acquired invalidity, type A Botulinum toxin and joystick riding toys. Conclusion: The current state of research shows that CIMT still has a vast potential for development in the field of rehabilitation research. The research hotspots are the clinical efficacy of CIMT combined with other therapies (botulinum toxin type A, transcranial direct current stimulation, virtual reality, mirror therapy, robotic-assisted) to enhance the functionality of upper limb hemiparesis in stroke patients, the mechanism of CIMT to improve the plasticity of the motor cortex through electrophysiological and imaging methods, and improvement of lower limb gait balance function in stroke patients and aphasia applications, the optimal intervention time and dose, and exploration of CIMT in new settings such as robot-assisted, telemedicine, and home rehabilitation.

Response-adapted ultra-low-dose 4 Gy radiation as definitive therapy of gastric MALT lymphoma: a single-centre, pilot trial.

BACKGROUND: Given the favourable prognosis of patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma, treatment-related toxicity should be minimised. We aimed to evaluate the efficacy of 4 Gy radiotherapy given in a response-adapted approach. METHODS: We conducted a single-centre, single-arm, prospective trial at MD Anderson Cancer Center (Houston, TX, USA) of response-adapted ultra-low-dose radiotherapy. Eligible patients were 18 years or older and had newly diagnosed or relapsed Helicobacter pylori-negative gastric MALT lymphoma, with stage I-IV disease. Given the expected low toxicity profile of treatment, performance status was not an exclusion criterion. Patients received external beam photon-based radiotherapy for a total dose of 4 Gy in two fractions. Patients with a complete response to 4 Gy via endoscopy and imaging at 3-4 months were observed; patients with a partial response were re-evaluated in 6-9 months. Residual disease at 9-13 months or stable or progressive disease at any time required additional treatment with 20 Gy. The primary endpoint was gastric complete response at 1 year (second evaluation timepoint) after 4 Gy treatment. All analyses were performed as intention to treat. This trial is registered at ClinicalTrials.gov (NCT03680586) and is complete and closed to enrolment. FINDINGS: Between March 27, 2019, and Oct 12, 2021, we enrolled 24 eligible patients. The median age of participants was 67 years (IQR 58-74; range 40-85); 15 (63%) were female and nine (37%) male; 18 (75%) were White, four (17%) Asian, and two (8%) Hispanic; 20 (83%) had stage I disease, one (4%) stage II, and three (13%) stage IV. Median follow-up time was 36 months (IQR 26-42). 20 patients (83%) had a complete response to 4 Gy (16 at 3-4 months, four at 9-13 months); two patients received 20 Gy for symptomatic stable disease at 3-4 months and two for residual disease at 9-13 months; all had a complete response. The 3-year local control rate was 96% (95% CI 88-100), with one local relapse at 14 months after 4 Gy radiotherapy salvaged successfully with 20 Gy. One patient with stage IV disease had a distant relapse. The most common adverse events were grade 1 nausea (nine [38%] of 24 patients who received 4 Gy and two [50%] of four patients who received 20 Gy) and grade 1 abdominal pain (five [21%] of 24 and zero of four, respectively). No grade 3 or worse adverse events were noted, including no treatment-related deaths. INTERPRETATION: Most patients had a complete response after 4 Gy radiotherapy; all who required an additional 20 Gy had a complete response within 12 months. This response-adapted strategy could be used to select patients who would benefit from additional radiotherapy and spare others potential associated toxicity. FUNDING: National Cancer Institute.

Arbuscular Mycorrhizal Fungi Regulate Lipid and Amino Acid Metabolic Pathways to Promote the Growth of Poncirus trifoliata (L.) Raf.

Arbuscular mycorrhizal (AM) fungi can enhance the uptake of soil nutrients and water by citrus, promoting its growth. However, the specific mechanisms underlying the action of AM fungi in promoting the growth of citrus were not fully elucidated. This study aimed to explore the role of AM fungi Funneliformis mosseae in the regulatory mechanisms of P. trifoliata growth. Pot experiments combined with non-targeted metabolomics methods were used to observe the growth process and changes in metabolic products of P. trifoliata under the conditions of F. mosseae inoculation. The results showed that F. mosseae could form an excellent symbiotic relationship with P. trifoliata, thereby enhancing the utilization of soil nutrients and significantly promoting its growth. Compared with the control, the plant height, stem diameter, number of leaves, and aboveground and underground dry weight in the F. mosseae inoculation significantly increased by 2.57, 1.29, 1.57, 4.25, and 2.78 times, respectively. Moreover, the root system results confirmed that F. mosseae could substantially promote the growth of P. trifoliata. Meanwhile, the metabolomics data indicated that 361 differential metabolites and 56 metabolic pathways were identified in the roots of P. trifoliata and were inoculated with F. mosseae. This study revealed that the inoculated F. mosseae could participate in ABC transporters by upregulating their participation, glycerophospholipid metabolism, aminoacyl tRNA biosynthesis, tryptophan metabolism and metabolites from five metabolic pathways of benzoxazinoid biosynthesis [mainly enriched in lipid (39.50%) and amino acid-related metabolic pathways] to promote the growth of P. trifoliata.

Potentiating dual-directional immunometabolic regulation with nanomedicine to enhance anti-tumor immunotherapy following incomplete photothermal ablation.

Photothermal therapy (PTT) is a promising cancer treatment method due to its ability to induce tumor-specific T cell responses and enhance therapeutic outcomes. However, incomplete PTT can leave residual tumors that often lead to new metastases and decreased patient survival in clinical scenarios. This is primarily due to the release of ATP, a damage-associated molecular pattern that quickly transforms into the immunosuppressive metabolite adenosine by CD39, prevalent in the tumor microenvironment, thus promoting tumor immune evasion. This study presents a photothermal nanomedicine fabricated by electrostatic adsorption among the Fe-doped polydiaminopyridine (Fe-PDAP), indocyanine green (ICG), and CD39 inhibitor sodium polyoxotungstate (POM-1). The constructed Fe-PDAP@ICG@POM-1 (FIP) can induce tumor PTT and immunogenic cell death when exposed to a near-infrared laser. Significantly, it can inhibit the ATP-adenosine pathway by dual-directional immunometabolic regulation, resulting in increased ATP levels and decreased adenosine synthesis, which ultimately reverses the immunosuppressive microenvironment and increases the susceptibility of immune checkpoint blockade (aPD-1) therapy. With the aid of aPD-1, the dual-directional immunometabolic regulation strategy mediated by FIP can effectively suppress/eradicate primary and distant tumors and evoke long-term solid immunological memory. This study presents an immunometabolic control strategy to offer a salvage option for treating residual tumors following incomplete PTT.

Standalone Stretchable Biophysical Sensing System Based on Laser Direct Write of Patterned Porous Graphene/Co3O4 Nanocomposites.

Skin-interfaced wearable sensors can continuously monitor various biophysical and biochemical signals for health monitoring and disease diagnostics. However, such devices are typically limited by unsatisfactory and unstable output performance of the power supplies under mechanical deformations and human movements. Furthermore, there is also a lack of a simple and cost-effective fabrication technique to fabricate and integrate varying materials in the device system. Herein, we report a fully integrated standalone stretchable biophysical sensing system by combining wearable biophysical sensors, triboelectric nanogenerator (TENG), microsupercapacitor arrays (MSCAs), power management circuits, and wireless transmission modules. All of the device components and interconnections based on the three-dimensional (3D) networked graphene/Co3O4 nanocomposites are fabricated via low-cost and scalable direct laser writing. The self-charging power units can efficiently harvest energy from body motion into a stable and adjustable voltage/current output to drive various biophysical sensors and wireless transmission modules for continuously capturing, processing, and wirelessly transmitting various signals in real-time. The novel material modification, device configuration, and system integration strategies provide a rapid and scalable route to the design and application of next-generation standalone stretchable sensing systems for health monitoring and human-machine interfaces.

Application and Development of Electrospun Nanofiber Scaffolds for Bone Tissue Engineering.

Nanofiber scaffolds have gained significant attention in the field of bone tissue engineering. Electrospinning, a straightforward and efficient technique for producing nanofibers, has been extensively researched. When used in bone tissue engineering scaffolds, electrospun nanofibers with suitable surface properties promote new bone tissue growth and enhance cell adhesion. Recent advancements in electrospinning technology have provided innovative approaches for scaffold fabrication in bone tissue engineering. This review comprehensively examines the utilization of electrospun nanofibers in bone tissue engineering scaffolds and evaluates the relevant literature. The review begins by presenting the fundamental principles and methodologies of electrospinning. It then discusses various materials used in the production of electrospun nanofiber scaffolds for bone tissue engineering, including natural and synthetic polymers, as well as certain inorganic materials. The challenges associated with these materials are also described. The review focuses on novel electrospinning techniques for scaffold construction in bone tissue engineering, such as multilayer nanofibers, multifluid electrospinning, and the integration of electrospinning with other methods. Recent advancements in electrospinning technology have enabled the fabrication of precisely aligned nanofiber scaffolds with nanoscale architectures. These innovative methods also facilitate the fabrication of biomimetic structures, wherein bioactive substances can be incorporated and released in a controlled manner for drug delivery purposes. Moreover, they address issues encountered with traditional electrospun nanofibers, such as mechanical characteristics and biocompatibility. Consequently, the development and implementation of novel electrospinning technologies have revolutionized scaffold fabrication for bone tissue engineering.

Combined Genetic Association and Differed Expression Analysis of UBE2L3 Uncovers a Genetic Regulatory Role of (Immuno)proteasome in IgA Nephropathy.

Introduction: IgA nephropathy (IgAN) is a leading cause of end-stage renal disease. The exact pathogenesis of IgAN is not well defined, but some genetic studies have led to a novel discovery that the (immuno)proteasome probably plays an important role in IgAN. Methods: We firstly analyzed the association of variants in the UBE2L3 region with susceptibility to IgAN in 3,495 patients and 9,101 controls, and then analyzed the association between lead variant and clinical phenotypes in 1,803 patients with regular follow-up data. The blood mRNA levels of members of the ubiquitin-proteasome system including UBE2L3 were analyzed in peripheral blood mononuclear cells from 53 patients and 28 healthy controls. The associations between UBE2L3 and the expression levels of genes involved in Gd-IgA1 production were also explored. Results: The rs131654 showed the most significant association signal in UBE2L3 region (OR: 1.10, 95% CI: 1.04-1.16, p = 2.29 × 10-3), whose genotypes were also associated with the levels of Gd-IgA1 (p = 0.04). The rs131654 was observed to exert cis-eQTL effects on UBE2L3 in various tissues and cell types, particularly in immune cell types in multiple databases. The UBE2L3, LUBAC, and proteasome subunits were highly expressed in patients compared with healthy controls. High expression levels of UBE2L3 were not only associated with higher proteinuria (r = 0.34, p = 0.01) and lower eGFR (r = -0.28, p = 0.04), but also positively correlated with the gene expression of LUBAC and other proteasome subunits. Additionally, mRNA expression levels of UBE2L3 were also positively correlated with IL-6 and RELA, but negatively correlated with the expression levels of the key enzyme in the process of glycosylation including C1GALT1 and C1GALT1C1. Conclusion: In conclusion, by combined genetic association and differed expression analysis of UBE2L3, our data support a role of genetically conferred dysregulation of the (immuno)proteasome in regulating galactose-deficient IgA1 in the development of IgAN.

Evaluating and Enhancing Large Language Models' Performance in Domain-specific Medicine: Explainable LLM with DocOA.

BACKGROUND: Evaluating and Enhancing Large Language Models' Performance in Domain-specific Medicine: Explainable LLM with DocOA. OBJECTIVE: This study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using osteoarthritis (OA) management as a case study. METHODS: A domain specific benchmark framework was developed, which evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation (RAG) and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through RAG, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. RESULTS: Results showed that general LLMs like GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. CONCLUSIONS: This study introduces a novel benchmark framework which assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.