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Abstract Background The 12-oxo-phytodienoic acid reductase (OPR) enzyme is crucial for the synthesis of jasmonates (JAs), and is involved in the plant stress response. However, the OPR gene family in sweetpotato, an important horticultural crop, remains unidentified. Results In this study, we employed bioinformatics techniques to identify nine IbOPR genes. Phylogenetic analysis revealed that these genes could be divided into Group I and Group II. Synteny analysis indicated that IbOPR evolution was driven by tandem duplication, whole-genome duplication (WGD), and segmental duplication events. The promoter sequences of IbOPRs were found to be associated with stress and hormonal responses. Additionally, we successfully cloned four IbOPRs from "Haida HD7791" and "Haida HD7798" using homologous cloning technology. These sequences were 1203 bp, 1200 bp, 1134 bp, and 1137 bp in length and encoded 400, 399, 377, and 378 amino acids, respectively. The protein sequence similarity between the salt-tolerant variety "Haida HD7791" and the salt-sensitive variety "Haida HD7798" was determined to be 96.75% for IbOPR2, 99.75% for IbOPR3, 92.06% for IbOPR6, and 98.68% for IbOPR7. Phylogenetic analysis categorized IbOPR2 and IbOPR3 proteins into Group II, while IbOPR6 and IbOPR7 proteins belonged to Group I. Subcellular localization experiments showed IbOPR2 protein present in the peroxisome, while IbOPR3, IbOPR6, and IbOPR7 proteins were found in the cytoplasm and nucleus. Salt stress induction experiments demonstrated that IbOPR2, IbOPR3, and IbOPR7 were significantly upregulated only in 'Haida HD7791' after 6 h. In contrast, IbOPR6 was induced in 'Haida HD7798' at 6 h but inhibited in 'Haida HD7791' at later time points (12, 24, 48, and 72 h), highlighting functional differences in salt stress responses. Conclusions Our findings suggest that IbOPR2 may play a crucial role in sweetpotato's response to salt stress by participating in JAs synthesis. These results provide a foundation for future functional analyses of OPR genes in sweetpotato.

Abstract Brain takes up approximately 20% of the total body oxygen and glucose consumption due to its relatively high energy demand. Glucose is one of the major sources to generate ATP, the process of which can be realized via glycolysis, oxidative phosphorylation, pentose phosphate pathways and others. Lactate serves as a hub molecule amid these metabolic pathways, as it may function as product of glycolysis, substrate of a variety of enzymes and signal molecule. Thus, the roles of lactate in central nervous system (CNS) diseases need to be comprehensively elucidated. Histone lactylation is a novel lactate-dependent epigenetic modification that plays an important role in immune regulation and maintaining homeostasis. However, there’s still a lack of studies unveiling the functions of histone lactylation in the CNS. In this review, we first comprehensively reviewed the roles lactate plays in the CNS under both physiological and pathological conditions. Subsequently, we’ve further discussed the functions of histone lactylation in various neurological diseases. Furthermore, future perspectives regarding histone lactylation and its therapeutic potentials in stroke are also elucidated, which may possess potential clinical applications.

Abstract Background Socio-economic status is associated with self-harm at the individual and area level. In Canada, there is limited evidence on the relationship between area-level markers of socio-economic status and self-harm. The objective of this study was to assess the impact of small area-level material and social deprivation on rates of hospitalization due to self-harm. Methods Administrative data on hospitalizations from two databases in Canada (Discharge Abstract Database and Ontario Mental Health Reporting System) for the period April 1, 2015 to March 31, 2022, were analyzed. Rates of self-harm hospitalization and percentage of repeated admissions were estimated across quintiles of material and social deprivation. Rate ratios were computed to evaluate disparities. The rates were stratified by sex and age group; the percentage of repeated admissions were stratified by sex. Results In total, the study included 109,398 hospitalizations due to self-harm. Populations in more deprived areas had higher rates of self-harm hospitalizations than those in less deprived areas. The rate ratios for people who lived in the most deprived areas over the least deprived areas were 1.48 (95% CI: 1.38–1.58) and 1.71 (95% CI: 1.60–1.82) for material and social deprivation, separately. The largest disparity was among people aged 25–44 years for material and 45–64 years for social deprivation. Percentages of repeated self-harm hospitalizations were significantly higher in more deprived areas compared to less deprived areas for social deprivation, and among males but not females for material deprivation. Conclusions Both material and social deprivation were associated with self-harm hospitalization and repeated admissions; the disparity varied by subgroup and the deprivation components. This study demonstrated a need to consider interventions at the neighbourhood level and address both community and population-level conditions of social and material need.

Abstract Background Hypoxic-ischemic encephalopathy (HIE) is a major cause of neonatal disability and mortality. Although intensive studies and therapeutic approaches, there are limited restorative treatments till now. Human embryonic stem cell (hESCs)-derived cortical neural progenitors have shown great potentials in ischemic stroke in adult brain. However, it is unclear whether they are feasible for cortical reconstruction in immature brain with hypoxic-ischemic encephalopathy. Methods By using embryonic body (EB) neural differentiation method combined with DAPT pre-treatment and quantitative cell transplantation, human cortical neuroblasts were obtained and transplanted into the cortex of hypoxic-ischemic injured brain with different dosages 2 weeks after surgery. Then, immunostaining, whole-cell patch clamp recordings and behavioral testing were applied to explore the graft survival and proliferation, fate commitment of cortical neuroblasts in vitro, neural circuit reconstruction and the therapeutic effects of cortical neuroblasts in HIE brain. Results Transplantation of human cortical neural progenitor cells (hCNPs) in HIE-injured cortex exhibited long-term graft overgrowth. DAPT pre-treatment successfully synchronized hCNPs from different developmental stages (day 17, day 21, day 28) to deep layer cortical neuroblasts which survived well in HIE injured brain and greatly prevented graft overgrowth after transplantation. Importantly, the cortical neuroblasts primarily differentiated into deep-layer cortical neurons and extended long axons to their projection targets, such as the cortex, striatum, thalamus, and internal capsule in both ipsilateral and contralateral HIE-injured brain. The transplanted cortical neurons established synapses with host cortical neurons and exhibited spontaneous excitatory or inhibitory post-synaptic currents (sEPSCs or sIPSCs) five months post-transplantation. Rotarod and open field tests showed greatly improved animal behavior by intra-cortex transplantation of deep layer cortical neuroblasts in HIE injured brain. Conclusions Transplanted hESCs derived cortical neuroblasts survive, project to endogenous targets, and integrate into host cortical neural circuits to rescue animal behavior in the HIE-injured brain without graft overgrowth, providing a novel and safe cell replacement strategy for the future treatment of HIE.

Abstract Branching networks are key elements in natural landscapes and have attracted sustained research interest across the geosciences and numerous intersecting fields. The prevailing consensus has long held that branching networks are optimized and exhibit fractal properties adhering to power-law scaling relationships. However, tidal networks in coastal wetlands and mudflats exhibit scaling properties that defy conventional power-law descriptions, presenting a longstanding enigma. Here we show that the observed atypical scaling represents a universal deviation from an ideal fractal branching network capable of fully occupying the available space. Using satellite imagery of tidal networks from diverse global locations, we identified an inherent “laziness” in this deviation—where the increased ease of channel formation paradoxically decreases the space-filling efficiency of the network. We developed a theoretical model that reproduces the ideal fractal branching network and the laziness phenomenon. The model suggests that branching networks can emerge under a localized competition principle without adhering to conventionally assumed optimization-driven processes. These results reveal the dual nature of branching networks, where “laziness” complements the well-known optimization process. This property provides more flexible strategies for controlling tidal network morphogenesis, with implications for coastal management, wetland restoration, and studies in fluvial and planetary systems.

Maesa flabellifera (Primulaceae-Maesoideae) from southeast Yunnan, China, is described and illustrated here. This new species belongs to the informal long corolla-tube species group and is morphologically similar to M. permollis and M. kurzii, but can be distinguished by lacking hairs, membranaceous leaves and long panicles with 7−16 branches. According to the IUCN criteria, M. flabellifera is assessed as “Least Concern”.

Artemisia sylvatica Maximowicz 1859 is one of the medicinal herbs in Artemisia. This study presents the complete chloroplast genome of A. sylvatica, sequenced using the Illumina NovaSeq platform. The genome is 151,161 bp in length, featuring a GC content of 38%. It consists of a large single-copy (LSC) region of 82,892 bp, a small single-copy (SSC) region of 18,353 bp, and two inverted repeat (IR) regions of 24,958 bp each. In total, the genome contains 132 genes, including 87 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Phylogenetic analysis positions A. sylvatica within the subgenus Artemisia, highlighting its evolutionary relationships within this diverse genus. The first chloroplast genome of A. sylvatica was reported in this work contributes to the enrichment of genomic data for the genus Artemisia.

Abstract The incidence of multiple primary lung cancer (MPLC) is increasing, with some of our surgical patients exhibiting numerous lesions. We defined lung cancer with five or more primary lesions as super MPLCs. Elucidating the genomic characteristics of this special MPLC subtype can help reduce disease burden and understand tumor evolution. In our cohort of synchronous super early-stage MPLCs (PUMCH-ssesMPLC), whole-exome sequencing on 130 resected malignant specimens from 18 patients provided comprehensive super-MPLC genomic landscapes. Mutations are enriched in PI3k-Akt and MAPK pathways. Their BRAF mutation frequency (31.5%) is significantly higher than MPLC with fewer lesions and early-stage single-lesion cancer, while EGFR mutations are significantly fewer (13.8%). As lesion counts increase, BRAF mutations gradually become dominant. Also, invasive lesions more tend to have classic super-MPLC mutation patterns. High-frequency BRAF mutations, especially Class II, and low-frequency EGFR mutations could be a reason for the limited effectiveness of targeted therapy in super-MPLC patients.

Abstract Background Volumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT. Methods The classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient’s anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets. Results This presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s. Conclusion The dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

Abstract Aims The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies. Methods The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system’s computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports. Results In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0–100.0%), a positive predictive value of 90.9% (95%CI: 76.5–100.0%), and an F1 score of 93.0% (95%CI: 83.7–100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction. Conclusions The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Abstract Aims To examine longitudinal and dose-d ependent associations between dietary fiber intake and various clinical outcomes over 48 weeks of pharmacological treatment in T2DM patients. Methods In this secondary analysis, we used data from the MARCH trial, which was designed to compare the efficacy of acarbose or metformin monotherapy as the initial therapy in Chinese patients newly diagnosed with T2DM. Dietary data were obtained using a 24-h dietary recall method to evaluate the intakes of dietary fiber from different sources as well as the carbohydrate-to-fiber ratio. Results A total of 551 newly-diagnosed patients with T2DM complete dietary records (286 in the acarbose group and 265 in the metformin group) were included. Higher intake of total fiber and whole grain fiber was positively associated with better β‐cell function, insulin sensitivity and postprandial glycemic control under acarbose treatment. Higher intake of legume fiber was associated with better glycemic control under both acarbose and metformin treatment but with better weight loss only under metformin treatment. A high-carbohydrate–low-fiber diet was associated with worse glycemic control and lower HDL-C under acarbose treatment but with higher insulin sensitivity and better weight loss under metformin treatment. Conclusions The notable effects of various dietary fibers when combined with different oral glucose-lowering medications should be considered to maximize therapeutic benefit.

Lipid metabolism imbalance combined with over-activated inflammation are two key factors for hepatic stestosis. However, on-demand anchoring inflammation and lipid metabolism disorder for hepatic stestosis treatment has yet to be realized. Here we propose a charge reversal fullerene based nano-assembly to migrate hepatic steatosis via inhibiting macrophage-mediated inflammation and normalizing hepatocellular lipid metabolism in obesity mice. Our nano-assembly (abbreviated as FPPD) is comprised of electropositive polyetherimide (PEI), charge-shielded dimethylmaleic anhydride (DMA), and poly(lactic-co-glycolic acid) (PLGA), which provides hydrophobic chains for self-assembly with anti-oxidative dicarboxy fullerene poly(ethylene glycol) molecule (FP). The obtained FPPD nano-assembly owns a charge reversal ability that switches to a positive charge in an acidic environment that targets the electronegative mitochondria both in pro-inflammatory macrophages and steatosis hepatocytes. We demonstrate that the anti-oxidative and mitochondria-targeting FPPD notably reduces inflammation in macrophages and lipid accumulation in hepatocytes by quenching excessive reactive oxygen species (ROS) and improving mitochondrial function in vitro. Importantly, FPPD nano-assembly reveals a superior anti-hepatic steatosis effect via migrating inflammation and facilitating lipid transport in obesity mice. Overall, the charge reversal nano-assembly reduces over-activated inflammation and promotes lipid metabolism that provides an effectiveness of a multi-target strategy for hepatic steatosis treatment.

Abstract NMDA receptor-dependent long-term depression (LTD) in the hippocampus is a well-known form of synaptic plasticity that has been linked to different cognitive functions. Although the underlying mechanisms remain unclear, this form of LTD cannot be induced by low-frequency stimulation (LFS) in adult mice. In this study, we found that LFS-induced LTD was not easily induced in adult animals and was age dependent. Interestingly, the level of the 5-HT1A receptor was correspondingly increased and exhibited an inverse correlation with the magnitude of LFS-LTD during development. Knockout or pharmacological inhibition of the 5-HT1A receptor reversed impaired LFS-LTD in adult mice (P60), while activation or inhibition of this receptor disturbed or enhanced LFS-LTD in adolescent mice (P21), respectively. Furthermore, the astrocytic 5-HT1A receptor in the hippocampus predominantly mediated age-dependent LFS-LTD through enhancing GABAergic neurotransmission. Finally, fear memory extinction differed among the above conditions. These observations enrich our knowledge of LTD at the cellular level and suggest a therapeutic approach for LTD-related psychiatric disorders.

Abstract In recent years, significant improvement has been made in the management of non‐small cell lung cancer (NSCLC), primarily driven by advances in targeted therapy and immunotherapy. Research on neoadjuvant targeted therapy has also experienced considerable development, primarily directed towards NSCLC harboring epidermal growth factor receptor or anaplastic lymphoma kinase mutations. Nevertheless, there remains a dearth of studies investigating neoadjuvant targeted therapy in the context of BRAF (V‐Raf murine sarcoma viral oncogene homolog B) V600E mutant NSCLC. Herein, we describe the clinical trajectory of a stage IIIA NSCLC patient who underwent a two‐month course of neoadjuvant targeted therapy comprising BRAF and MEK (mitogen‐activated extracellular signal‐regulated kinase) inhibitors prior to surgical intervention, and subsequent postoperative evaluation unveiled a pathological complete response. The case reported here indicates the efficacy and safety of combining BRAF and MEK inhibitors as neoadjuvant targeted therapy in BRAF V600E‐mutant NSCLC and suggests the potential viability of such a therapeutic modality in improving treatment outcomes in this subset of NSCLC.

Abstract Focal segmental glomerulosclerosis (FSGS), a clinicopathological condition characterized by nephrotic-range proteinuria, has a high risk of progression to end-stage renal disease (ESRD). Meanwhile, the recurrence of FSGS after renal transplantation is one of the main causes of graft loss. The diagnosis of recurrent FSGS is mainly based on renal puncture biopsy transplants, an approach not widely consented by patients with early mild disease. Therefore, there is an urgent need to find definitive diagnostic markers that can act as a target for early diagnosis and intervention in the treatment of patients. In this review, we summarize the domestic and international studies on the pathophysiology, pathogenesis and earliest screening methods of FSGS and describe the functions and roles of specific circulating factors in the progression of early FSGS, in order to provide a new theoretical basis for early diagnosis of FSGS recurrence, as well as aid the exploration of therapeutic targets.

Abstract Aquaporin 3 (AQP3), which is mostly expressed in pulmonary epithelial cells, was linked to lung adenocarcinoma (LUAD). However, the underlying functions and mechanisms of AQP3 in the tumor microenvironment (TME) of LUAD have not been elucidated. Single-cell RNA sequencing (scRNA-seq) was used to study the composition, lineage, and functional states of TME-infiltrating immune cells and discover AQP3-expressing subpopulations in five LUAD patients. Then the identifications of its function on TME were examined in vitro and in vivo. AQP3 was associated with TNM stages and lymph node metastasis of LUAD patients. We classified inter- and intra-tumor diversity of LUAD into twelve subpopulations using scRNA-seq analyses. The analysis showed AQP3 was mainly enriched in subpopulations of M2 macrophages. Importantly, mechanistic investigations indicated that AQP3 promoted M2 macrophage polarization by the PPAR-γ/NF-κB axis, which affected tumor growth and migration via modulating IL-6 production. Mixed subcutaneous transplanted tumor mice and Aqp3 knockout mice models were further utilized, and revealed that AQP3 played a critical role in mediating M2 macrophage polarization, modulating glucose metabolism in tumors, and regulating both upstream and downstream pathways. Overall, our study demonstrated that AQP3 could regulate the proliferation, migration, and glycometabolism of tumor cells by modulating M2 macrophages polarization through the PPAR-γ/NF-κB axis and IL-6/IL-6R signaling pathway, providing new insight into the early detection and potential therapeutic target of LUAD.

Abstract In nature, coenzyme-independent oxidases have evolved in selective catalysis using isolated substrate-binding pockets. Single-atom nanozymes (SAzymes), an emerging type of non-protein artificial enzymes, are promising to simulate enzyme active centers, but owing to the lack of recognition sites, realizing substrate specificity is a formidable task. Here we report a metal-ligand dual-site SAzyme (Ni-DAB) that exhibited selectivity in uric acid (UA) oxidation. Ni-DAB mimics the dual-site catalytic mechanism of urate oxidase, in which the Ni metal center and the C atom in the ligand serve as the specific UA and O2 binding sites, respectively, characterized by synchrotron soft X-ray absorption spectroscopy, in situ near ambient pressure X-ray photoelectron spectroscopy, and isotope labeling. The theoretical calculations reveal the high catalytic specificity is derived from not only the delicate interaction between UA and the Ni center but also the complementary oxygen reduction at the beta C site in the ligand. As a potential application, a Ni-DAB-based biofuel cell using human urine is constructed. This work unlocks an approach of enzyme-like isolated dual sites in boosting the selectivity of non-protein artificial enzymes.

Rationale & Objective: Hypokalemia is common and potentially life-threatening in patients undergoing peritoneal dialysis (PD). However, the current literature has produced varying results. This study aimed to evaluate the prevalence and adverse outcomes of hypokalemia and the role of potassium supplementation in patients receiving PD. Study Design: Systematic review and meta-analysis of randomized controlled trials and observational studies. Setting & Study Populations: Adults receiving maintenance PD. Selection Criteria for Studies: Studies that investigated the prevalence and adverse outcomes of hypokalemia and the effect of potassium supplementation. Data Extraction: Two independent reviewers evaluated studies for eligibility and extracted relevant data. Analytical Approach: Random effects meta-analysis was conducted to pool hazard ratios (HRs) and 95% CIs for the outcomes of interest. The certainty of findings was rated according to the Grading of Recommendations Assessment, Development and Evaluation criteria. Results: Of 3,632 reports identified, 24 studies involving 60,313 participants met the inclusion criteria. The prevalence of hypokalemia was 37.9% (95% CI, 27.2%-52.7%), 17.7% (95% CI, 12.0%-25.9%), and 4.4% (95% CI, 1.9%-10.2%) in patients with potassium level

IntroductionDiabetic retinopathy (DR) is a common complication of diabetes. Retinal neuronal degeneration is an early event in DR, indicated by the declined electroretinogram (ERG). Dipeptide alanine-glutamine (Ala-Gln) is widely used as a nutritional supplement in the clinic and has anti-inflammatory effects on the gastrointestinal system. Studies also reported that glutamine has beneficial effects on diabetes. This study aimed to investigate the possible therapeutic effects of Ala-Gln in diabetic retinal neurodegeneration and to delineate its mechanism of action.MethodsThe Streptozotocin (STZ)-induced rat model was used as a DR model. ERG was used to measure the neuronal function of the retina. Western blot analysis was performed to test the expression of proteins. Immunofluorescence staining was used for the detection and localization of proteins.ResultsIn diabetic rats, the amplitudes of ERG were declined, while Ala-Gln restored the declined ERG. Retinal levels of inflammatory factors were significantly decreased in Ala-Gln-treated diabetic rats. Ala-Gln mitigated the declined levels of glutamine synthetase and ameliorated the upregulated levels of glial fibrillary acidic protein (GFAP) in diabetic retinas. Moreover, Ala-Gln upregulated the glycolytic enzymes pyruvate kinase isozymes 2 (PKM2), lactate dehydrogenase A (LDHA) and LDHB and stimulated the mTOR signaling pathway in diabetic retinas. The mitochondrial function was improved after the treatment of Ala-Gln in diabetic retinas.DiscussionAla-Gln ameliorates retinal neurodegeneration by reducing inflammation and enhancing glucose metabolism and mitochondrial function in DR. Therefore, manipulation of metabolism by Ala-Gln may be a novel therapeutic avenue for retinal neurodegeneration in DR.

Objectives To evaluate and analyse the quality of clinical practice guidelines for Peripherally Inserted Central Catheter-related thrombosis (PICC-related thrombosis) to identify the most current and effective prophylactic measures recommended in the guidelines.Design Scoring and analysis of the guidelines using the Appraisal of Guidelines for Research and Evaluation II (AGREE II).Data sources Cochrane Library, PubMed, EMBASE, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Chinese databases (China National Knowledge Infrastructure and Wan Fang) and the relevant websites of the guideline were searched through 8 August 2024.Eligibility criteria for selecting studies Studies that primarily clinical practice guidelines on the prevention of PICC-related thrombosis were included.Data extraction and synthesis Two reviewers independently screened the searched items and extracted data and scored documents using AGREE II. Findings were summarised in Grading of Recommendation, Assessment, Development and Evaluation (GRADE) evidence profiles and synthesised qualitatively.Results The analysis incorporated a total of nine guidelines, all rated as ‘recommended’ or ‘recommended with modifications’. Standardised scores revealed elevated performance in the domains of Scope and Purpose, Clarity of Presentation and Editorial Independence. Conversely, the Stakeholder Involvement and Applicability domains yielded the lowest average standardised scores. Disparities in standardised scores across guidelines were particularly evident in the domains of Rigour of Development, Stakeholder Involvement and Applicability. The agreement between the two appraisers was almost perfect (intraclass correlation coefficients higher than 0.80). A considerable proportion of recommendations relied on evidence of low-quality, in certain instances, were derived from expert opinions within working groups.Conclusions The review reveals that a significant portion of recommendations relies on low-quality evidence. Guideline developers are urged to prioritise methodological quality, with a specific focus on refining Stakeholder Involvement and Applicability domains. Addressing these aspects will enhance the overall quality and reliability of PICC-related thrombosis prevention guidelines. One potential way to mitigate these challenges is to endorse a standardised approach to guideline development and to synthesise reliable clinical evidence to reduce variation in recommendations.PROSPERO registration number CRD42023495519.

Introduction and Objectives: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. Patients and Methods: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. Results: Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium 6.8 μmol/L, hemoglobin 40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. Conclusions: The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Background SLE is a complex autoimmune disease with heterogeneous manifestations and unpredictable outcomes. Early diagnosis is challenging due to non-specific symptoms, and current treatments only manage symptoms. Epigenetic alternations, including 5-Hydroxymethylome (5hmC) modifications, are important contributors to SLE pathogenesis. However, the 5hmC modification status in circulating cell-free DNA (cfDNA) of patients with SLE remains largely unexplored. We investigated the distribution of 5hmC in cfDNA of patients with SLE and healthy controls (HCs), and explored its potential as an SLE diagnosis marker.Methods We used 5hmC-Seal to generate genome-wide 5hmC profiles of plasma cfDNA and bioinformatics analysis to screen differentially hydroxymethylated regions (DhMRs). In vitro mechanistic exploration was conducted to investigate the regulatory effect of CCCTC-binding factor (CTCF) in 5hmC candidate biomarkers.Results We found distinct differences in genomic regions and 5hmC modification motif patterns between patients with SLE and HCs, varying with disease progression. Increased 5hmC modification enrichment was detected in SLE. Additionally, we screened 151 genes with hyper-5hmC, which are significantly involved in SLE-related processes, and 5hmC-modified BCL2, CD83, ETS1 and GZMB as SLE biomarkers. Our findings suggest that CTCF regulates 5hmC modification of these genes by recruiting TET (ten-eleven translocation) protein, and CTCF knockdown affected the protein expression of these genes in vitro.Conclusions Our findings demonstrate the increased 5hmC distribution in plasma cfDNA in different disease activity in patients with SLE compared with HCs and relating DhMRs involved in SLE-associated pathways. Furthermore, we identified a panel of SLE relevant biomarkers, and these viewpoints could provide insight into the pathogenesis of SLE.

BackgroundSurgical approaches that access the posterior temporal bone require careful drilling motions to achieve adequate exposure while avoiding injury to critical structures.ObjectiveWe assessed a deep learning hand motion detector to potentially refine hand motion and precision during power drill use in a cadaveric mastoidectomy procedure.MethodsA deep-learning hand motion detector tracked the movement of a surgeon's hands during three cadaveric mastoidectomy procedures. The model provided horizontal and vertical coordinates of 21 landmarks on both hands, which were used to create vertical and horizontal plane tracking plots. Preliminary surgical performance metrics were calculated from the motion detections.Results1,948,837 landmark detections were collected, with an overall 85.9% performance. There was similar detection of the dominant hand (48.2%) compared to the non-dominant hand (51.7%). A loss of tracking occurred due to the increased brightness caused by the microscope light at the center of the field and by movements of the hand outside the field of view of the camera. The mean (SD) time spent (seconds) during instrument changes was 21.5 (12.4) and 4.4 (5.7) during adjustments of the microscope.ConclusionA deep-learning hand motion detector can measure surgical motion without physical sensors attached to the hands during mastoidectomy simulations on cadavers. While preliminary metrics were developed to assess hand motion during mastoidectomy, further studies are needed to expand and validate these metrics for potential use in guiding and evaluating surgical training.

Abstract A series of arecoline derivatives with amino acid moieties were designed and synthesised using an acylamide condensation strategy, taking arecoline as the foundational structure. The insecticidal efficacy of these compounds against Aphis craccivora and Tetranychus cinnabarinus was evaluated. Notably, derivatives 3h and 3i demonstrated superior insecticidal activity compared with arecoline. Additionally, 3h and 3i showed good fungicidal effectiveness against two types of plant fungi. Moreover, molecular docking analyses suggested that 3h and 3i could affect the nervous systems of A. craccivora and T. cinnabarinus by binding to neuronal nicotinic acetylcholine receptors. These findings suggest that compounds 3h and 3i represent promising leads for further development in insecticide and fungicide research.

The Chinese Society of Critical Care Medicine (CSCCM) has developed clinical practice guidelines for nutrition assessment and monitoring for patients in adult intensive care units (ICUs) in China. This guideline focuses on nutrition evaluation and metabolic monitoring to achieve optimal and personalized nutrition therapy for critically ill patients. This guideline was developed by experts in critical care medicine and evidence-based medicine methodology and was developed after a thorough review of the system and a summary of relevant trials or studies published from 2000 to July 2023. A total of 18 recommendations were formed and consensus was reached through discussions and reviews by expert groups in critical care medicine, parenteral and enteral nutrition, and surgery. The recommendations are based on currently available evidence and cover several key fields, including screening and assessment, evaluation and assessment of enteral feeding intolerance, metabolic and nutritional measurement and monitoring during nutrition therapy, and organ function evaluation related to nutrition supply. Each question was analyzed according to the Population, Intervention, Comparison, and Outcome (PICO) principle. In addition, interpretations were provided for four questions that did not reach a consensus but may have potential clinical and research value. The plan is to update this nutrition assessment and monitoring guideline using the international guideline update method within 3–5 years.

Objective To evaluate validity of hospital-discharged abstract administrative health data (DAD) over years through chart reviews. Methods We analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Calgary, Canada. Nurse reviewers determined the presence or absence of 17 clinical conditions employing similar protocols. The reviews were linked with DAD using a unique lifetime identifier, chart number, and admission date. We evaluated the validity of DAD, coded in ICD-10-Canada version, in recording conditions by comparing against chart reviews. The C-statistics was calculated in predicting in-hospital mortality. Results The mean difference in prevalence between chart reviews and DAD for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. However, some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For 17 conditions, the sensitivity ranged 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for in-hospital mortality based on DAD was 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022. Conclusion DAD increasingly under-coded conditions over 19 years. The validity of DAD decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction. Implications The under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. Consequently, there is a need to develop artificial intelligence methods based on electronic medical records to support coding practices and improve coding quality.

Objective Administrative data often underrepresents hospital adverse events (AEs) due to limitations in International Classification of Diseases (ICD) coding. By leveraging electronic medical records (EMRs), we aim to mitigate these discrepancies and enhance the precision of healthcare surveillance and performance evaluations. To this end, we have developed a machine learning (ML)-based approach that utilizes EMR text data to detect common AEs. Methods We sampled adult admissions from four Calgary hospitals (2017 - 2022). Registered nurses assessed charts for 17 AEs, and the results were used as reference standard. We compared two AE detection methods: the standard ICD-based method following Canadian guidelines, and our ML algorithm applied to EMR narratives. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and specificity for both methods were calculated and compared against the reference standard. Results We analyzed 9,566 patients, of whom 1,506 were identified with AEs. Of the 17 AEs, the sensitivity in ICD-coded data ranged from 0-37%, and in EMRs, it was between 75-100%. Both showed low PPV (0-50% ICD vs.1-34% EMR). ICD data had high specificity ranging from 99-100% and NPV (99%-100%), while EMRs had specificity between 68-94% and an NPV of 100%. Conclusion and Implications ML significantly enhances sensitivity for AE detection compared to ICD-10-CA coding, despite both methods experiencing low PPV due to imbalances in EMR data. This marked improvement in sensitivity highlights ML's potential to transform AE surveillance and reporting, promising significant advancements in patient safety and healthcare quality by enabling more accurate and comprehensive identification of AEs.

：Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Existing models, such as ConvLSTM, TrajGRU, and PredRNN, exhibit limitations in capturing fine-grained appearances due to insufficient memory units or addressing positional misalignment issues, thereby compromising the accuracy of model predictions. In this study, we propose trajPredRNN+, an innovative approach that integrates the trajectory segmentation model and the PredRNN deep learning model to address both limitations in nowcasting precipitation using weather radar echo images. By incorporating attention mechanisms, the model demonstrates an enhanced focus on short-term and imminent heavy rainfall events. To ensure improved stability during training, a residual network is introduced. Lastly, a more rational and effective training loss function is proposed, encompassing weight mechanism, SSIM index, and GAN loss. To validate the proposed model, we conducted a comparative experiment and an ablation experiment using the radar echo map dataset obtained from the Shenzhen Meteorological Bureau. The results of these experiments demonstrate that our model has achieved significant improvements across multiple key performance indicators.

Abstract Isoprene, a volatile hydrocarbon, is typically emitted from the leaves of many plant species. Given its well‐known function in plant growth and defense aboveground, we examined its effects on root physiology. We used isoprene‐emitting (IE) lines and a non‐emitting (NE) line of Arabidopsis and investigated their performance by analyzing root phenotype, hormone levels, transcriptome, and metabolite profiles under both normal and salt stress conditions. We show that IE lines emitted tiny amounts of isoprene from roots and showed an increased root/shoot ratio compared with NE line. Isoprene emission exerted a noteworthy influence on hormone profiles related to plant growth and stress response, promoting root development and salt‐stress resistance. Methyl erythritol 4‐phosphate pathway metabolites, precursors of isoprene and hormones, were higher in the roots of IE lines than in the NE line. Transcriptome data indicated that the presence of isoprene increased the expression of key genes involved in hormone metabolism/signaling. Our findings reveal that constitutive root isoprene emission sustains root growth under saline conditions by regulating and/or priming hormone biosynthesis and signaling mechanisms and expression of key genes relevant to salt stress defense.

Context and backgroundMicroplastics (MPs) and heavy metals (HMs) coexist in the farmland of China.MotivationIt still remains unclear the extent of their exposure and distribution in sweet potato fields.HypothesisPolyethylene (PE) or polyamide (PA) is the main MP pollutant in contaminated sweet potato sites, and the MP abundance in low-latitude and eastern areas is highest. Methods: In this study, saturated NaCl solution, a stereo microscope, a Fourier transform infrared spectrometer, and an electrothermal digester are used for the extraction, observation, identification of MPs, and analysis of Cd elements in soil, respectively.MethodsIn this study, saturated NaCl solution, a stereo microscope, a Fourier transform infrared spectrometer, and an electrothermal digester are used for the extraction, observation, identification of MPs, and analysis of Cd elements in soil, respectively.ResultsHere, we found an average MP level of 112,400 items/kg in 30 sweet potato field sites based on the items in 5 g soil and the magnification (200x), and the maximum abundance was 197,153 items/kg in Laiyang city, Shandong province, by field survey. The distribution characteristics of MPs are middle-latitude areas < low-latitude areas, and eastern areas > central areas. Most MPs are of the fragment and film shape, which account for 47.96 and 40.22%, respectively. In order to detect MP polymers in three cities named “Liancheng,” “Huanggang,” and “Laiyang” with different degrees of development, a laser infrared imaging system was used as a novel instrument to explore the MPs larger than 10 μm.ConclusionThe results showed that PA is the main MP pollutant in contaminated sweet potato sites, and soil texture, planting time, and urbanization processes may be the main factors affecting MP distribution. The average cadmium (Cd) concentration in 215 field sites is 0.15 mg/kg, and the local Cd pollution is existing, but the overall pollution is low. In addition, Cd concentration was negatively correlated with MP abundance. This study reveals the status of MP and also Cd pollution in sweet potato fields, which provides a theoretical basis for the safe production and utilization of sweet potato fields.

A new species group of Lilioceris Reitter, 1913 is proposed and reviewed, the semipunctata group. It includes eleven species: L. atrilateralis Kimoto & Takizawa, 1973, L. consentanea (Lacordaire, 1845), L. dentifemoralis Long, 1988, L. discrepens (Baly, 1879), L. jianfenglingensis Long, 1988, L. latissima (Pic, 1932), L. lianzhouensis Long, 2000a, L. rondoni Kimoto & Gressitt, 1979, L. rufometallica (Pic, 1923), L. semipunctata (Fabricius, 1801), and L. yuae Long, 2000b. Lilioceris discrepens and L. rondoni were new records from China. Two synonyms are proposed: Lilioceris xinglongensis Long, 1988, syn. nov. of L. consentanea (Lacordaire, 1845), and Crioceris rufimembris Pic, 1921, syn. nov. of L. semipunctata. An identification key, descriptions, habitus photographs, geographic distributions, host plants and habitats (if available), are provided for these species.

Abstract The Russia–Ukraine conflict has caused a global food security crisis, impacting sustainable development goals. Predicting the crisis’s impact on food security is crucial for global stability by 2030. From a macro-perspective, this paper constructs a food security evaluation indicator system and a food security composite index (FSCI), and using the autoregressive integrated moving average model to predict the variations in the FSCI for different regions of the world from 2023 to 2030 under scenarios with or without the “Russia–Ukraine conflict.” By quantitatively analyzing the differences in these variations, the potential impact of the conflict on regional food security is assessed. The results conclude that the global food security level progressively improved over the past 20 years. The FSCI in Europe, Latin America and Caribbean increased at a faster pace than the global average, with growth rates of 0.035/(10 years) and 0.034/(10 years), respectively. However, the FSCI in the Sub-Saharan Africa showed a declining trend. By 2030, it is expected that the Russia–Ukraine conflict will have a significant impact on the food security of Europe and Sub-Saharan Africa, with a contribution of 1.49% and 0.29%, respectively. However, the impact of the conflict on food security levels in Asia and Latin America and Caribbean is relatively small. This study introduces a new quantitative method to assess and project the overall influence of the Russia–Ukraine conflict on food security. The findings contribute crucial scientific support for effectively evaluating and monitoring the sustainable development objectives related to global food security.

Abstract Background Though (1S, 3R)-RSL3 has been used widely in basic research as a small molecular inducer of ferroptosis, the toxicity on normal cells and poor pharmacokinetic properties of RSL3 limited its clinical application. Here, we investigated the synergism of non-thermal plasma (NTP) and low-concentration RSL3 and attempted to rise the sensitivity of NSCLC cells on RSL3. Methods CCK-8 assay was employed to detect the change of cell viability. Microscopy and flowcytometry were applied to identify lipid peroxidation, cell death and reactive oxygen species (ROS) level respectively. The molecular mechanism was inspected with western blot and RT-qPCR. A xenograft mice model was adopted to investigate the effect of NTP and RSL3. Results We found the synergism of NTP and low-concentration RSL3 triggered severe mitochondria damage, more cell death and rapid ferroptosis occurrence in vitro and in vivo. NTP and RSL3 synergistically induced xCT lysosomal degradation through ROS/AMPK/mTOR signaling. Furthermore, we revealed mitochondrial ROS was the main executor for ferroptosis induced by the combined treatment. Conclusion Our research shows NTP treatment promoted the toxic effect of RSL3 by inducing more ferroptosis rapidly and provided possibility of RSL3 clinical application. Graphical Abstract Video Abstract

Objective Artificial hip arthroplasty (AHA) is widely accepted in elderly patients with femoral neck fractures, but it is associated with high risk of death and various postoperative complications due to old age and accompanying chronic diseases. Therefore, this study aimed to explore the risk factors for death in elderly patients with femoral neck fractures after AHA and to establish a nomogram risk prediction model, which is expected to reveal high‐risk patients and improve the postoperative quality of life and survival rate of patients. Methods Elderly patients who underwent AHA for femoral neck fractures in our hospital from September 2014 to May 2021were retrospectively analyzed. These patients were divided into a survival group and a death group according to their clinical outcomes. The following clinical data were recorded for the patients in the two groups: sex, age, underlying diseases, smoking and drinking history, preoperative nutritional risk score (NRS) and American Society of Anesthesiologists (ASA) score, as well as relevant indicators about the operation. These data were subject to univariate analysis and then logistic analysis to determine the risk factors of death. Subsequently, a nomogram risk prediction model was established and further validated with the receiver operating characteristic curve (ROC) and the Hosmer–Lemeshow test. Finally, the effects of predictive risk factors were analyzed using the Kaplan–Meier survival curve. Results Follow‐up was completed by 260 patients, including 206 patients in the survival group and 54 patients in the death group; the overall death rate was 20.77%, and the follow‐up time, age, postoperative 1, 3 and 5‐year death rates were 3.47 ± 1.93 years, 75.32 ± 9.12 years, 5.77%, 12.51%, and 25.61%, respectively. The top three causes of death in 54 patients were respiratory disease, cerebrocardiovascular disease, and digestive disease, respectively. The logistic analysis indicated that elderly patients with femoral neck fractures, the risk factors for death after AHA were age ≥ 80 years, preoperative NRS ≥ 4, HB ≤ 90 g/L, CR ≥ 110 umol/L, and ASA score ≥ 3, as well as postoperative albumin ≤ 35 g/L, the nomogram was established, and then its predictive performance was successfully validated using the ROC curve (AUC = 0.814, 95% confidence interval = 0.749–0.879) and the Hosmer–Lemeshow test (p = 0.840). Furthermore, Kaplan–Meier survival curve analysis revealed that the abovementioned six indicators were correlated with the post‐AHA survival time of elderly patients with femoral neck fractures (pLog Rank

In recent years, pedestrian tracking algorithms have witnessed extensive utilization in various domains. Particularly noteworthy is the attention garnered by hospital pedestrian tracking due to its potential to enhance operational efficiency and patient’s safety. The continuously stable and accurate patients’ position information can help hospitals to guarantee that patients remain within designated areas and get timely assistance when necessary. In this work, in order to improve the robustness of the data fusion filter for the pedestrian tracking, one robust error state FIR filter (ESFIR) will been proposed. To the data fusion model, the quaternion-based data fusion models for the accelerometer will be firstly derived, which can improve the accuracy of sensor data for the Zero velocity UPdaTe (ZUPT) by using the quaternion-based state vector and the accelerometer value as the measurement. Then, two filters are used to reduce the error accumulation in inertial navigation system (INS): with the data of accelerometer and gyroscope, the ESFIR filter is employed to provide the error estimation of the accelerometer, which is used to correct the accelerometer’s output directly, then, the finite impulse response (FIR) filter will be used in the Zero velocity UPdaTe (ZUPT) method. With the Gyroscope’s output and the ESFIR corrected accelerometer data, the human position can be computed. Finally, four real tests will be done to verify the performance of the proposed method. The actual test results show that the accuracy of the position information derived from the solution using this method is improved by 8.43% on average compared with the conventional Kalman-based method when the setting is friendly to KF. When the setting is bad for KF, the KF-based method has divergence, and the proposed method can still provide stable solution, it can improve the localization accuracy by about 11.81% when compared with the INS+ZUPT method, thus providing better robustness.

Purpose: We propose an FDK-based neural network to directly learn the filter from an iterative reconstruction (IR) algorithm and apply the learned filter in the FDK algorithm to obtain a high-quality CBCT reconstruction. Methods: The FDK algorithm is transformed into a linear expression of several matrix multipliers and embedded into neural network layers. Then, the FDK-based neural network framework is built including two fundamental modules and four core network layers. This network model can learn a filter directly from the iteratively reconstructed CBCT images by cascading the network layers of cosine weighting, filtering, backprojection, and leaky rectified linear unit and setting filter as the only trainable parameter. Preliminary and simulation studies performed on abdominal CT datasets are conducted to explore the correctness and effectiveness of the learned filter. Then, the head and neck CT data and Catphan phantom are utilized to demonstrate the generalization performance of the learned filter. Results: Preliminary study shows that the learned filter is consistent with the target filter, and the mean absolute difference is around 0.001. Compared with conventional FDK, the FDK-based neural network shows a better image quality with the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increasing by 67% and 6%, respectively. In terms of the line-pair slice in the Catphan phantom, the SSIM and PSNR are improved by 13.75% and 42.78%, respectively. Conclusions: The FDK-based neural network can reconstruct high-quality images by directly learning a filter from the label images and provides a new perspective on solving the time-consuming problem of IR methods.

Abstract Background Moyamoya disease (MMD) has attracted the attention of scholars because of its rarity and unknown etiology. Methods Data for this study were sourced from the Second Affiliated Hospital of Nanchang University. Regression analyses were conducted to examine the association in Lipoprotein [Lp(a)] and MMD. R and IBM SPSS were conducted. Results A cohort comprising 1012 MMD patients and 2024 controls was established through the propensity score matching method. Compared with controls, MMD patients showed higher median Lp(a) concentrations [18.5 (9.6–37.8) mg/dL vs. 14.9 (7.8–30.5) mg/dL, P

The Chinese species of Lilioceris are revised, and three new species are described from Tibet, China: Lilioceris zhentangensis Xu & Liang, sp. nov., Lilioceris medogensis Xu & Liang, sp. nov. and Lilioceris zayuensis Xu & Liang, sp. nov. Five species of Lilioceris are reported for China as new records: L. dromedarius (Baly, 1861), L. pulchella (Baly, 1859), L. semicostata (Jacoby, 1908), L. unicolor (Hope, 1831) and L. nepalensis Takizawa, 1989. Lilioceris seminigra (Jacoby, 1889) is proposed as a junior synonym of L. unicolor Hope, 1831. Redescriptions, habitus photographs, geographic distributions, host plants (if available) and habitats are provided for these species.

Abstract Background Hepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC. Methods The data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan–Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman’s rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2). Results We identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells. Conclusions The DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

Background: Osteoarthritis (OA) is a chronic degenerative joint disease that primarily affects middle-aged and elderly individuals. The decline in chondrocyte function plays a crucial role in the development of OA. Inflammasome-mediated chondrocyte pyroptosis is implicated in matrix degradation and cartilage degeneration in OA patients. Guanylate binding protein 5 (GBP5), a member of the GTPase family induced by Interferon-γ (IFN-γ), significantly influences cellular inflammatory responses, including intracellular inflammasome activation and cytokine release. However, the role of GBP5 in chondrocyte pyroptosis and OA progression remains unclear. Methods: In this study, we used tumor necrosis factor-α (TNF-α) to induce inflammation and created an OA mouse model with surgically-induced destabilization of the medial meniscus (DMM). We isolated and cultured primary chondrocytes from the knee joints of suckling C57 mice. TNF-α-stimulated primary chondrocytes served as an in vitro model for OA and underwent RNA sequencing. Chondrocytes were transfected with GBP5-overexpression plasmids and small interfering RNA and were subsequently treated with TNF-α. We assessed the expression of cartilage matrix components (COL2A1 and aggrecan), catabolic factors (MMP9 and MMP13), and NLRP3 inflammasome pathway genes (NLRP3, Caspase1, GSDMD, Pro-IL-1β, and Pro-Caspase1) using RT-qPCR and Western blotting. We analyzed the expression of GBP5, NLRP3, and Caspase1 in the cartilage of DMM-induced post-traumatic OA mice and human OA patients. Immunohistochemistry (IHC) was used to detect the expression of GBP5, NLRP3 and GSDMD in cartilage specimens from OA patients and mouse DMM models. Chondrocyte pyroptosis was assessed using flow cytometry, and the levels of interleukin-1β (IL-1β) and interleukin-18 (IL-18) were measured with ELISA. We conducted double luciferase reporter gene and chromatin immunoprecipitation (ChIP) assays to confirm the relationship between IRF1 and GBP5. Results: GBP5 expression increased in TNF-α-induced chondrocytes, as revealed by RNA sequencing. GBP5 inhibited COL2A1 and aggrecan expression while promoting the expression of MMP9, MMP13, NLRP3, Caspase1, GSDMD, Pro-IL-1β, and Pro-Caspase1. GBP5 expression also increased in the cartilage of DMM-induced post-traumatic OA mice and human OA patients. Knockout of GBP5 reduced chondrocyte injury in OA mice. GBP5 promoted chondrocyte pyroptosis and the production of IL-1β and IL-18. Additionally, we found that IRF1 bound to the promoter region of GBP5, enhancing its expression. After co-transfected with ad-IRF1 and siGBP5, the expression of pyroptosis-related genes was significantly decreased compared with ad-IRF1 group. Conclusions: The IRF1/GBP5 axis enhances extracellular matrix (ECM) degradation and promotes pyroptosis during OA development, through the NLRP3 inflammasome signaling pathway. The translational potential of this article: This study underscores the significance of the IRF1/GBP5 axis in NLRP3 inflammasome-mediated chondrocyte pyroptosis and osteoarthritic chondrocyte injury. Modulating IRF1 and GBP5 expression could serve as a novel therapeutic target for OA.

A major engineering challenge in construction projects lies in the stability assessment of rocky slopes with faults. The left bank slope of the Jinchuan Hydropower Station in the outlet field is notably influenced by the steeply inclined faults and rock joints, resulting in highly complex geological conditions and raising critical concerns regarding slope stability throughout construction and operation. The evolution mechanism of rock mass from fracture to deformation during slope excavation and unloading is determined using microseismic (MS) monitoring and numerical simulation. Results show that the MS activity can effectively reveal the influence of slope construction disturbance on geological structure damage and structural plane activation. Specifically, the g7 fault is a crucial factor influencing the failure mode of the left bank slope of the Jinchuan Hydropower Station in the outlet field. The comparison of monitoring data, numerical simulation, and in-situ failure demonstrates consistency between the deformation area of rock mass and the damage area defined by MS monitoring, confirming the accuracy of MS monitoring and numerical simulation results. Overall, the results can serve as a basis for the later-stage construction of the dam foundation slope at the Jinchuan Hydropower Station and provide guidance for the stability assessment of similar slope engineering projects.

Cadmium (Cd) is a highly toxic metal that is difficult to completely eliminate from soil, despite advancements in modern agricultural and environmental technologies that have successfully reduced Cd levels. However, rice remains a key source of Cd exposure for humans. Even small amounts of Cd absorbed by rice can pose a potential health risk to the human body. Laser-induced breakdown spectroscopy (LIBS) has the advantages of simple sample preparation and fast analysis, which, combined with the transfer learning method, is expected to realize the real-time and rapid detection of low-level heavy metals in rice. In this work, 21 groups of naturally matured rice samples from potentially Cd-contaminated environments were collected. These samples were processed into rice husk, brown rice, and polished rice groups, and the reference Cd content was measured by ICP-MS. The XGBoost algorithm, known for its excellent performance in handling high-dimensional data and nonlinear relationships, was applied to construct both the XGBoost base model and the XGBoost-based transfer learning model to predict Cd content in brown rice and polished rice. By pre-training on rice husk source data, the XGBoost-based transfer learning model can learn from the abundant information available in rice husk to improve Cd quantification in rice grain. For brown rice, the XGBoost base model achieved RC2 of 0.9852 and RP2 of 0.8778, which were improved to 0.9885 and 0.9743, respectively, with the XGBoost-based transfer learning model. In the case of polished rice, the base model achieved RC2 of 0.9838 and RP2 of 0.8683, while the transfer learning model enhanced these to 0.9883 and 0.9699, respectively. The results indicate that the transfer learning method not only improves the detection capability for low Cd content in rice but also provides new insights for food safety detection.

This study proposes a dual foot-mounted localisation scheme with a minimum-distance-constraint (MDC) Kalman filter (KF) for human localisation under coloured measurement noise (CMN). The dual foot-mounted localisation employs inertial measurement unit (IMUs), one on each foot, and is intended for human navigation. The KF under CMN (cKF) is then derived from the data-fusion model of the proposed navigation scheme. Finally, the MDC condition is designed and an MDC–cKF model is proposed to reduce the error in the IMUs. Empirical results showed that the proposed method effectively improves the navigation accuracy from that of MDC–KF, which neglects the effect of CMN.

To determine the pivotal microorganisms affecting intramuscular fat (IMF) accumulation in Min pigs and to discern the extent of the influence exerted by various intestinal segments on IMF-related traits, we sequenced 16S rRNA from the contents of six intestinal segments from a high IMF group (Group H) and a low IMF group (Group L) of Min pigs weighing 90 ± 1 kg. We then compared their diversity and disparities in bacterial genera. Group H exhibited considerably higher α diversity in the jejunum and colon than Group L (p < 0.05). When 95% confidence levels were considered, the main β diversity components for the ileum, caecum, and colon within Groups H and L exhibited absolute segregation. Accordingly, 31 differentially abundant genera across Group H were pinpointed via LEfSe and the Wilcoxon test (p < 0.05) and subsequently scrutinised based on their distribution and abundance across distinct intestinal segments and their correlation with IMF phenotypes. The abundances of Terrisporobacter, Acetitomaculum, Bacteroides, Fibrobacter, Treponema, Akkermansia, Blautia, Clostridium sensu stricto 1, Turicibacter, Subdoligranulum, the [Eubacterium] siraeum group, and dgA 11 gut groups were positively correlated with IMF content (p < 0.05), whereas those of Bacillus, the Lachnospiraceae NK4A136 group, Streptococcus, Roseburia, Solobacterium, Veillonella, Lactobacillus, the Rikenellaceae RC9 gut group, Anaerovibrio, and the Lachnospiraceae AC2044 group were negatively associated with IMF content (p < 0.05). Employing PICRUSt2 for predicting intergenic metabolic pathways that differ among intestinal microbial communities revealed that within the 95% confidence interval the colonic microbiome was enriched with the most metabolic pathways, including those related to lipid metabolism. The diversity results, bacterial genus distributions, and metabolic pathway disparities revealed the colonic segment as an influential region for IMF deposition.

Rana dybowskii is one of the important aquaculture species in Northeast China. The fallopian tubes of female R. dybowskii are used to prepare oviductus ranae (an important traditional Chinese medicine). Therefore, R. dybowskii females have higher economical value than males. An increasing female R. dybowskii population can increase the benefits from R. dybowskii culture. However, the genome of amphibians is complex, making it difficult to investigate their sex determination mechanism. In this study, we analyzed the genome of male R. dybowskii using next-generation sequencing technology. A total of 200,046,452,400 bp of clean data were obtained, and the K-mer analysis indicated that the depth was 50×. The genome size of R. dybowskii was approximately 3585.05 M, with a heterozygosity rate, repeat sequence ratio, and genome GC content of 1.15%, 68.96%, and approximately 43.0%, respectively. In total, 270,785 contigs and 498 scaffolds were generated. The size of the contigs and scaffolds was 3,748,543,415 and 3,765,862,278 bp, respectively, with the N50 length of 31,988 and 336,385,783. The longest contig and scaffold were of the size 137,967,485 and 1,808,367,828 bp, respectively. The number of contigs and scaffolds > 10K nt was 99,620 and 451, respectively. Through annotation, 40,913 genes were obtained, including 156,609 CDS (i.e., 3.83 CDS per gene). Sequence alignment was performed with the assembled scaffolding genome in this study. Two and one fragment had high homology with two male-specific DNA molecular markers of R. dybowskii discovered previously (namely, MSM-222 and MSM-261, respectively). In addition, the Dmrt1 gene of R. dybowskii was obtained with a length of 18,893 bp by comparison and splicing. The forward primers amplifying MSM-222 and MSM-261 were located at 322–343 and 14,501–14,526 bp of Dmrt1, respectively. However, sequence alignment revealed that MSM-222 and MSM-261 were not located on Dmrt1, and only some homologous parts were observed. This indicated that in addition to Dmrt1, other important genes may play a crucial role in the sex determination mechanism of R. dybowskii. Our study provided a foundation for the subsequent high-quality genome construction and provided important genomic resources for future studies on R. dybowskii.

ObjectiveThe development of surgical microscope-associated cameras has given rise to a new operating style embodied by hybrid microsurgical and exoscopic operative systems. These platforms utilize specialized camera systems to visualize cranial neuroanatomy at various depths. Our study aims to understand how different camera settings in a novel hybrid exoscope system influence image quality in the context of neurosurgical procedures.MethodsWe built an image database using captured cadaveric dissection images obtained with a prototype version of a hybrid (microsurgical/exoscopic) operative platform. We performed comprehensive 4K-resolution image capture using 76 camera settings across three magnification levels and two working distances. Computer algorithms such as structural similarity (SSIM) and mean squared error (MSE) were used to measure image distortion across different camera settings. We utilized a Laplacian filter to compute the overall sharpness of the acquired images. Additionally, a monocular depth estimation deep learning model was used to examine the image's capability to visualize the depth of deeper structures accurately.ResultsA total of 1,368 high-resolution pictures were captured. The SSIM index ranged from 0.63 to 0.85. The MSE was nearly zero for all image batches. It was determined that the exoscope could accurately detect both the sharpness and depth based on the Laplacian filter and depth maps, respectively. Our findings demonstrate that users can utilize the full range of camera settings available on the exoscope, including adjustments to aperture, color saturation, contrast, sharpness, and brilliance, without introducing significant image distortions relative to the standard mode.ConclusionThe evolution of the camera incorporated into a surgical microscope enables exoscopic visualization during cranial base surgery. Our result should encourage surgeons to take full advantage of the exoscope's extensive range of camera settings to match their personal preferences or specific clinical requirements of the surgical scenario. This places the exoscope as an invaluable asset in contemporary surgical practice, merging high-definition imaging with ergonomic design and adaptable operability.

ObjectivesConfocal laser endomicroscopy (CLE) is an intraoperative real-time cellular resolution imaging technology that images brain tumor histoarchitecture. Previously, we demonstrated that CLE images may be interpreted by neuropathologists to determine the presence of tumor infiltration at glioma margins. In this study, we assessed neurosurgeons’ ability to interpret CLE images from glioma margins and compared their assessments to those of neuropathologists.MethodsIn vivo CLE images acquired at the glioma margins that were previously reviewed by CLE-experienced neuropathologists were interpreted by four CLE-experienced neurosurgeons. A numerical scoring system from 0 to 5 and a dichotomous scoring system based on pathological features were used. Scores from assessments of hematoxylin and eosin (H&E)-stained sections and CLE images by neuropathologists from a previous study were used for comparison. Neurosurgeons’ scores were compared to the H&E findings. The inter-rater agreement and diagnostic performance based on neurosurgeons’ scores were calculated. The concordance between dichotomous and numerical scores was determined.ResultsIn all, 4275 images from 56 glioma margin regions of interest (ROIs) were included in the analysis. With the numerical scoring system, the inter-rater agreement for neurosurgeons interpreting CLE images was moderate for all ROIs (mean agreement, 61%), which was significantly better than the inter-rater agreement for the neuropathologists (mean agreement, 48%) (p < 0.01). The inter-rater agreement for neurosurgeons using the dichotomous scoring system was 83%. The concordance between the numerical and dichotomous scoring systems was 93%. The overall sensitivity, specificity, positive predictive value, and negative predictive value were 78%, 32%, 62%, and 50%, respectively, using the numerical scoring system and 80%, 27%, 61%, and 48%, respectively, using the dichotomous scoring system. No statistically significant differences in diagnostic performance were found between the neurosurgeons and neuropathologists.ConclusionNeurosurgeons’ performance in interpreting CLE images was comparable to that of neuropathologists. These results suggest that CLE could be used as an intraoperative guidance tool with neurosurgeons interpreting the images with or without assistance of the neuropathologists. The dichotomous scoring system is robust yet simple and may streamline rapid, simultaneous interpretation of CLE images during imaging.

ObjectivesCardiogenic cerebral embolism (CCE) poses a significant health risk; however, there is a dearth of published prognostic prediction models addressing this issue. Our objective is to establish prognostic prediction models (PM) for predicting poor functional outcomes at 3 months in patients with acute CCE associated with non-valvular atrial fibrillation (NVAF) and perform both internal and external validations.MethodsWe included a total of 730 CCE patients in the development cohort. The external regional validation cohort comprised 118 patients, while the external time-sequential validation cohort included 63 patients. Multiple imputation by chained equations (MICE) was utilized to address missing values and the least absolute shrink and selection operator (LASSO) regression was implemented through the glmnet package, to screen variables.ResultsThe 3-month prediction model for poor functional outcomes, denoted as N-ABCD2, was established using the following variables: NIHSS score at admission (N), Age (A), Brain natriuretic peptide (BNP), C-reactive protein (CRP), D-dimer polymers (D), and discharge with antithrombotic medication (D). The model’s Akaike information criterion (AIC) was 637.98, and the area under Curve (AUC) for the development cohort, external regional, and time-sequential cohorts were 0.878 (95% CI, 0.854–0.902), 0.918 (95% CI, 0.857–0.979), and 0.839 (95% CI, 0.744–0.934), respectively.ConclusionThe N-ABCD2 model can accurately predict poor outcomes at 3 months for CCE patients with NVAF, demonstrating strong prediction abilities. Moreover, the model relies on objective variables that are readily obtainable in clinical practice, enhancing its convenience and applicability in clinical settings.

The whole-genome sequence of an African swine fever virus (ASFV) strain (HuB/HH/2019) isolated from Hubei, China, was highly similar to that of the Georgia 2007/1 strain ASFV. After infection with strong strains, domestic pigs show typical symptoms of infection, including fever, depression, reddening of the skin, hemorrhagic swelling of various tissues, and dysfunction. The earliest detoxification occurred in pharyngeal swabs at 4 days post-infection. The viral load in the blood was extremely high, and ASFV was detected in multiple tissues, with the highest viral loads in the spleen and lungs. An imbalance between pro- and anti-inflammatory factors in the serum leads to an excessive inflammatory response in the body. Immune factor expression is suppressed without effectively eliciting an immune defense. Antibodies against p30 were not detected in acutely dead domestic pigs. Sequencing of the peripheral blood mononuclear cell transcriptome revealed elevated transcription of genes associated with immunity, defense, and stress. The massive reduction in lymphocyte counts in the blood collapses the body’s immune system. An excessive inflammatory response with a massive reduction in the lymphocyte count may be an important cause of mortality in domestic pigs. These two reasons have inspired researchers to reduce excessive inflammatory responses and stimulate effective immune responses for future vaccine development.

Background In medical research, the effectiveness of machine learning algorithms depends heavily on the accuracy of labeled data. This study aimed to assess inter-rater reliability (IRR) in a retrospective electronic medical chart review to create high quality labeled data on comorbidities and adverse events (AEs).Methods Six registered nurses with diverse clinical backgrounds reviewed patient charts, extracted data on 20 predefined comorbidities and 18 AEs. All reviewers underwent four iterative rounds of training aimed to enhance accuracy and foster consensus. Periodic monitoring was conducted at the beginning, middle, and end of the testing phase to ensure data quality. Weighted Kappa coefficients were calculated with their associated 95% confidence intervals (CIs).Results Seventy patient charts were reviewed. The overall agreement, measured by Conger's Kappa, was 0.80 (95% CI: 0.78-0.82). IRR scores remained consistently high (ranging from 0.70 to 0.87) throughout each phase.Conclusion Our study suggests the detailed manual for chart review and structured training regimen resulted in a consistently high level of agreement among our reviewers during the chart review process. This establishes a robust foundation for generating high-quality labeled data, thereby enhancing the potential for developing accurate machine learning algorithms.