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Background: Nephrotic syndrome (NS) is a condition often necessitating continuous kidney replacement therapy (CKRT) due to severe edema and other complications. Anticoagulation is critical in CKRT to prevent filter clotting, with regional citrate anticoagulation (RCA) being the first-line method. However, the hypercoagulable state of NS may require alternative strategies. Optimal anticoagulation therapy in NS patients undergoing CKRT is lacking.

Methods: This retrospective observational study included 251 CKRT sessions from 122 NS patients treated at Nanfang Hospital, Southern Medical University, from January 2019 to December 2022. Patients were divided into three groups based on anticoagulation method: Low-molecular-weight-heparin (LMWH) alone, RCA alone, and RCA plus LMWH. Filter lifespan, incidence of filter clotting, and adverse events were assessed to evaluate the efficacy and safety profiles of each anticoagulation methods.

Results: The combination of RCA and LMWH demonstrated a significantly longer mean filter lifespan and lower incidence of filter clotting compared to LMWH or RCA alone. RCA plus LMWH also showed a lower incidence of overall adverse events, particularly thrombosis, without an increase in bleeding complications. Multivariate Cox analysis indicated that RCA plus LMWH was particularly effective in patients with normal kidney function.

Conclusions: RCA combined with LMWH provides a superior anticoagulation strategy in NS patients undergoing CKRT, with enhanced filter lifespan and reduced clotting and thrombotic events without increasing bleeding risk. Further research is needed to optimize dosing and validate these results in broader populations.

Compared to single-drug therapy, combination therapy involves the use of two or more drugs to reduce drug dosage, decrease drug toxicity, and improve treatment efficacy. We developed an extreme gradient boosting (XGBoost)-based drug-drug cell line prediction model (XDDC) to predict synergistic drug combinations. XDDC was based on XGBoost and used one of the largest drug combination datasets, NCI-ALMANAC. In XDDC, drug chemical structures, adverse drug reactions, and target information were selected as drug features; gene expression, methylation, mutations, copy number variations, and RNA interference data were used as cell line features; and pathway information was incorporated to link drug features and cell line features. XDDC improved the interpretability of drug combination features and outperformed other machine learning methods. It achieved an area under the curve (AUC) of 0.966 ± 0.002 and an AUPR of 0.957 ± 0.002 when cross-validated on NCI-ALMANAC data. Different types of omics data were evaluated and compared in the model. Literature and experimental verification confirmed some of our predictions. XDDC could help medical professionals to rapidly screen synergistic drug combinations against specific cancer cell lines.

Background: Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD.

Methods: A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC).

Results: Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients.

Conclusion: The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction.

Background: Muscle hypertrophy may be influenced by biological differences between males and females. This meta-analysis investigated absolute and relative changes in muscle size following resistance training (RT) between males and females and whether measures of muscle size, body region assessed, muscle fibre type, and RT experience moderate the results.

Methods: Studies were included if male and female participants were healthy (18-45 years old) adults that completed the same RT intervention, and a measure of pre- to post-intervention changes in muscle size was included. Out of 2,720 screened studies, 29 studies were included in the statistical analysis. Bayesian methods were used to estimate a standardised mean difference (SMD), log response ratio (lnRR) with exponentiated percentage change (Exp. % Change of lnRR), and probability of direction (pd) for each outcome.

Results: Absolute increases in muscle size slightly favoured males compared to females (SMD = 0.19 (95% HDI: 0.11 to 0.28); pd = 100%), however, relative increases in muscle size were similar between sexes (Exp. % Change of lnRR = 0.69% (95% HDI: -1.50% to 2.88%)). Outcomes were minimally influenced by the measure of muscle size and not influenced by RT experience of participants. Absolute hypertrophy of upper-body but not lower-body regions was favoured in males. Type I muscle fibre hypertrophy slightly favoured males, but Type II muscle fibre hypertrophy was similar between sexes.

Conclusion: Our findings strengthen the understanding that females have a similar potential to induce muscle hypertrophy as males (particularly when considering relative increases in muscle size from baseline) and findings of our secondary analyses should inform future research that investigates sex differences in highly trained participants and muscle fibre type-specific hypertrophy.

Background: Antepartum hemorrhage (APH) is associated with perinatal mortality and maternal morbidity. Previous studies have reported that obesity in pregnancy adversely influences both fetal and neonatal outcomes. This study aimed to investigate gestational overweight and the risk of APH in pregnant women with complete placenta previa (CPP).

Methods: This was a retrospective cohort study of pregnant women with CPP delivery at our hospital from 2013 to 2015. Outcomes were stratified according to APH and non-APH.

Results: Of 193 pregnancies with CPP, 40.4% (78) were diagnosed with APH. Maternal weight and BMI at delivery were significantly decreased in women with APH (61.15 ± 8.73 vs. 65.22 ± 7.80, 24.47 ± 3.12 vs. 26.21 ± 2.85; P = 0.001, P = 0.001; respectively), and the prevalence of overweight at delivery was higher in the non-APH group compared to those in the APH group (54.9% (62) vs. 39.7% (27); OR 2.18; 95%CI [1.16-4.11]). After adjusting for gestation week and other potential confounding factors, maternal weight and BMI were associated with the APH (OR 0.95, 95%CI [0.91-0.99]; 0.85, 95%CI [0.75-0.97], respectively).

Conclusion: Appropriate weight gain during pregnancy may decrease the risk of antepartum hemorrhage in pregnant women with complete placenta previa.

Sipuncula, specifically the family Aspidosiphonidae, faces taxonomic challenges due to brief original descriptions and the poor condition or loss of the type material. Detailed standardized redescriptions are essential to understanding the diversification in this group. Herein, a comprehensive redescription of Aspidosiphon (Paraspidosiphon) steenstrupii based on an extensive material collection from the tropical Western Atlantic is provided. Based on morphological data and the analysis of COI sequences, we delimited A. (P.) steenstrupii morphologically, restricting its distribution to the tropical Western Atlantic. Also, the redescriptions and proposals for reinstatement of A. (P.) exostomum, A. (P.) ochrus, and A. (P.) speculator, previously considered junior synonyms of A. (P.) steenstrupii, are included. Furthermore, a comprehensive discussion on diagnostic morphological features to recognize aspidosiphonid species and a detailed revision of synonyms of A. (P.) steenstrupii are included. Notable differences in morphology and genetic data suggest the need for revising the taxonomic status of several synonyms within the family, highlighting underestimated diversity in sipunculans.

This study analyzed the loss of heterozygosity (LOH) effect on gastric cancer (GC) tumor samples from 21 Mexican patients, including diffuse (DGC) and intestinal (IGC) subtypes, as well as non-atrophic gastritis (NAG, control). Whole-genome high-density arrays were performed, and LOH regions were identified among the tissue samples. The differences in affected chromosomes were established among groups, with chromosomes 6 and 8 primarily affected in DGC and chromosomes 3, 16, and 17 in IGC. Functional pathway analysis revealed involvement in cancer-associated processes, such as signal transduction, immune response, and cellular metabolism. Five LOH-genes (IRAK1, IKBKG, PAK3, TKTL1, PRPS1) shared between GC and NAG suggest an early role in carcinogenesis. Specific genes were highlighted for Hallmarks of Cancer NAG-related genes (PTPRJ and NDUFS) were linked to cell proliferation and growth; IGC genes (GNAI2, RHOA, MAPKAPK3, MST1R) to genomic instability, metastasis, and arrest of cell death; and DGC genes to energy metabolism and immune evasion. These findings emphasize the role of LOH in GC pathogenesis and underscore the need for further research to understand LOH-affected genes and their diagnostic or evolution potential in cancer management. Portions of this text were previously published as part of a preprint (https://www.medrxiv.org/content/10.1101/2024.07.29.24311063v1).

Supplementation of the growing substrate has been reported to enhance the production of cultivated mushrooms; however, supplementation using nano-sized additives is not yet investigated on Shiitake (Lentinula edodes). The study investigated the potential of a nano-supplement (Lithovit®-Amino25) containing an admixture of 25% L-amino acids on shiitake cultivated on sawdust from locally available oak, maple, and apple trees (oak sawdust: OS, maple sawdust: MS, and apple sawdust: AS). Initially, sawdusts were enriched equally with wheat bran (WB) and at the first soaking, Lithovit®-Amino25 was applied at 5 g/L. Treatments were: T1: OS-WB (control), T2: OS-WB+ nano-amino, T3: MS-WB, T4: MS-WB+ nano-amino, T5: AS-WB, and T6: AS-WB+ nano-amino. Among non-supplemented substrates, complete mycelia run, fruiting, and harvest dates were faster in T1 than in T3 and T5. Complete mycelial development was delayed by 7, 5, 9, and 6 d in T3, T4, T5, and T6 compared to T1. The harvest date was delayed by 7.7-8.3 d on maple sawdust and by 10.5-12.7 d on apple sawdust compared to oak sawdust. However, nano-supplementation hastened fruiting and harvest dates (by 9.3 d) in T4 compared to T3. The biological yield of the second harvest was higher on supplemented than on non-supplemented maple and apple sawdusts. Only T1 and T2 showed consistency in production over two consecutive harvests. Nano-supplementation improved the total biological yield in T2, T4, and T6 by 9.8, 21.0, and 22.5%, respectively. Nevertheless, all treatments, except T4, had lower biological efficiencies compared with T1. In T4, results of stepwise regression showed a strong positive correlation (R² = 0.96) between the total biological yield and mushroom weight at the second harvest. Supplementation caused a slight or significant improvement in pileus diameter and mushroom firmness and a significant improvement in mushroom's crude protein (by 2.9-8.2% compared to T1) and fiber contents (by 1-2.3% compared to T1). In conclusion, supplemented maple sawdust would alternate oak sawdust for shiitake production, though other timings of supplementation might be further investigated to optimize production on this substrate.

N6-methyladenosine (m6A) represents one of the most prevalent methylation modifications in eukaryotes and it is crucial to accurately identify its modification sites on RNA sequences. Traditional machine learning based approaches to m6A modification site identification primarily focus on RNA sequence data but often incorporate additional biological domain knowledge and rely on manually crafted features. These methods typically overlook the structural insights inherent in RNA sequences. To address this limitation, we propose M6A-SAI, an advanced predictor for RNA m6A modifications. M6A-SAI leverages a transformer-based deep learning framework to integrate structure-aware insights into sequence representation learning, thereby enhancing the precision of m6A modification site identification. The core innovation of M6A-SAI lies in its ability to incorporate structural information through a multi-step process: initially, the model utilizes a Transformer encoder to learn RNA sequence representations. It then constructs a similarity graph based on Manhattan distance to capture sequence correlations. To address the limitations of the smooth similarity graph, M6A-SAI integrates a structure-aware optimization block, which refines the graph by defining anchor sets and generating an awareness graph through PageRank. Following this, M6A-SAI employs a self-correlation fusion graph convolution framework to merge information from both the similarity and awareness graphs, thus producing enriched sequence representations. Finally, a support vector machine is utilized for classifying these representations. Experimental results validate that M6A-SAI substantially improves the recognition of m6A modification sites by incorporating structure-aware insights, demonstrating its efficacy as a robust method for identifying RNA m6A modification sites.