Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports.

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.

Longitudinal associations of dietary fiber and its source with 48-week weight loss maintenance, cardiometabolic risk factors and glycemic status under metformin or acarbose treatment: a secondary analysis of the March randomized trial.

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 ß-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.

Construction of cytochrome P450 3A and P-glycoprotein knockout rats with application in rivaroxaban-verapamil interactions.

Cytochrome P450 3A (CYP3A) and P-glycoprotein (P-gp), as important metabolic enzymes and transporters, participate in the biological transformation and transport of many substances in the body. CYP3A and P-gp are closely related, with very high substrate overlap and regulation similarity, making it particularly difficult to investigate the function of one or the other individually in vivo. Rivaroxaban and verapamil are commonly used together to treat nonvalvular atrial fibrillation in clinical practice. However, this combination therapy can increase systemic exposure to rivaroxaban and the risk of major bleeding and intracranial hemorrhage. In this study, Cyp3a1/2 and Mdr1a/b quadruple gene knockout (qKO) rat model was generated and characterized for the first time. CYP3A1/2 and P-gp are completely absent in this novel rat model. Then, the qKO rat model was applied for the evaluation of the drug-drug interactions (DDI) between rivaroxaban and verapamil. The results demonstrated that CYP3A and P-gp were jointly and selectively involved in the pharmacokinetic interactions between rivaroxaban and verapamil. This study may provide useful information for understanding the role of CYP3A and P-gp in rivaroxaban-verapamil therapy and predicting the potential interaction between CYP3A and P-gp.

Measured and predicted floc size of cohesive sediment in the presence of microalgae.

In aquatic environments, biological factors significantly influence the flocculation process of cohesive sediments, thereby impacting sediment transport dynamics. Due to its complexity, the mechanism of biological flocculation still remains unknown. Here, we conducted laboratory experiments to investigate how living microalgae (Skeletonema costatum) affects the flocculation of mineral clay under various shear rates and suspended sediment concentrations (SSC) in saline water. The microalgae (Skeletonema costatum) and SSC both have positive influences on the increase in floc size. However, the shear rate (G) shows dual effect. Specifically, there exists a critical shear rate, G*, at which the floc size increases with G when G≤G* and decreases with G when G>G*. More importantly, G*is affected by SSC and exhibits no dependence on microalgae content. The microalgae (Skeletonema costatum) has a dominant effect on both floc shape and floc size of microalgae-mineral flocs compared to shear rate under the present experimental conditions (SSC: 700 mg/L, chlorophyll-a concentration: 0∼13.76 µg/L, shear rate: 10∼90 s-1). Additionally, the elongated-rod flocs are more easily formed in microalgae-mineral clay suspensions, whereas the plate-stacked flocs are more abundant in pure mineral clay suspensions. The promoting effect of microalgae is obvious under low shear rate conditions (G≤40 s-1), while at high shear rate (G>40 s-1), this effect is significantly attenuated, with a reduction by nearly half. Finally, a new bioflocculation model was proposed to predict the equilibrium median floc size for both conditions with and without microalgae. The model was well validated through comparisons with laboratory measurements.

Rubisco supplies pyruvate for the 2-C-methyl-D-erythritol-4-phosphate pathway.

RIBULOSE-1,5-BISPHOSPHATE CARBOXYLASE/OXYGENASE (Rubisco) produces pyruvate in the chloroplast through ß-elimination of the aci-carbanion intermediate1. Here we show that this side reaction supplies pyruvate for isoprenoid, fatty acid and branched-chain amino acid biosynthesis in photosynthetically active tissue. 13C labelling studies of intact Arabidopsis plants demonstrate that the total carbon commitment to pyruvate is too large for phosphoenolpyruvate to serve as a precursor. Low oxygen stimulates Rubisco carboxylase activity and increases pyruvate production and flux through the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway, which supplies the precursors for plastidic isoprenoid biosynthesis2,3. Metabolome analysis of mutants defective in phosphoenolpyruvate or pyruvate import and biochemical characterization of isolated chloroplasts further support Rubisco as the main source of pyruvate in chloroplasts. Seedlings incorporated exogenous,13C-labelled pyruvate into MEP pathway intermediates, while adult plants did not, underscoring the developmental transition in pyruvate sourcing. Rubisco ß-elimination leading to pyruvate constituted 0.7% of the product profile in in vitro assays, which translates to 2% of the total carbon leaving the Calvin-Benson-Bassham cycle. These insights solve the "pyruvate paradox"4, improve the fit of metabolic models for central metabolism and connect the MEP pathway directly to carbon assimilation.

Construction of Ion-Imprinted Graphene Oxide Mixed-Matrix Membranes for Selective Adsorption and Separation of Tm3.

Efficient adsorption and separation of rare earth from other similar rare earth wastewater has become an urgent demand for resource utilization of ion-type rare earth minerals in China. Herein, thulium (Tm) ion-imprinted graphene oxide (GO)-doped polyether sulfone (PES) membranes (GO-TII/PES-2 membranes) were prepared, in which ion-imprinted graphene oxide was applied as an efficient Tm3+ ionic ligand in the imprinted layer and polyether sulfone was applied as a carrier in the membrane matrix to achieve the selective adsorption and separation of Tm3+ and neighboring rare earth ions. Combined with an ion rectifier, the separation and purification performances of Tm3+ were explored. The separation factors ß(Tm3+/Tb3+), ß(Tm3+/Sm3+), ß(Tm3+/Nd3+), and ß(Tm3+/Ce3+) in the dynamic adsorption process increased significantly from 1.22, 1.04, 1.04, and 1.02 for nonimprinting to 3.07, 3.91, 3.91, and 3.33 for imprinted membranes. The GO-TII/PES-2 membrane adsorbed about three times more Tm3+ than the nonionic-imprinted (GO-NII/PES) membrane by adding a color developer and quantifying Tm3+ based on a fast and easy UV-photometric method. After eight dynamic permeations, the adsorption of Tm3+ by the GO-TII/PES-2 membrane decreased by only 13%, indicating that the membrane has good reuse performance. Additionally, the investigation examined the influence of Tm3+ on wheat seed germination, underscoring its potential application in agriculture and the importance of adsorbing and separating rare earth ions.

5-Hydroxymethylcytosine in circulating cell-free DNA as a potential diagnostic biomarker for SLE.

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.

A deep learning-based dose calculation method for volumetric modulated arc therapy.

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.

Super multiple primary lung cancers harbor high-frequency BRAF and low-frequency EGFR mutations in the MAPK pathway.

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.

An automated commissioning method based on virtual source models: Customizing Monte Carlo dose verification models for individual accelerators.

BACKGROUND: In pursuit of precise dose calculation and verification, the importance of beam modelling cannot be overstated, as it ensures an accurate distribution of particles incident upon the human body. The virtual source model, as one of the beam modelling methods, offers the advantage of not requiring detailed accelerator information. Although various virtual source models exist, manual adjustment to these models demands a substantial investment of time and computational resources. There has long been a desire to develop an efficient and automated approach for model commissioning. PURPOSE: To develop an automatic commissioning method for the virtual source model to customize the accelerator model for independent Monte Carlo dose verification. METHODS: Initially, the accelerator model is established using the virtual source model and self-developed Jaw and MLC models. Then, a fully automated iteration process is employed to adjust the parameters of the virtual source model. Three types of objective functions are designed to represent differences from water tank measurements. Each objective function is paired with a specific parameter for adjustment, and their effectiveness is demonstrated through physical evidence. In each iteration, parameters with the highest objective function percentage are chosen for adjustment, and step length is determined based on current objective function values. Iteration is terminated when changes in any direction from the optimal solution no longer produce an improvement. Dose verification model for nine accelerators has been accomplished using this method. Additionally, under the same initial conditions, verification models for Versa HD accelerator (FF and FFF modes) are established using this method, Nelder-Mead Simplex optimization method, and the Bayesian optimization method to compare the efficiency and quality of these three iterative approaches. RESULTS: Iterations for all nine accelerators are completed within 30 iterations. The relative dose differences in dose fall-off region compared to water tank measurements are all less than 2%, and the average gamma passing rates (3%/2 mm) for ArcCHECK measurements in QA plans are all higher than 97%. For Versa HD accelerator in FFF and FF modes, the proposed method achieves an average relative dose difference below 1% within 11 and 13 iterations, respectively. In contrast, the Simplex optimization reached 1% within 78 iterations in FFF mode. Furthermore, the Simplex optimization in FF mode and Bayesian optimization in both modes failed to achieve a 1% difference within 100 iterations. CONCLUSIONS: The proposed iterative method achieves fast and automated commissioning of dose verification models, contributing to accurate and reliable clinical dose verification.

Intraoperative confocal laser endomicroscopy during 5-aminolevulinic acid-guided glioma surgery: significant considerations for resection at the tumor margin.

OBJECTIVE: Because gliomas have poorly defined tumor margins, the ability to achieve maximal resection is limited. To better discern these margins, fluorescence-guided surgery has been used to aid maximal safe resection. The authors describe their experience with the simultaneous use of intraoperative fluorescein sodium (FNa) confocal laser endomicroscopy (CLE) and operating microscope 5-aminolevulinic acid (5-ALA) fluorescence imaging for glioma resection to improve CLE use for better margin discrimination. METHODS: FNa CLE and 5-ALA wide-field imaging were used in 33 patients with gliomas. CLE imaging was enhanced with the use of a telesurgical pathology software platform that enables real-time conversation between the operating neurosurgeons and the pathologists located remotely. CLE was used for imaging tumor regions that were subjectively regarded as tumor margins under normal visualization with the operative microscope. After FNa CLE imaging, 5-ALA wide-field imaging was performed in the same regions. Tissue was biopsied at imaging locations, and interpretations of FNa CLE and 5-ALA wide-field imaging were compared to those of permanent histological sections. RESULTS: Eighty-eight deep- and superficial-margin regions of interest (ROIs) were imaged with FNa CLE and 5-ALA imaging. Most of the ROIs interpreted by the neuropathologist as infiltrative glioma based on FNa CLE imaging lacked 5-ALA-induced fluorescence. Permanent histological sections from the corresponding regions were concordant with the interpretation of FNa CLE images in 57 of 88 (65%) ROIs and with the interpretation of 5-ALA imaging in 43 of 88 (49%) ROIs. The sensitivity and specificity of FNa CLE for the interpretation of tumor margins were 73% and 41%, respectively, and those of 5-ALA were 38% and 82%, respectively. Positive and negative predictive values for CLE were 79% and 33%, respectively, and those for 5-ALA were 86% and 31%, respectively. CONCLUSIONS: Conventional intraoperative evaluation of tumor margins, based on MRI and wide-field fluorescence imaging, can underestimate the invasiveness of gliomas. FNa CLE showed higher accuracy in detecting regions with infiltrating tumors than intraoperative 5-ALA imaging. Future considerations should include more rigorous comparisons of FNa CLE imaging and 5-ALA-guided resections on a larger cohort of patients.

Dose escalation in radical radio(chemo)therapy for cervical and upper thoracic esophageal cancer with 3DCRT/IMRT (ChC&UES): a multicenter retrospective study.

BACKGROUND: Cervical and upper thoracic esophageal cancer (ESCA) presents treatment challenges due to limited clinical evidence. This multi-center study (ChC&UES) explores radical radio(chemo)therapy efficacy and safety, especially focusing on radiation dose. METHOD: We retrospectively analyzed clinical data from 1,422 cases across 8 medical centers. According to the radiation dose for primary gross tumor, patients were divided into standard dose radiotherapy (SD, 50-55 Gy) or high dose (HD, > 55 Gy) radiotherapy. HD was further subdivided into conventional- high-dose group (HD-conventional, 55-63 Gy) and ultra-high-dose group (HD-ultra, ≥ 63 Gy). Primary outcome was Overall Survival (OS). RESULTS: The median OS was 33.0 months (95% CI: 29.401-36.521) in the whole cohort. Compared with SD, HD shown significant improved survival in cervical ESCA in Kaplan-Meier (P = 0.029) and cox multivariate regression analysis (P = 0.024) while shown comparable survival in upper thoracic ESCA (P = 0.735). No significant difference existed between HD-conventional and HD-ultra in cervical (P = 0.976) and upper thoracic (P = 0.610) ESCA. Incidences of radiation esophagitis and pneumonia from HD were comparable to SD (P = 0.097, 0.240), while myosuppression risk was higher(P = 0.039). The Bonferroni method revealed that, for both cervical and upper thoracic ESCA, HD-ultra enhance the objective response rate (ORR) compared to SD (P < 0.05). CONCLUSION: HD radiotherapy benefits cervical but not upper thoracic ESCA, while increasing bone marrow suppression risk. Further dose escalating (≥ 63 Gy) doesn't improve survival but enhances ORR.

trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning.

：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.

Deep learning detection of hand motion during microvascular anastomosis simulations performed by expert cerebrovascular neurosurgeons.

OBJECTIVE: Deep learning enables precise hand tracking without the need for physical sensors, allowing for unsupervised quantitative evaluation of surgical motion and tasks. We quantitatively assessed the hand motions of experienced cerebrovascular neurosurgeons during simulated microvascular anastomosis using deep learning. We explored the extent to which surgical motion data differed among experts. METHODS: A deep learning detection system tracked 21 landmarks corresponding to digit joints and the wrist on each hand of 5 expert cerebrovascular neurosurgeons. Tracking data for each surgeon was analyzed over long and short time intervals to examine gross movements and micromovements, respectively. Quantitative algorithms assessed the economy and flow of motion by calculating mean movement distances from the baseline median landmark coordinates and median times between sutures, respectively. RESULTS: Tracking data correlated with specific surgical actions observed in microanastomosis video analysis. Economy of motion during suturing was calculated as 19, 26, 29, 27, and 28 pixels for surgeons 1, 2, 3, 4, and 5, respectively. Flow of motion during microanastomosis was 31.96, 29.40, 28.90, 7.37, and 47.21 secs for surgeons 1, 2, 3, 4, and 5, respectively. CONCLUSIONS: Hand tracking data showed similarities among experts, with low movements from baseline, minimal excess motion, and rhythmic suturing patterns. The data revealed unique patterns related to each expert's habits and techniques. The results showed that surgical motion can be correlated with hand motion and assessed using mathematical algorithms. We also demonstrated the feasibility and potential of deep learning-based motion detection to enhance surgical training.

Exploring the behavioral intentions of PICC-related thrombosis prevention in breast cancer patients undergoing chemotherapy: a qualitative study based on theory of planned behavior.

PURPOSE: To explore the behavioral intention of breast cancer patients undergoing chemotherapy to prevent PICC-related thrombosis based on the theory of planned behavior (TPB). METHODS: This qualitative study employed purposive sampling and conducted semi-structured interviews with 14 breast cancer patients undergoing chemotherapy in the outpatient chemotherapy ward of a tertiary A-level comprehensive hospital in Beijing from July to August 2023. Data were analyzed using Colaizzi's descriptive analysis framework. RESULTS: Data analysis identified 10 themes that were derived from 4 aspects. Regarding behavioral attitude, three themes were condensed: (1) Considering the benefits of preventive measures, (2) Simple and easy preventive measures, and (3) Underestimating the importance of PICC-related thrombosis prophylaxis. Subjective norms yielded two main themes and five sub-themes: (1) Support from those close to the patient motivates adherence to prophylaxis (support from the patient's family, healthcare professionals, and other patients) and (2) Patients are influenced by personal factors to form an internal driving force (physical symptoms, fear of PICC-related thrombosis). Regarding perceived behavioral control, three main themes and four sub-themes were extracted: (1) Obstacles before actual prevention exercise (prevention information, hard-to-remember information), (2) Forgetfulness is the main obstacle factor, and (3) Wanting to overcome barriers to adhere to regular prevention (confidence to overcome obstacles, hope to get support). CONCLUSIONS: The impediments and facilitators identified in this study may provide a scientific foundation for subsequent targeted non-pharmacological preventive interventions for PICC-related thrombosis based on TPB in breast cancer patients undergoing chemotherapy. Special interventions should be designed for the patients in three areas: the patients themselves, the supporters around the patient, and the healthcare professionals.

Correlation analysis of cofilin-1 with renal prognosis in primary IgA nephropathy.

PURPOSE: The purpose of this study was to investigate the correlation between podocyte related biomarker cofilin-1 and renal function, and explore the value of cofilin-1 in predicting the risk of renal adverse prognosis in IgA nephropathy (IgAN). METHODS: Patients with primary IgAN diagnosed by initial renal biopsy performed in our hospital from January 2019 to February 2022 were included. This study was a prospective cohort study. All IgAN patients were detected the expression of cofilin-1 and other related biomarkers (RhoA, NGAL) in urine by enzyme-linked immunosorbent assay (ELISA) and follow-up at least 6 months. We also collected baseline clinicopathologial data of IgAN. The decreased renal function group was defined as baseline eGFR < 60 ml/min/1.73m2. Logistic and Cox regression model were used to analyze the correlation among cofilin-1 and renal prognosis. RESULTS: 133 IgAN patients were included, with a male-to-female ratio of 1.25:1 and an age of 37.67 ± 13.78 years, as well as an average of eGFR was 71.63 (40.42,109.33) ml/min/1.73m2. 56 patients (42.1%) had decreased renal function at baseline, with the average of eGFR was 34.07 (16.72, 49.21) ml/min/1.73 m2. 12 of which developed to renal adverse prognosis. The average of follow-up time was 22.035 ± 8.992 months. The multivariate regression analysis showed that increased urinary cofilin-1 was an independent risk factor associated with baseline renal function decline and renal adverse prognosis in IgAN patients (P < 0.05). ROC curves showed great efficacy of urinary cofilin-1 levels in diagnosing baseline renal function decline and predicting renal adverse prognosis (the area under the ROC curve was 0.708 and 0.803). CONCLUSION: Cofilin-1 as a novel biomarker of podocyte lesion is closely related to renal function decline in IgAN. Cofilin-1 has certain clinical value in predicting the risk of renal adverse prognosis. Podocyte fusion affects the renal prognosis of IgAN.

Genomic analysis of hypoxia-tolerant population of the Chinese mitten crab (Eriocheir sinensis).

Hypoxic stress, triggered by a multitude of factors, has inflicted significant economic repercussions on the aquaculture of Eriocheir sinensis. In this research, we sequenced a collective of 60 samples from both hypoxia-sensitive and hypoxia-resistant groups utilizing streamlined genome sequencing techniques. Subsequently, we delved into population evolution, scrutinized the selective sweep within these populations, and performed a genome-wide association study (GWAS) focused on the hypoxia tolerance traits within the population, all through the lens of SNPs molecular markers. This comprehensive analysis aimed to uncover the SNPs and pinpoint the pertinent candidate genes that influence the hypoxia tolerance capabilities of E. sinensis. The selective sweep analysis revealed that genes harboring potential genetic variations within the two populations were predominantly enriched in areas such as signaling molecules and interactions, energy metabolism, glycolipid metabolism, and immune response. In the genome-wide association study focusing on hypoxia tolerance traits, we identified four SNPs significantly associated with hypoxia resistance. Furthermore, one potential candidate gene, Dscam2, which is believed to influence hypoxia tolerance, was discovered within a 50 kb vicinity of these SNPs. These identified SNPs can serve as molecular markers for screening hypoxia tolerance, offering valuable insights for the genetic improvement of E. sinensis.

Aspirin increases estrogen levels in the placenta to prevent preeclampsia by regulating placental metabolism and transport function.

Preeclampsia is a unique multisystem progressive disease during pregnancy, which seriously endangers the health of pregnant women and fetuses. In clinical practice, aspirin is recommended for the prevention of preeclampsia, but the mechanism by which aspirin prevents preeclampsia has not yet been revealed. This report comprehensively evaluates the effects of aspirin on the expression and activity of placental metabolic enzymes and transporters. We found that after aspirin administration, only the expression of organic anion transporter 4 (OAT4) in the placenta showed a significant increase at both mRNA and protein levels, consistent with the results in JAR cells. Meanwhile, studies on the metabolic enzyme activity in the placenta showed a high upregulation of CYP19A1 activity. Subsequently, significant increases in endogenous substrates of OAT4 and CYP19A1 (dehydroepiandrosterone sulfate (DHEAS) and androstenedione) as well as estrone were detected in placental tissue. In summary, aspirin enhances the transport of DHEAS through OAT4 and promotes the metabolism of androstenedione through CYP19A1, thereby increasing estrogen levels in the placenta. This may be the mechanism by which aspirin prevents preeclampsia and maintains pregnancy by regulating the metabolism and transport function of the placenta.

Melatonin downregulates angiogenesis and lymphangiogenesis by regulating tumor-associated macrophages via NLRP3 inflammasomes in lung adenocarcinoma.

Tumor-associated macrophages (TAMs), present within the tumor microenvironment (TME), strictly modulate tumor angiogenesis and lymphangiogenesis. Nevertheless, the associated signaling networks and candidate drug targets for these events remains to be elucidated. Given its antioxidative activities, we speculated that melatonin may reduce pyroptosis, and thereby modulate both angiogenesis and lymphangiogenesis. We revealed that a co-culture of A549 cells and THP-1 macrophages strongly enhanced expressions of the NLRP3 inflammasome axis members, and augmented angiogenesis and lymphangiogenesis. Next, we overexpressed NLRP3 in the A549 cells, and demonstrated that excess NLRP3 expression substantially upregulated VEGF and CXCL cytokine expressions, and enhanced lymphatic endothelial cells (LECs) tube formation. In contrast, NLRP3 inhibition produced the opposite effect. In addition, relative to controls, melatonin administration strongly inhibited the NLRP3 inflammasome axis, as well as angiogenesis and lymphangiogenesis in the co-culture system. Subsequent animal experiments using a Lewis Lung Carcinoma (LLC) subcutaneous tumor model in mice corroborate these findings. Melatonin treatment and NLRP3 knockdown significantly inhibit tumor growth and downregulate NLRP3 and IL-1ß expression in tumor tissues. Furthermore, melatonin downregulates the expression of angiogenic and lymphangiogenic markers in tumor tissues. Taken together, the evidence suggested that a THP-1 macrophage and A549 cell co-culture stimulates angiogenesis and lymphangiogenesis via the NLRP3 axis. Melatonin protected against the TAMs- and NLRP3 axis-associated promotion of the aforementioned events in vitro and in vivo. Hence, melatonin is a promising candidate for managing for tumor-related angiogenesis and lymphangiogenesis in lung adenocarcinoma.