A comprehensive review of approaches for spatial domain recognition of spatial transcriptomes

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

In Silico Prediction of ERRα Agonists Based on Combined Features and Stacking Ensemble Method

Estrogen-related receptor α (ERRα) is considered a very promising target for treating metabolic diseases such as type 2 diabetes. Development of a prediction model to quickly identify potential ERRα agonists can significantly reduce the time spent on virtual screening. In this study, 298 ERRα agonists and numerous nonagonists were collected from various sources to build a new dataset of ERRα agonists. Then a total of 90 models were built using a combination of different algorithms, molecular characterization methods, and data sampling techniques. The consensus model with optimal performance was also validated on the test set (AUC=0.876, BA=0.816) and external validation set (AUC=0.867, BA=0.777) based on five selected baseline models. Furthermore, the model's applicability domain and privileged substructures were examined, and the feature importance was analyzed using the SHAP method to help interpret the model. Based on the above, it's hoped that our publicly accessible data, models, codes, and analytical techniques will prove valuable in quick screening and rational designing more novel and potent ERRα agonists.

Effect of the mRNA decapping enzyme scavenger (DCPS) inhibitor RG3039 on glioblastoma

BACKGROUND

Patients with glioblastoma (GBM) have a poor prognosis and limited treatment options. The mRNA decapping enzyme scavenger (DCPS) is a cap-hydrolyzing enzyme. The DCPS inhibitor RG3039 exhibited excellent central nervous system bioavailability in vivo and was safe and well tolerated in healthy volunteers in a phase 1 clinical trial. In this study, we investigated the expression of DCPS in GBM and the anti-tumor activity of RG3039 in various preclinical models of GBM.

METHODS

DCPS expression was examined in human GBM and paired peritumoral tissues. Its prognostic role was evaluated together with clinicopathological characteristics of patients. The anti-GBM effect of RG3039 was determined using GBM cell lines, patient-derived organoids, and orthotopic mouse models. The therapeutic mechanisms of DCPS inhibition were explored.

RESULTS

DCPS is overexpressed in GBM and is associated with poor survival of patients with GBM. The DCPS inhibitor RG3039 exhibited robust anti-GBM activities in GBM cell lines, patient-derived organoids and orthotopic mouse models, with drug exposure achievable in humans. Mechanistically, RG3039 downregulated STAT5B expression, thereby suppressing proliferation, survival and colony formation of GBM cells.

CONCLUSIONS

DCPS is a promising target for GBM. Inhibition of DCPS with RG3039 at doses achievable in humans downregulates STAT5B expression and reduces proliferation, survival and colony formation of GBM cells. Given the excellent anti-cancer activity and central nervous system bioavailability in vivo and good tolerance in humans, RG3039 warrants further study as a potential GBM therapy.

Differences in the Determination of Volatile Organic Compounds between Chrysanthemum morifolium Ramat. and Chrysanthemum indicum L. (Wild Chrysanthemum) by HS-GC-IMS

Chrysanthemums and wild chrysanthemums are herbs with high application value. As edible plants of the Asteraceae family, they have good antioxidant, anti-inflammatory and hepatoprotective properties. Chrysanthemums and wild chrysanthemums contain a wide variety of volatile organic compounds, and these volatile components are the main factors contributing to the flavor differences. Therefore, in this study, we investigated the volatile components of holland chrysanthemum from Bozhou, Anhui Province, Chu-chrysanthemum from Chuzhou, Anhui Province, Gong-chrysanthemums from Huangshan, Anhui Province, Huai-chrysanthemums from Jiaozuo, Henan Province, Hang-chrysanthemum from Hangzhou, Zhejiang Province, and wild chrysanthemum from Dabie Mountain by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) coupled with principal component analysis (PCA). The results showed that Chrysanthemum and wild chrysanthemum contain alcohols, esters, hydrocarbons, ketones, aldehydes, acids, camphor, pyrazines and furans. Among them, alcohols, esters and hydrocarbons accounted for more than 15%. It was hypothesized that 2-methyl-1-propanol, 2-methylbutanol, 1-hexanol in alcohols and hexyl acetate, 3-methylbutyl acetate and ethyl 2-methylpropanoate in esters might be the main reasons for the alcoholic and sweet flavors of chrysanthemum and chrysanthemum officinale. Based on the principal component analysis, cluster analysis with the Euclidean distance and similarity analysis of fingerprints, it was found that there were significant differences in the volatile components in chrysanthemums from different origins, among which the differences between Chu-chrysanthemum and Hang-chrysanthemum were the most significant. In addition, as a genus of wild chrysanthemum with the same species, it contains a richer variety of volatile organic compounds, and the content of hydrocarbons and alcohols is significantly higher than that of chrysanthemum.

MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks

Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.

Single-cell atlas profiling revealed cellular characteristics and dynamic changes after PD-1 blockade therapy of brain metastases from laryngeal squamous cell carcinoma

Brain metastasis (BM) in laryngeal squamous cell carcinoma (LSCC) is uncommon but prognosis is poor. Anti-PD-1 immunotherapy benefits some advanced LSCC cases, yet its efficiency is limited by tumor complexity. We analyzed paired metastatic tumor samples from before and after immunotherapy using single-cell RNA sequencing (scRNA-seq), along with a primary LSCC dataset and bulk RNA sequencing. This identified changes post-immunotherapy and revealed differences in single-cell transcriptomes among LSCC, primBM, and neoBM. Our findings show that anti-PD-1 treatment suppresses metastasis-promoting pathways like VEGF and EMT in cancer cells, and alters immune cell functions. Notably, it upregulates T cell activation, leading to CD8 T cell exhaustion from excess heat shock proteins, notably HSPA8. However, CD8 T cell cytotoxic functions improve post-treatment. In myeloid cells, anti-PD-1 therapy enhances antigen presentation and promotes a proinflammatory shift post-metastasis. Additionally, NUPR1 is linked to BM in LSCC, and NEAT1 is a potential metastatic cancer cell cycle participant. Our study provides insights into cancer heterogeneity and the impact of PD-1 immunotherapy on metastasis, aiding precise diagnosis and prognosis.

Hyb_SEnc: An Antituberculosis Peptide Predictor Based on a Hybrid Feature Vector and Stacked Ensemble Learning

Tuberculosis has plagued mankind since ancient times, and the struggle between humans and tuberculosis continues. Mycobacterium tuberculosis is the leading cause of tuberculosis, infecting nearly one-third of the world's population. The rise of peptide drugs has created a new direction in the treatment of tuberculosis. Therefore, for the treatment of tuberculosis, the prediction of anti-tuberculosis peptides is crucial.This paper proposes an anti-tuberculosis peptide prediction method based on hybrid features and stacked ensemble learning. First, a random forest (RF) and extremely randomized tree (ERT) are selected as first-level learning of stacked ensembles. Then, the five best-performing feature encoding methods are selected to obtain the hybrid feature vector, and then the decision tree and recursive feature elimination (DT-RFE) are used to refine the hybrid feature vector. After selection, the optimal feature subset is used as the input of the stacked ensemble model. At the same time, logistic regression (LR) is used as a stacked ensemble secondary learner to build the final stacked ensemble model Hyb_SEnc. The prediction accuracy of Hyb_SEnc achieved 94.68% and 95.74% on the independent test sets of AntiTb_MD and AntiTb_RD, respectively. In addition, we provide a user-friendly Web server (http://www.bioailab. com/Hyb_SEnc). The source code is freely available at https://github.com/fxh1001/Hyb_SEnc.

The powerful antioxidant effects of plant fruits, flowers, and leaves help to improve retinal damage and support the relief of visual fatigue

With the popularization of electronic products, visual fatigue is inevitably frequent. The causes of visual fatigue are varied, but from the perspective of physiological mechanisms, it is mainly closely related to retinal function or structural damage, especially the light source from various mobile devices and office equipments nowadays, which induces oxidative stress damage in the retina and exacerbates the degree of visual fatigue, resulting in the inability to use the eyes for a long period of time, pain in the eyes and periorbital area, blurred vision, dry eyes, tearing, and other discomforts. Food ingredients derived from natural plants have greater application in relieving visual fatigue. Therefore, this paper presents a detailed compilation of six plants that are widely used for their visual fatigue-relieving function, in the hope of providing more raw material choices for the development of products with visual fatigue-relieving functions in the future.

A Review: The Effect of Bovine Colostrum on Immunity in People of All Ages

Bovine colostrum provides newborn calves with strong passive immunity, which will further affect the immunity of their offspring. Compared with other commercial dairy products, bovine colostrum emphasizes the limit of aflatoxin M1, pathogenic bacteria, microorganisms, antibiotics, stimulants, and other items, so it is safe to use. There are many reports that the use of bovine colostrum as a breast milk fortifier for preterm infants provides necessary immune support for premature infants, but the selection of bovine colostrum products chosen must be free of Bacillus cereus because they are very dangerous for premature infants. This also emphasizes that for the bovine colostrum that is used in preterm infants, more clinical research support is needed. At the same time, it should also be emphasized that the composition of BC is different from that of human colostrum, in particular, the main protein of BC is casein, while the main protein in breast milk is whey protein, especially α-lactalbumin, which together with ovalbumin is still the reference protein with the best biological value, especially for muscles. Therefore, bovine colostrum is currently not a complete substitute for breast milk. In recent years, in addition to reports of bovine colostrum use in preterm infants, studies have also found that bovine colostrum has immunomodulatory and promoting effects in adolescents, adults, and the elderly. This suggests that bovine colostrum has the potential to provide appropriate immune support for people of all ages. Therefore, this study aimed to evaluate the quality of nutritional characteristics of bovine colostrum on three dimensions. The effects of bovine colostrum on people of all ages is a narrative review of the effects of bovine colostrum on immunity in people of all ages. This review identified several classes of immunoactive substances in bovine colostrum, including immunoglobulins, cytokines, and enzymes, and compared the nutritional composition of bovine colostrum with mature milk, colostrum and mature milk in full-term breast milk, and colostrum and mature milk in preterm breast milk, to demonstrate that bovine colostrum provides a rich range of immunoactive components. In addition, the influencing factors affecting the quality of bovine colostrum (immunoglobulin) were reviewed, and it was found that individual differences, environmental factors, and processing methods had a great impact on the quality of BC. More importantly, the immunomodulatory effects of bovine colostrum in people of all ages were reviewed in detail (with an emphasis on preterm infants and immunocompromised children in neonates) as evidence to support the immunity effects of colostrum in people of all ages. This review hopes to use the above evidence to make people understand the health role of bovine colostrum as having a human immunomodulatory effect, and at the same time, when seeing the potential value of bovine colostrum in the future, the limitations of its application should also be deeply re-explored, such as lactose intolerance, allergies, etc., to provide effective solutions for the wide application of bovine colostrum.

[Effect of autologous platelet-rich plasma perfusion on cytokines in uterine drainage fluid of patients with intrauterine adhesions following hysteroscopic adhesiolysis]

Objective: To investigate the effect of autologous platelet-rich plasma (PRP) perfusion on the levels of cytokines in uterine drainage fluid in patients with moderate to severe intrauterine adhesions (IUA) following hysteroscopic adhesiolysis. Methods: Thirty patients with moderate to severe IUA who underwent hysteroscopic adhesiolysis at Beijing Obstetrics and Gynecology Hospital, Capital Medical University from November 2020 to March 2021 were randomly divided into two groups: the PRP group (15 patients with placement of intrauterine-suitable balloons and PRP infusion) and the control group (15 patients with placement of intrauterine-suitable balloons only). For all patients, the channel switch was opened 48 hours after the surgery. The drainage fluid of the uterine cavity was collected using syringes through the proximal end of the drainage channel switch at 24 hours after the surgery and through the drainage channel directly at 48, 72, 96, and 120 hours after the surgery, and the levels of related cytokines including platelet-derived growth factor BB (PDGF-BB), vascular endothelial growth factor A (VEGF-A), insulin-like growth factor 1 (IGF-1) and transforming growth factor-β1 (TGF-β1) in the drainage fluid of the uterine cavity were evaluated, respectively. Results: (1) The changes in volumes of uterine cavity drainage fluid: the total drainage fluid volumes of the PRP group and the control group in 120 hours after the surgery were (21.8±2.9) and (22.7±2.7) ml, respectively, and there was no statistically significant difference between the two groups (t=-0.847, P>0.05). No significant differences were found in the volumes of drainage fluid between the two groups at 72, 96, and 120 hours after the surgery (all P>0.05). (2) Variation in cytokine levels in the uterine cavity drainage fluid: ① PDGF-BB: median PDGF-BB levels at 24 and 48 hours after the surgery in the PRP group (6.6 and 9.6 μg/L, respectively) were significantly higher than those in the control group (4.7 and 2.7 μg/L, respectively; all P<0.05). There were no significant differences in PDGF-BB levels between the two groups at 72, 96, and 120 hours after the surgery (all P>0.05). ② VEGF-A: median VEGF-A levels at 24 and 48 hours after the surgery in the PRP group (3.5 and 2.8 μg/L, respectively) were significantly higher than those in the control group (1.6 and 1.2 μg/L, respectively; all P<0.05). There were no significant differences in VEGF-A levels between the two groups at 72, 96, and 120 hours after the surgery (all P>0.05). ③ IGF-1: median IGF-1 level at 48 hours after the surgery in the PRP group was significantly higher than that in the control group (39.5 vs 8.6 μg/L, P<0.05). No significant differences were found in IGF-1 levels at 24, 72, 96, and 120 hours after the surgery between the two groups (all P>0.05). ④ TGF-β1: There were no significant differences in TGF-β1 levles between the two groups at 24, 48, 72, 96, and 120 hours after the surgery (all P>0.05). Conclusions: PRP perfusion following hysteroscopic adhesiolysis may increase the levels of PDGF-BB, VEGF-A, and IGF-1 in the uterine cavity drainage fluid, which plays a beneficial role in improving wound microvascular formation, reducing adhesion reformation, and promoting endometrial regeneration and repair.

Longitudinal evaluation of cervical length and shear wave elastography in women with spontaneous preterm birth

OBJECTIVE

To evaluate longitudinal changes in cervical length (CL) and mean cervical shear wave elastography (CSWE) score in women with a singleton or twin pregnancy who undergo spontaneous preterm birth (sPTB) compared with those who deliver at term.

METHODS

This was a prospective longitudinal study of unselected women with a singleton or twin pregnancy attending a dedicated research clinic for screening for sPTB at four timepoints during pregnancy: 11 + 0 to 15 + 6 weeks, 16 + 0 to 20 + 6 weeks, 21 + 0 to 24 + 6 weeks and 28 + 0 to 32 + 6 weeks. At each visit, a transvaginal ultrasound scan was conducted to measure the CL and the CSWE scores in six regions of interest (ROI) (inner, middle and external parts of anterior and posterior cervical lips). The mean CSWE score from the six ROIs was calculated for analysis. Log10 transformation was applied to data to produce a Gaussian distribution prior to statistical analysis. A multilevel mixed-effects analysis was performed to compare longitudinally CL and CSWE between the sPTB and term-delivery groups.

RESULTS

The final cohort consisted of 1264 women, including 1143 singleton pregnancies, of which 57 (5.0%) were complicated by sPTB, and 121 twin pregnancies, of which 33 (27.3%) were complicated by sPTB. Compared to those who delivered at term, women with sPTB had a lower CL across gestation when controlling for history of cervical surgery, number of fetuses, gestational age (GA) at cervical assessment and the interaction between GA at cervical assessment and sPTB (P < 0.001). Specifically, CL in the sPTB group was significantly lower at 21 + 0 to 24 + 6 weeks (P = 0.039) and 28 + 0 to 32 + 6 weeks (P < 0.001). Twin pregnancies had significantly greater CL throughout pregnancy compared with singleton pregnancies (regression coefficient, 0.01864; P < 0.001). After adjusting for maternal age, weight, height, body mass index and GA at cervical assessment, CSWE score in the sPTB group was significantly lower compared with that in the term-delivery group across gestation (P = 0.013). However, on analysis of individual visits, CSWE score in the sPTB group was significantly lower than that in the term-delivery group only at 11 + 0 to 15 + 6 weeks (P = 0.036). There was no difference in CSWE score between singleton and twin pregnancies throughout gestation (regression coefficient, -0.00128; P = 0.937).

CONCLUSIONS

Women with sPTB have a shorter and softer cervix across gestation compared with those who deliver at term. A shorter cervix in the sPTB group is observed from the late second trimester onwards, while lower cervical stiffness in the sPTB group is observed primarily in the first trimester. CL is significantly lower in singleton pregnancies compared with twin pregnancies, while cervical stiffness does not differ between the two. Our findings indicate that the cervix tends to undergo a softening process prior to shortening in sPTB cases. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Therapeutic Effects of Natural Products on Liver Cancer and Their Potential Mechanisms

Liver cancer ranks third globally among causes of cancer-related deaths, posing a significant public health challenge. However, current treatments are inadequate, prompting a growing demand for novel, safe, and effective therapies. Natural products (NPs) have emerged as promising candidates in drug development due to their diverse biological activities, low toxicity, and minimal side effects. This paper begins by reviewing existing treatment methods and drugs for liver cancer. It then summarizes the therapeutic effects of NPs sourced from various origins on liver cancer. Finally, we analyze the potential mechanisms of NPs in treating liver cancer, including inhibition of angiogenesis, migration, and invasion; regulation of the cell cycle; induction of apoptosis, autophagy, pyroptosis, and ferroptosis; influence on tumor metabolism; immune regulation; regulation of intestinal function; and regulation of key signaling pathways. This systematic review aims to provide a comprehensive overview of NPs research in liver cancer treatment, offering a foundation for further development and application in pharmaceuticals and functional foods.

Environmental enrichment attenuates depressive-like behavior in maternal rats by inhibiting neuroinflammation and apoptosis and promoting neuroplasticity

Gestational stress can exacerbate postpartum depression (PPD), for which treatment options remain limited. Environmental enrichment (EE) may be a therapeutic intervention for neuropsychiatric disorders, including depression, but the specific mechanisms by which EE might impact PPD remain unknown. Here we examined the behavioral, molecular, and cellular impact of EE in a stable PPD model in rats developed through maternal separation (MS). Maternal rats subjected to MS developed depression-like behavior and cognitive dysfunction together with evidence of significant neuroinflammation including microglia activation, neuronal apoptosis, and impaired synaptic plasticity. Expanding the duration of EE to throughout pregnancy and lactation, we observed an EE-associated reversal of MS-induced depressive phenotypes, inhibition of neuroinflammation and neuronal apoptosis, and improvement in synaptic plasticity in maternal rats. Thus, EE effectively alleviates neuroinflammation, neuronal apoptosis, damage to synaptic plasticity, and consequent depression-like behavior in mother rats experiencing MS-induced PPD, paving the way for new preventive and therapeutic strategies for PPD.

Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening

CONTEXT

Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models.

OBJECTIVE

To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients.

DESIGN

From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol.

SETTING

This study was conducted at Sun Yat-Sen University Cancer Center.

PATIENTS

PitNET patients who were pathologically confirmed were enrolled in this study.

INTERVENTIONS

Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed.

MAIN OUTCOME MEASURES

PDOs retained key genetic and morphological features of their parental tumors.

RESULTS

PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs.

CONCLUSION

The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.

Research Progress on New Functions of Animal and Plant Proteins

Protein is composed of peptides, essential nutrients for human survival and health, and the easy absorption of peptides further promotes human health. According to the source of the protein, it can be divided into plants, animals, and micro-organisms, which have important physiological effects on the health of the body, especially in enhancing immunity. The most widely used raw materials are animal protein and plant protein, and the protein composition formed by the two in a certain proportion is called "double protein". In recent years, China's State Administration for Market Regulation has issued an announcement on the "Implementation Rules for the Technical Evaluation of New Functions and Products of Health Foods (Trial)", which provides application conditions and listing protection for the research and development of new functions of health foods. At present, some researchers and enterprises have begun to pay attention to the potential of animal and plant proteins to be used in new functions. In this article, the research progress of animal and plant proteins in the new functions of Chinese health food is reviewed in detail, and suggestions for future research on animal and plant proteins are put forward.

The value of T2-weighted MRI contrast ratio combined with DWI in evaluating the pathological grade of solid lung adenocarcinoma

AIM

To assess the predictive value of T2-weighted (T2W) magnetic resonance imaging (MRI) in combination with diffusion-weighted imaging (DWI) for determining the pathological grading of solid lung adenocarcinoma.

MATERIALS AND METHODS

The clinical and imaging data from 153 cases of solid lung adenocarcinoma (82 men, 71 women, mean age 63.2 years) confirmed at histopathology in The First Affiliated Hospital of Xi'an Jiaotong University from January 2017 to May 2022 were analysed retrospectively. Adenocarcinomas were classified into low-grade (G1 and G2) and high-grade (G3) groups following the 2020 pathological grading system proposed by the International Association for the Study of Lung Cancer. The T2-weighted contrast ratio (T2CR), calculated as the T2 signal intensity of the lung mass/nodule divided by the T2 signal intensity of the right rhomboid muscle was utilised. Two experienced radiologists reviewed the MRI images independently, measured the T2CR, and obtained apparent diffusion coefficient (ADC) values. The Mann-Whitney U-test was used to compare general characteristics (sex, age, maximum diameter), T2CR, and ADC values between the low-grade and high-grade groups. The non-parametric Kruskal-Wallis test determined differences in T2CR and ADC values among the five adenocarcinoma subtypes. Receiver characteristic curve (ROC) analysis, along with area under the curve (AUC) calculation, assessed the effectiveness of each parameter in distinguishing the pathological grade of lung adenocarcinoma. A Z-test was used to compare the AUC values.

RESULTS

Among the 153 patients with adenocarcinoma, 103 had low-grade adenocarcinoma, and 50 had high-grade adenocarcinoma. The agreement between T2CR and ADC observers was good (0.948 and 0.929, respectively). None of the parameters followed a normal distribution (p<0.05). The ADC value was lower in the high-grade adenocarcinoma group compared to the low-grade adenocarcinoma group (p=0.004), while the T2CR value was higher in the high-grade group (p=0.011). Statistically significant differences were observed in maximum diameter and gender between the two groups (p<0.001 and p=0.005, respectively), while no significant differences were noted in age (p=0.980). Among the five adenocarcinoma subtypes, only the lepidic and micropapillary subtypes displayed statistical differences in ADC values (p=0.047), with the remaining subtypes showing no statistical differences (p>0.05). The AUC values for distinguishing high-grade adenocarcinoma from low-grade adenocarcinoma were 0.645 for ADC and 0.627 for T2CR. Combining T2CR, ADC, sex, and maximum diameter resulted in an AUC of 0.778, sensitivity of 70%, and specificity of 75%. This combination significantly improved diagnostic efficiency compared to T2CR and ADC alone (p=0.008, z = 2.624; p=0.007, z = 2.679).

CONCLUSION

The MRI quantitative parameters are useful for distinguishing the pathological grades of solid lung adenocarcinoma, offering valuable insights for precise lung cancer treatment.

MedChatZH: A tuning LLM for traditional Chinese medicine consultations

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs.

PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning

DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.

Oligodendrocyte-derived exosomes-containing SIRT2 ameliorates depressive-like behaviors and restores hippocampal neurogenesis and synaptic plasticity via the AKT/GSK-3β pathway in depressed mice

AIMS

To investigate the antidepressant role of oligodendrocyte-derived exosomes (ODEXs)-containing sirtuin 2 (SIRT2) and the underlying mechanism both in vivo and in vitro.

METHODS

Oligodendrocyte-derived exosomes isolated from mouse serum were administered to mice with chronic unpredictable mild stress (CUMS)-induced depression via the tail vein. The antidepressant effects of ODEXs were assessed through behavioral tests and quantification of alterations in hippocampal neuroplasticity. The role of SIRT2 was confirmed using the selective inhibitor AK-7. Neural stem/progenitor cells (NSPCs) were used to further validate the impact of overexpressed SIRT2 and ODEXs on neurogenesis and synapse formation in vitro.

RESULTS

Oligodendrocyte-derived exosome treatment alleviated depressive-like behaviors and restored neurogenesis and synaptic plasticity in CUMS mice. SIRT2 was enriched in ODEXs, and blocking SIRT2 with AK-7 reversed the antidepressant effects of ODEXs. SIRT2 overexpression was sufficient to enhance neurogenesis and synaptic protein expression. Mechanistically, ODEXs mediated transcellular delivery of SIRT2, targeting AKT deacetylation and AKT/GSK-3β signaling to regulate neuroplasticity.

CONCLUSION

This study establishes how ODEXs improve depressive-like behaviors and hippocampal neuroplasticity and might provide a promising therapeutic approach for depression.

In Silico prediction of inhibitors for multiple transporters via machine learning methods

Transporters play an indispensable role in facilitating the transport of nutrients, signaling molecules and the elimination of metabolites and toxins in human cells. Contemporary computational methods have been employed in the prediction of transporter inhibitors. However, these methods often focus on isolated endpoints, overlooking the interactions between transporters and lacking good interpretation. In this study, we integrated a comprehensive dataset and constructed models to assess the inhibitory effects on seven transporters. Both conventional machine learning and multi-task deep learning methods were employed. The results demonstrated that the MLT-GAT model achieved superior performance with an average AUC value of 0.882. It is noteworthy that our model excels not only in prediction performance but also in achieving robust interpretability, aided by GNN-Explainer. It provided valuable insights into transporter inhibition. The reliability of our model's predictions positioned it as a promising and valuable tool in the field of transporter inhibition research. Related data and code are available at https://gitee.com/wutiantian99/transporter_code.git.

Machine learning-based prediction model for distant metastasis of breast cancer

BACKGROUND

Breast cancer is the most prevalent malignancy in women. Advanced breast cancer can develop distant metastases, posing a severe threat to the life of patients. Because the clinical warning signs of distant metastasis are manifested in the late stage of the disease, there is a need for better methods of predicting metastasis.

METHODS

First, we screened breast cancer distant metastasis target genes by performing difference analysis and weighted gene co-expression network analysis (WGCNA) on the selected datasets, and performed analyses such as GO enrichment analysis on these target genes. Secondly, we screened breast cancer distant metastasis target genes by LASSO regression analysis and performed correlation analysis and other analyses on these biomarkers. Finally, we constructed several breast cancer distant metastasis prediction models based on Logistic Regression (LR) model, Random Forest (RF) model, Support Vector Machine (SVM) model, Gradient Boosting Decision Tree (GBDT) model and eXtreme Gradient Boosting (XGBoost) model, and selected the optimal model from them.

RESULTS

Several 21-gene breast cancer distant metastasis prediction models were constructed, with the best performance of the model constructed based on the random forest model. This model accurately predicted the emergence of distant metastases from breast cancer, with an accuracy of 93.6 %, an F1-score of 88.9 % and an AUC value of 91.3 % on the validation set.

CONCLUSION

Our findings have the potential to be translated into a point-of-care prognostic analysis to reduce breast cancer mortality.

Clinical application of a three-dimensional-printed model in the treatment of intracranial and extracranial communicating tumors: a pilot study

BACKGROUND

Surgical management for intracranial and extracranial communicating tumors is difficult due to the complex anatomical structures. Therefore, assisting methods are urgently needed. Accordingly, this study aimed to investigate the utility of a three-dimensional (3D)-printed model in the treatment of intracranial and extracranial communicating tumors as well as its applicability in surgical planning and resident education.

METHODS

Individualized 3D-printed models were created for eight patients with intracranial and extracranial communicating tumors. Based on these 3D-printed models, a comprehensive surgical plan was made for each patient, after which the patients underwent surgery. The clinicopathological data of patients were collected and retrospectively analyzed to determine surgical outcomes. To examine the educational capability of the 3D-printed models, specialists and resident doctors were invited to review three of these cases and then rate the clinical utility of the models using a questionnaire.

RESULTS

The 3D-printed models accurately replicated anatomical structures, including the tumor, surrounding structures, and the skull. Based on these models, customized surgical approaches, including the orbitozygomatic approach and transcervical approach, were designed for the patients. Although parameters such as operation time and blood loss varied among the patients, satisfactory surgical outcomes were achieved, with only one patient developing a postoperative complication. Regarding the educational applicability of the 3D-printed model, the mean agreement for all eight questionnaire items was above six (seven being complete agreement). Moreover, no significant difference was noted in the agreement scores between specialists and residents.

CONCLUSION

The results revealed that 3D-printed models have good structural accuracy and are potentially beneficial in developing surgical approaches and educating residents. Further research is needed to test the true applicability of these models in the treatment of intracranial and extracranial communicating tumors.