Innovative mesenchymal stem cell treatments for fatty liver disease.

The incidence of non-alcoholic fatty liver disease (NAFLD) and alcohol-associated liver disease (ALD) is increasing year by year due to changes in the contemporary environment and dietary structure, and is an important public health problem worldwide. There is an urgent need to continuously improve the understanding of their disease mechanisms and develop novel therapeutic strategies. Mesenchymal stem cells (MSCs) have shown promise as a potential therapeutic strategy in therapeutic studies of NAFLD and ALD. NAFLD and ALD have different triggers and their specific mechanisms of disease progression are different, but both involve disease processes such as hepatocellular steatosis and potential fibrosis, cirrhosis, and even hepatocellular carcinoma. MSCs have metabolic regulatory, anti-apoptotic, antioxidant, and immunomodulatory effects that together promote liver injury repair and functional recovery, and have demonstrated positive results in preclinical studies. This editorial is a continuum of Jiang et al's review focusing on the advantages and limitations of MSCs and their derivatives as therapeutics for NAFLD and ALD. They detail how MSCs attenuate the progression of NAFLD by modulating molecular pathways involved in glucolipid metabolism, inflammation, oxidative stress, endoplasmic reticulum stress, and fibrosis. Based on recent advances, we discuss MSCs and their derivatives as therapeutic strategies for NAFLD and ALD, providing useful information for their clinical application.

A comprehensive evaluation of large language models in mining gene relations and pathway knowledge.

Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large-scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)-based models and open-source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API-based models GPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged behind their API-based counterparts, whereas Falcon-180b and llama2-7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon-180b and 0.2207 for llama2-7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh-aza).

Bioinformatics and biomedical informatics with ChatGPT: Year one review.

The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre-trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

An updated systematic review and meta-analysis of the efficacy and safety of early oral feeding vs. traditional oral feeding after gastric cancer surgery.

Introduction: Early oral feeding (EOF) has been shown to improve postoperative recovery for many surgeries. However, surgeons are still skeptical about EOF after gastric cancer surgery due to possible side effects. This updated systematic review and meta-analysis aimed to investigate the efficacy and safety of EOF in patients after gastric cancer surgery. Methods: Randomized controlled trials (RCTs) investigating EOF in patients after gastric cancer surgery were searched in the databases of PubMed, Embase, Clinicaltrials.gov, and Cochrane from 2005 to 2023, and an updated meta-analysis was performed using RevMan 5.4 software. Results: The results of 11 RCTs involving 1,352 patients were included and scrutinized in this analysis. Hospital days [weighted mean difference (WMD), -1.72; 95% confidence interval (CI), -2.14 to -1.30; p<0.00001), the time to first flatus (WMD, -0.72; 95% CI, -0.99 to -0.46; p<0.00001), and hospital costs (WMD, -3.78; 95% CI, -4.50 to -3.05; p<0.00001) were significantly decreased in the EOF group. Oral feeding tolerance [risk ratio (RR), 1.00; 95% CI, 0.95-1.04; p=0.85), readmission rates (RR, 1.28; 95% CI, 0.50-3.28; p=0.61), postoperative complications (RR, 1.02; 95% CI, 0.81-1.29; p=0.84), anastomotic leakage (RR, 0.83; 95% CI, 0.25-2.78; p=0.76), and pulmonary infection (RR, 0.65; 95% CI, 0.31-1.39; p=0.27) were not significantly statistical between two groups. Conclusion: This meta-analysis reveals that EOF could reduce hospital days, the time to first flatus, and hospital costs, but it was not associated with oral feeding tolerance, readmission rates, or postoperative complications especially anastomotic leakage and pulmonary infection, regardless of whether laparoscopic or open surgery, partial or total gastrectomy, or the timing of EOF initiation.

[Treatment of massive rotator cuff tears with modified Chinese-way technique].

OBJECTIVE: To explore clinical effect of modified Chinese-way technique under shoulder arthroscopy in treating massive rotator cuff tears. METHODS: From January 2019 to June 2022, 22 patients with massive rotator cuff tears who underwent arthroscopic rotator cuff repair with improved Chinese-way technique, including 10 males and 12 females, aged from 46 to 76 years old with an average of(64.14±7.45) years old;the courses of disease ranged from 5 to 14 months with an average of(8.32±2.42) months;19 patients were complete repaired, and 3 patients were partial repaired. Visual analogue scale (VAS) and University of California at Los Angeles (UCLA) scale were used to evaluate pain and function of shoulder joint preoperatively and 1 year postoperatively. Postoperative complications, the integrity of reconstructed tissue structure and the size of subacromial space were observed. RESULTS: All patients were followed up from 12 to 34 months with an average of (17.14±5.93) months. Re-tear were occurred in 4 patients during MRI follow-up, but clinical symptoms of patients were improved significantly and they were satisfied with the treatment, the others were no complications such as incision infection, peripheral nerve injury, loosening and falling off of internal fixation anchors. Preoperative and 1 year after operation VAS were (8.05±1.12) and (1.82±1.50), UCLA scores were (7.45±1.65) and (31.41±2.87) respectively, and the difference was statistically significant (P<0.05). CONCLUSION: The modified Chinese-way technique under shoulder arthroscopy for the massive rotator cuff tear could relieve pain obviously and recovery postoperative function well, with satisfactory curative effect.

A highly sensitive and reproducible fluorescence sensor for continuously measuring hydrogen peroxide at the sub-ppm level.

A highly sensitive fluorescence sensor for monitoring low concentrations of hydrogen peroxide was designed. The sensor employs the commercially available palladium or platinum metal on activated charcoal as catalysts to decompose hydrogen peroxide into water and molecular oxygen. The produced oxygen concentration can be measured in real time using an oxygen-sensitive layer doped with photostable oxygen probes. The sensor exhibits high sensitivity that is able to measure hydrogen peroxide concentration down to 20 ppb and can measure hydrogen peroxide concentration in the range of 0.1-100 ppm and 0.02-100 ppm, respectively. The response is fully reversible and the typical response time is less than one minute, which makes it suitable to continuously measure hydrogen peroxide over a long duration. Due to the excellent batch-to-batch consistency of palladium or platinum metal on activated charcoal, the sensor can be massively produced with good reproducibility and affordable price, which holds great potential for constructing sensors for industrial and practical applications.

Phase I-II study of OBI-888, a humanized monoclonal IgG1 antibody against the tumor-associated carbohydrate antigen Globo H, in patients with advanced solid tumors.

PURPOSE: OBI-888 is a humanized, monoclonal IgG1 antibody specific to the tumor-associated carbohydrate antigen Globo H. We conducted a phase I-II study of OBI-888 in patients with advanced cancer. METHODS: Patients were treated with OBI-888 5, 10, or 20 mg/kg IV weekly in Part A ("3 + 3" design) and 20 mg/kg IV weekly in Part B (Simon's 2-stage design) (1 cycle = 28 days). RESULTS: Overall, 54 patients were treated (Part A, n = 14; Part B, n = 40). OBI-888 was safe and well tolerated across the doses studied, with a low incidence of OBI-888-related treatment emergent adverse events. The maximum tolerated dose of OBI-888 was not reached. No dose-limiting toxicities were noted up to the 20 mg/kg dose level (recommended phase 2 dose). Stable disease (SD) was noted in 28.6% and 20% of Parts A and B, respectively, including three patients with SD for 6+, 7+, and 9 months. Antibody-dependent cellular cytotoxicity (ADCC) was induced after each OBI-888 treatment (average increase, 3.8-fold and 4.7-fold in Parts A and B, respectively), suggesting that ADCC induction is a potential mechanism of action of OBI-888. CONCLUSIONS: OBI-888 was well tolerated. Prolonged SD was noted in three patients. ADCC was induced after each OBI-888 treatment.

Preoperative plasma fibrinogen level is a risk factor for the long-term survival of postmenopausal women after surgery for breast cancer.

BACKGROUND: Studies have indicated an association between fibrinogen levels and the prognosis of breast cancer patients. However, fibrinogen levels are notably susceptible to fluctuations due to the menstrual cycle. This study explored the relationship between preoperative plasma fibrinogen levels and the prognosis of postmenopausal breast cancer women after surgery. METHOD: 855 patients with postmenopausal breast cancer were monitored for 10 years. Cox proportional hazards regression models were used to perform univariate and multivariate analyses to identify factors that are of substantial prognostic value. RESULTS: The median follow-up was 77 months (51-105 months), and the maximum 142 months. Over the follow-up period, 65 deaths (7.6 %) were recorded. Multivariate Cox regression results show that preoperative plasma fibrinogen level (hazard ratio [HR] =1.615, 95 % confidence interval [CI]: 1.233-2.115) and age (HR = 1.626, 95%CI: 1.250-2.116) were independent risk factors for 10-year overall survival after surgery in postmenopausal breast cancer patients, while endocrine therapy (HR = 0.414, 95%CI: 0.202-0.846) was an independent protective factor. Multivariate Cox regression results also show preoperative plasma fibrinogen level was an independent risk factor for 10-year disease-free survival (HR = 1.398, 95 % CI: 1.137-1.719) and 10-year distant metastasis-free survival (HR = 1.436, 95%CI: 1.153-1.787). CONCLUSION: Elevated pretreatment plasma fibrinogen levels are associated with a poorer long-term prognosis in postmenopausal breast cancer patients following surgical treatment.

Electrocatalytic Aromatic Alcohols Splitting to Aldehydes and H2 Gas.

Selective electrocatalytic transformation of alcohols to aldehydes offers an efficient and environmentally friendly platform for the simultaneous production of fine chemicals and pure hydrogen gas. However, traditional alcohol oxidation reactions (AORs) in aqueous electrolyte unavoidably face competitive reactions (e.g., water oxidation and overoxidations reactions) for the presence of active oxygen species from water oxidation, causing an unwanted decrease in final efficiency and selectivity. Here, we developed an integrated all-solid proton generator-transfer electrolyzer to trigger the pure alcohol splitting reaction (ASR). In this splitting process, only O-H and C-H bonds can be cleaved at the proton generator (Pt nanoparticles), thereby completely avoiding all competitive reactions involving oxygen active species to give a > 99% selectivity to aldehydes. The as-generated protons are transported to the cathode by a three-dimensional (3D) conducting network (assemblies of ionomers and carbon spheres) for efficient hydrogen production. Unlike the poor selectivity (<22%) and durability (<3 h) of a conventional AOR electrolyzer, this ASR electrolyzer could be continuously operated at a low cell voltage of 1.2 V for at least 10 days to give a high Faradaic efficiency of 80-93% for aldehyde production.

Early-stage postoperative depression and anxiety following orthognathic surgery: a cross-sectional study.

BACKGROUND: The objective of this study was to observe the incidence and potential risk factors of postoperative depression and anxiety in patients during the early period after undergoing orthognathic surgery. METHODS: From March 7 to September 7, 2023, patients ≥ 18 years of age who were scheduled for elective orthognathic surgery under general anesthesia in Peking University School and Hospital of Stomatology were included in this study. We prospectively evaluated their degrees of pre- and postoperative depression and anxiety using the Patient Health Questionnaire-9 and the State Trait Anxiety Inventory. Associations between the perioperative factors and occurrences of postoperative anxiety and depression were evaluated using a multivariate logistic regression model. RESULTS: A total of 371 patients were included in the analysis. Within five days after surgery, we observed the occurrence of depression in 32% (116) of the patients and anxiety in 72.8% (270) of them. Their preoperative depression score on the Pain Catastrophizing Scale and intraoperative urine output were significantly associated with a higher risk of postoperative depression. The presence of preoperative anxiety, postoperative moderate-to-severe pain, postoperative nausea and vomiting and postoperative insomnia were significantly associated with a higher risk of postoperative anxiety. Furthermore, a monthly income ≥ ¥10000 was found to be significantly associated with a lower risk of postoperative anxiety. CONCLUSIONS: Postoperative depression and anxiety are common among patients who undergo orthognathic surgery. Moreover, preoperative psychological status and incidence of postoperative adverse events were associated with an increased risk of depression and anxiety after surgery. The results of the present study suggest that careful psychological assessment and appropriate management are necessary to improve patients' recovery following orthognathic surgery.

Evolution of the regulatory subunits for the heteromeric acetyl-CoA carboxylase.

The committed step for de novo fatty acid (FA) synthesis is the ATP-dependent carboxylation of acetyl-coenzyme A catalysed by acetyl-CoA carboxylase (ACCase). In most plants, ACCase is a multi-subunit complex orthologous to prokaryotes. However, unlike prokaryotes, the plant and algal orthologues are comprised both catalytic and additional dedicated regulatory subunits. Novel regulatory subunits, biotin lipoyl attachment domain-containing proteins (BADC) and carboxyltransferase interactors (CTI) (both three-gene families in Arabidopsis) represent new effectors specific to plants and certain algal species. The evolutionary history of these genes in autotrophic eukaryotes remains elusive, making it an ongoing area of research. Analyses of potential protein-protein and co-occurrence interactions, informed by gene network patterns using the STRING database, in Arabidopsis thaliana and Chlamydomonas reinhardtii unveil intricate gene associations with ACCase, suggesting a complex interplay between FA synthesis and other cellular processes. Among both species, a higher number of co-expressed genes was identified in Arabidopsis, indicating a wider potential regulatory network of ACCase in plants. This review investigates the extent to which these genes arose in autotrophic eukaryotes and provides insights into their evolutionary trajectory. This article is part of the theme issue 'The evolution of plant metabolism'.

Survival after neoadjuvant chemotherapy or chemoradiotherapy for resectable esophageal squamous cell carcinoma: A pooled analysis of randomized clinical trials.

BACKGROUND: The comparison of neoadjuvant chemoradiotherapy (nCRT) versus neoadjuvant chemotherapy (nCT) for locally advanced esophageal squamous cell carcinoma (ESCC) remains inconclusive, and the optimal regimen is still under investigation. METHODS: Prospective randomized clinical trials were systematically searched in electronic databases from inception to Oct 2023. A graphical reconstructive algorithm was employed to extract time-to-event outcomes from Kaplan-Meier curves presented in the original studies. Using reconstructed individual patient data, summary overall survival (OS) and disease progression-free survival (DFS) for nCRT versus nCT, primarily doublet chemotherapy were recalculated. Hazard Ratios (HRs) of OS and DFS reported were also pooled by the fixed-effects model. RESULTS: A total of 6 randomized clinical trials comprising 1162 patients were included in our analysis. In the individual patient data (IPD) pooled analysis, a significant OS benefit was found for nCRT in ESCC (HR=0.81, 95 %CI:0.67-0.98, p=0.029), compared with the treatment of nCT. The median overall survival time were 53 months (95 %CI:41.9-67.7 m) and 66 months(95 %CI:57.2-NA) respectively in the nCT and nCRT groups. Additionally, a significant improvement in PFS for nCRT compared to nCT in the IPD pooled analysis (HR=0.79,95 %CI:0.64-0.98; p=0.027). Consistent with above results, the pooled HRs of OS and DFS for nCRT versus nCT were 0.78 (95 % CI 0.65-0.92, p=0.004) and 0.79 (95 % CI: 0.65-0.97, p=0.02), respectively. Notably, no substantial heterogeneity across studies was observed. CONCLUSIONS: Our findings indicate that nCRT offers better survival outcomes for ESCC, at least when compared to neoadjuvant doublet chemotherapy.This evidence continues to support the clinical practice of employing nCRT in locally advanced resectable ESCC.

Deviceization of high-performance and flexible Ag2Se films for electronic skin and servo rotation angle control.

Ag2Se shows significant potential for near-room-temperature thermoelectric applications, but its performance and device design are still evolving. In this work, we design a novel flexible Ag2Se thin-film-based thermoelectric device with optimized electrode materials and structure, achieving a high output power density of over 65 W m-2 and a normalized power density up to 3.68 µW cm-2 K-2 at a temperature difference of 42 K. By fine-tuning vapor selenization time, we strengthen the (013) orientation and carrier mobility of Ag2Se films, reducing excessive Ag interstitials and achieving a power factor of over 29 µW cm-1 K-2 at 393 K. A protective layer boosts flexibility of the thin film, retaining 90% performance after 1000 bends at 60°. Coupled with p-type Sb2Te3 thin films and rational simulations, the device shows rapid human motion response and precise servo motor control, highlighting the potential of high-performance Ag2Se thin films in advanced applications.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data.

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

Integrative metagenomic and lipidomic analyses reveal alterations in children with obesity and after lifestyle intervention.

Background: Despite emerging evidence linking alterations in gut microbiota to childhood obesity, the metabolic mechanisms linking gut microbiota to the lipid profile during childhood obesity and weight loss remain poorly understood. Methodology: In this study, children with obesity were treated with lifestyle weight loss therapy. Metagenomics association studies and serum untargeted lipidomics analyses were performed in children with obesity and healthy controls before and after weight loss. Main findings: We identified alterations in gut microbiota associated with childhood obesity, as well as variations in circulating metabolite concentrations. Children with obesity showed significant decreases in the levels of s-Rothia_kristinae and s-Enterobacter_roggenkampii, alongsige elevated levels of s-Clostridiales_bacterium_Marseille-P5551. Following weight loss, the levels of s-Streptococcus_infantarius and s-Leuconostoc_citreum increased by factors of 3.354 and 1.505, respectively, in comparison to their pre-weight loss levels. Correlation analyses indicated a significant positive relationship between ChE(2:0) levels and both with s-Lachnospiraceae_bacterium_TF09-5 and fasting glucose levels. CoQ8 levels were significantly negatively correlated with s-Rothia_kristinae and HOMA-IR. Conclusion: We linked altered gut microbiota and serum lipid levels in children with obesity to clinical indicators, indicating a potential impact on glucose metabolism via lipids. This study contributes to understanding the mechanistic relationship between altered gut microbiota and childhood obesity and weight loss, suggesting gut microbiome as a promising target for intervention. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=178971, ChiCTR2300072179.

Barriers and facilitators for implementing the WHO Safe Childbirth Checklist (SCC) in Mozambique: A qualitative study using the Consolidated Framework for Implementation Research (CFIR).

High maternal and neonatal mortality rates persist in Mozambique, with stillbirths remaining understudied. Most maternal and neonatal deaths in the country are due to preventable and treatable childbirth-related complications that often occur in low-resource settings. The World Health Organization introduced the Safe Childbirth Checklist (SCC) in 2015 to reduce adverse birth outcomes. The SCC, a structured list of evidence-based practices, targets the main causes of maternal and neonatal deaths and stillbirths in healthcare facilities. The SCC has been tested in over 35 countries, demonstrating its ability to improve the quality of care. However, it has not been adopted in Mozambique. This study aimed to identify potential facilitators and barriers to SCC implementation from the perspective of birth attendants, clinical administrators, and decision-makers to inform future SCC implementation in Mozambique. We conducted a qualitative study involving focus group discussions with birth attendants (n = 24) and individual interviews with clinical administrators (n = 6) and decision-makers (n = 8). The Consolidated Framework for Implementation Research guided the questions used in the interviews and focus group discussions, as well as the subsequent data analysis. A deductive thematic analysis of Portuguese-to-English translated transcripts was performed. In Mozambique, most barriers to potential SCC implementation stem from the challenges within a weak health system, including underfunded maternal care, lack of infrastructure and human resources, and low provider motivation. The simplicity of the SCC and the commitment of healthcare providers to better childbirth practices, combined with their willingness to adopt the SCC, were identified as major facilitators. To improve the feasibility of SCC implementation and increase compatibility with current childbirth routines for birth attendants, the SCC should be tailored to context-specific needs. Future research should prioritize conducting pre-implementation assessments to align the SCC more effectively with local contexts and facilitate sustainable enhancements in childbirth practices.

An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

Diagnostic value of an interpretable machine learning model based on clinical ultrasound features for follicular thyroid carcinoma.

Background: Follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) present diagnostic challenges due to overlapping clinical and ultrasound features. Improving the diagnosis of FTC can enhance patient prognosis and effectiveness in clinical management. This study seeks to develop a predictive model for FTC based on ultrasound features using machine learning (ML) algorithms and assess its diagnostic effectiveness. Methods: Patients diagnosed with FTA or FTC based on surgical pathology between January 2009 and February 2023 at Zhejiang Provincial Cancer Hospital and Zhejiang Provincial People's Hospital were retrospectively included. A total of 562 patients from Zhejiang Provincial Cancer Hospital comprised the training set, and 218 patients from Zhejiang Provincial People's Hospital constituted the validation set. Subsequently, clinical parameters and ultrasound characteristics of the patients were collected. The diagnostic parameters were analyzed using the least absolute shrinkage and selection operator and multivariate logistic regression screening methods. Next, a comparative analysis was performed using seven ML models. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), precision, recall, and comprehensive evaluation index (F-score) were calculated to compare the diagnostic efficacy among the seven models and determine the optimal model. Further, the optimal model was validated, and the SHapley Additive ExPlanations (SHAP) approach was applied to explain the significance of the model variables. Finally, an individualized risk assessment was conducted. Results: Age, echogenicity, thyroglobulin antibody (TGAb), echotexture, composition, triiodothyronine (T3), thyroglobulin (TG), margin, thyroid-stimulating hormone (TSH), calcification, and halo thickness >2 mm were influential factors for diagnosing FTC. The XGBoost model was identified as the optimal model after a comprehensive evaluation. The AUC of this model in the validation set was 0.969 [95% confidence interval (CI), 0.946-0.992], while its precision sensitivity, specificity, and accuracy were 0.791, 0.930, 0.913 and 0.917, respectively. Conclusions: XGBoost model based on ultrasound features was constructed and interpreted using the SHAP method, providing evidence for the diagnosis of FTC and guidance for the personalized treatment of patients.

Analyzing the role of TM4SF1 expression in pancreatic adenocarcinoma: understanding prognostic implications and therapeutic opportunities.

Background: Pancreatic adenocarcinoma (PAAD) is a highly lethal malignancy characterized by aggressive growth and poor prognosis. Understanding the molecular mechanisms underlying PAAD is crucial for developing effective therapies. This study aimed to explore the role of TM4SF1 and other key genes in PAAD progression, their prognostic implications, and therapeutic opportunities. Methods: Differential gene expression analysis was performed using PAAD and normal tissue samples to identify upregulated genes, with TM4SF1 emerging as significantly elevated in PAAD. Functional enrichment analysis elucidated associated signaling pathways. A prognostic model comprising BPIFB4, PLEKHN1, CPTP, DVL1, and DDR1 was developed using least absolute shrinkage and selection operator (LASSO) regression and validated in an independent cohort. Genetic mutation analysis provided insights into the functional significance of identified genes. Pharmacogenomic analysis examined associations between gene expression and drug sensitivity. Experimental validation included quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blot analyses to confirm gene expression patterns and protein levels. Results: Lower TM4SF1 expression correlated with enhanced anti-tumor immune activity in PAAD, suggesting a complex interplay between genetic expression and immune response. The prognostic model showed robust associations with patient survival outcomes, validated across diverse patient cohorts. Genetic mutation analysis highlighted potential therapeutic targets. Pharmacogenomic analysis revealed correlations between gene expression profiles and drug responsiveness, suggesting personalized treatment strategies. Experimental validation confirmed elevated TM4SF1 levels in tumor tissues and demonstrated its role in promoting cancer cell proliferation and colony formation. Conclusions: This study advances understanding of the molecular landscape of PAAD, emphasizing TM4SF1 as a key regulator and potential therapeutic target. The integration of genetic expression, immune response dynamics, and pharmacogenomics offers a multifaceted approach to personalized treatment strategies for PAAD, paving the way for improved patient outcomes and novel therapeutic interventions. Further research is warranted to elucidate the clinical utility of targeting TM4SF1 and other identified genes in PAAD management.

Deep learning-assisted distinguishing breast phyllodes tumors from fibroadenomas based on ultrasound images: a diagnostic study.

OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumors (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. METHODS: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting six radiologists with different levels of experience. RESULTS: Upon testing, Xception model demonstrated the best diagnostic performance (AUC: 0.87, 95%CI: 0.81-0.92), outperforming all radiologists (all p < 0.05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. CONCLUSIONS: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). ADVANCES IN KNOWLEDGE: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate two types of breast tumors which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.