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Background: Differential diagnosis of pancreatic solid lesion (PSL) and prognosis of pancreatic cancer (PC) is a clinical challenge. We aimed to explore the differential diagnostic value of sound speed (SS) obtained from endoscopic ultrasonography (EUS) in PSL and the prognostic value of SS in PC. Methods: Patients with PSL in The Third Xiangya Hospital of Central South University from March 2019 to October 2019 were prospectively enrolled, who obtained SS from PSL. Patients were divided into the PC group and the pancreatic benign lesion (PBL) group. SS1 is the SS of lesions and SS2 is the SS of normal tissues adjacent to lesions. Ratio1 is equal to SS1 divided by SS2 of PSL (ratio1 = SS1/SS2). Results: Eighty patients were enrolled (24 PBL patients, 56 PC patients). SS1 and ratio1 in PC group were higher compared with PBL group (SS1:1568.00 vs. 1550.00, Z = −2.066, p = 0.039; ratio1: 1.0110 vs. 1.0051, Z = −3.391, p = 0.001). The SS1 in PC (Z = −6.503, p < 0.001) was higher compared to SS2. In the nonsurgical group of PC, low ratio1 predicted high overall survival (OS) (7.000 months vs. 4.000 months; p = 0.039). In the surgical group of PC, low SS1 was associated with low median OS (4.000 months vs. 12.000 months; p = 0.033). Conclusions: SS plays a vital role in distinguishing between PBL and PC. Higher SS1 and ratio1 obtained by EUS are more related to PC than PBL. In PC patients, high SS1 may predict pancreatic lesions. In the nonsurgical group of PC, low ratio1 may predict high OS. However, in the surgical group of PC, low SS1 may predict low OS. [ABSTRACT FROM AUTHOR]

The article discusses the concept of foundation models (FMs) in surgical artificial intelligence (AI). FMs are trained on broad datasets and can be adapted to various surgical tasks, potentially revolutionizing the field of surgical AI. The article highlights the challenges in developing a surgical FM, such as the need for large volumes of data and computing power, as well as the contextual nature of surgery. It also explores potential applications of FMs in surgery, including risk prediction and anatomical segmentation. The article emphasizes the importance of validation, ethical considerations, and collaboration in the development of FMs. [Extracted from the article]

Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics. [ABSTRACT FROM AUTHOR]

Background: Acute upper gastrointestinal bleeding (AUGIB) is a major cause of morbidity and mortality. This presentation however is not universally high risk as only 20–30% of bleeds require urgent haemostatic therapy. Nevertheless, the current standard of care is for all patients admitted to an inpatient bed to undergo endoscopy within 24 h for risk stratification which is invasive, costly and difficult to achieve in routine clinical practice. Objectives: To develop novel non-endoscopic machine learning models for AUGIB to predict the need for haemostatic therapy by endoscopic, radiological or surgical intervention. Design: A retrospective cohort study Method: We analysed data from patients admitted with AUGIB to hospitals from 2015 to 2020 (n = 970). Machine learning models were internally validated to predict the need for haemostatic therapy. The performance of the models was compared to the Glasgow-Blatchford score (GBS) using the area under receiver operating characteristic (AUROC) curves. Results: The random forest classifier [AUROC 0.84 (0.80–0.87)] had the best performance and was superior to the GBS [AUROC 0.75 (0.72–0.78), p < 0.001] in predicting the need for haemostatic therapy in patients with AUGIB. A GBS cut-off of ⩾12 was associated with an accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.74, 0.49, 0.81, 0.41 and 0.85, respectively. The Random Forrest model performed better with an accuracy, sensitivity, specificity, PPV and NPV of 0.82, 0.54, 0.90, 0.60 and 0.88, respectively. Conclusion: We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy. [ABSTRACT FROM AUTHOR]

Background: Intestinal dysbacteriosis is associated with depression. This study aimed to establish an antibiotics-induced depression mouse model and explore the mechanism of antibiotic-induced depression.Methods: C57BL/6 J mice were treated with antibiotics to prepare the antibiotic-induced depression mouse model. Behavioral tests and depression-related bio-markers were examined. To understand the abundance of different bacteria in intestinal flora and screen out the predominant bacterial species, metagenomic analysis of feces was carried out. Finally, we detected the expression of NF-κB-p65 and p-NF-κB-p65 in PFC and the hippocampus using Western blot.Results: Mixtures A and B caused depression-like behavior in mice. Norepinephrine, 5-hydroxytryptamine, and brain-derived neurotrophic factor in hippocampus and PFC of antibiotic-induced depression mice significantly decreased. The serum adrenocorticotropic hormone and corticosterone concentrations increased. The abundance values of Bacteroides thetaiotaomicron, Klebsiella oxytoca, and Klebsiella aerogenes in antibiotic-induced depression mice significantly increased, and the characteristic KO genes and metabolic pathways in antibiotic-induced depression mice were significantly different with in CUMS depression mice (the positive control) and normal mice. The relative levels of p-NF-κB-p65 in antibiotics-induced depression mice were significantly higher than in normal mice.Limitations: How dysbacteriosis induces inflammation in the central nervous system is unclear.Conclusions: Specific antibiotic mixture can cause depression-like behavior and changes of depression-related bio-markers in mice. The antibiotic-induced depression mice display changes in the species and metabolism of intestinal bacterial flora. The activation of NF-κB inflammatory signaling pathway in the central nervous system may act as one of the mechanisms in the development of antibiotic-induced depression. [ABSTRACT FROM AUTHOR]

Background and Aims: It is crucial to manage the recurrence of Crohn's disease (CD). This study is aimed to explore whether visceral adipose tissue (VAT) and skeletal muscle (SM) are associated with the recurrence of CD upon different treatments.Methods: All patients with a definite diagnosis of CD were retrospectively divided into three groups according to distinct treatment regimens: 5-amino salicylic acid group (Group A), steroids + azathioprine (Group B) and biologics (Group C). The pretreatment computerized tomography (CT) images and clinical data were collected. The VAT area, mesenteric fat index (MFI), the ratio of VAT area to fat mass (VAT area/FM) were assessed. The primary end point was the recurrence of CD within 1 year of follow-up.Results: A total of 171 CD patients were enrolled, including 57 (33.33%) patients in Group A, 70 (40.94%) patients in Group B and 44 (25.73%) patients in Group C. Patients with 1-year recurrence had higher MFI (P = 0.011) and VAT area/FM (P = 0.000). ROC curve demonstrated that patients with the ratio of VAT area/FM and MFI higher than 0.578 and 1.394 tended to have recurrence with the AUC of 0.707 and 0.709. Similar results could be observed in Group A & B but not in Group C.Conclusions: High VAT area/FM and MFI are related to recurrence within 1 year for newly diagnosed CD patients treated by 5-amino salicylic or azathioprine + steroids rather than biologics. We could not observe any radiological data associated with the recurrence of CD patients under biological treatment. [ABSTRACT FROM AUTHOR]

Objective: In order to understand the role of long noncoding RNAs (lncRNAs) played in the mechanisms of glyphosate neurotoxicity in neuronal development. Methods: Perinatal glyphosate exposure (PGE) mouse model was constructed, and a lncRNA microarray was used to study the lncRNA expression changes in the hippocampus tissue of perinatal glyphosate exposure mice. Then we used GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) databases to analyze the function of the differentially expressed mRNAs and lncRNAs. Results: LncRNA microarray analysis revealed that 1759 lncRNAs and 759 mRNAs were differentially expressed in the perinatal glyphosate exposure (PGE) mice group (G group) compared with the normal control mice group (C group). The functions of the DEmRNAs are involved in the cellular response to hormone stimulus. The ceRNA analysis showed that some interaction networks existed, including (ENSMUST00000137546, ENSMUST00000160950)/(miR‐34a‐3p, miR‐130a‐3p)/(Il12b, Irf1). Further analysis of the target mRNAs of miRNAs indicated that the possible functions involved the neuroactive ligand‐receptor interaction and calcium signaling pathway, which are involved in perinatal glyphosate exposure‐induced neurotoxicity. Conclusion: The aberrant expression of lncRNAs is related to the perinatal glyphosate‐exposed neurotoxicity. These lncRNAs affect the target gene expression level, might by regulating neuroactive ligand‐receptor interactions. The (ENSMUST00000137546, ENSMUST00000160950)/ (miRNA‐34a‐5p, miR‐130a‐3p) / mRNAs (e.g., Il12b, Irf1) interaction network may functions in perinatal glyphosate exposure‐induced neurotoxicity. [ABSTRACT FROM AUTHOR]

Malnutrition is a major concern in low- and middle-income countries (LMIC), but the full extent of nutritional deficiencies remains unknown largely due to lack of accurate assessment methods. This study seeks to develop and validate an objective, passive method of estimating food and nutrient intake in households in Ghana and Uganda. Household members (including under-5s and adolescents) are assigned a wearable camera device to capture images of their food intake during waking hours. Using custom software, images captured are then used to estimate an individual's food and nutrient (i.e., protein, fat, carbohydrate, energy, and micronutrients) intake. Passive food image capture and assessment provides an objective measure of food and nutrient intake in real time, minimizing some of the limitations associated with self-reported dietary intake methods. Its use in LMIC could potentially increase the understanding of a population's nutritional status, and the contribution of household food intake to the malnutrition burden. This project is registered at clinicaltrials.gov (NCT03723460). [ABSTRACT FROM AUTHOR]