Machine learning based identification potential feature genes for prediction of drug efficacy in nonalcoholic steatohepatitis animal model.

BACKGROUND: Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. PURPOSE: Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. METHODS: The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. FINDINGS: Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). CONCLUSION: This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.

Correction: Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

[This corrects the article DOI: 10.1039/D4MD00023D.].

Immunohistochemical expression of SATB2 and PAX8 in differentiating primary from metastatic ovarian mucinous neoplasms.

Accurate stratification of an ovarian mucinous neoplasm as primary or secondary is always challenging as they show overlapping histomorphological and immunohistochemical features. Immunohistochemical staining for SATB2 and PAX8 was performed on 80 cases of mucinous ovarian neoplasms subdivided into 53 primary [25 primary ovarian mucinous carcinomas (POMCs) and 28 mucinous borderline tumors (MBTs)] and 27 secondary (12 of colonic origin, 7 of appendiceal origin, and 8 of gastric origin). Expression was correlated with different clinicopathologic parameters. PAX8-positive immunostaining was detected in 38 out of 53 cases (71.69%) of primary ovarian mucinous neoplasms (POMNs) with null positivity in the secondary ovarian mucinous tumors (0/27). SATB2-positive expression was detected in 16 out of 27 cases (59.26%) of the secondary ovarian mucinous tumors. None of the studied POMNs showed any positive immunostaining for SATB2 (0/53). A profile of SATB2-/PAX8+ and SATB2+/PAX8- can be used to differentiate POMNCs from secondary ovarian mucinous tumors of GI origin, respectively, with 100% specificity. PAX8 expression is associated with some clinicopathologic parameters providing the basis for the possible usage of PAX8 as prognostic marker.

Diagnostic Value of Combined BCOR, Cyclin D1, and CD10 in Differentiating Endometrial Stromal Sarcoma From Other Uterine Spindle Cell Lesions.

Uterine spindle cell lesions share a dilemmatic overlapped features that needed to be addressed by the pathologist to reach a conclusive accurate diagnosis for its prognostic value and different management decisions. Usage of combined IHC panel can be an aiding guiding tool in this context. The aim of this study is to evaluate the diagnostic value of combined BCOR, Cyclin D1, and CD10 IHC panel in differentiating endometrial stromal sarcoma from other uterine spindle cell lesions. This study included 60 cases categorized into endometrial stromal sarcoma group (ESS) (12 cases high-grade endometrial stromal sarcoma [HGESS] and 18 cases low-grade endometrial stromal sarcoma [LGESS]), malignant uterine spindle cell lesions group (5 cases adenosarcoma [AS], 6 cases leiomyosarcoma [LS], 4 cases carcinosarcoma [CS]), and benign uterine lesions group (5 cases endometrial stromal nodule [ESN], 5 cases leiomyoma, and 5 cases adenomyosis). IHC staining procedure and evaluation for BCOR, Cyclin D1, and CD10 was performed on all studied cases. BCOR IHC staining was positive in all HGESS (12/12) of ESS group cases, with diffuse pattern in 75% of cases. BCOR-diffuse staining pattern was not recorded in any of LGESS (0/18), malignant mesenchymal lesions group (0/15), and also benign lesions group (0/15). Cyclin D1 positivity was observed only in HGESS cases, in parallel with positive-BCOR expression. On the contrary, CD10 was negatively expressed in all HGESS and positive in all LGESS, ESN, and adenomyosis cases. A specificity of 100% and sensitivity of 75% were recorded in differentiating HGESS from malignant mesenchymal lesions (including LMS, AS, and CS) and also HGESS from LGESS when using the combined panel BCOR +ve D /Cyclin D1 +ve / CD10 -ve , considering only the BCOR-diffuse staining pattern. In conclusion, BCOR +ve D /Cyclin D1 +ve /CD10 -ve as a combined panel is 100% specific and with lesser sensitivity in diagnosing HGESS as well as differentiating it from LGESS and other malignant uterine spindle cell lesions.

Experimental investigation for nonalcoholic fatty pancreas management using probiotics.

BACKGROUND: Nonalcoholic fatty pancreatitis (NAFP) presents a pressing challenge within the domain of metabolic disorders, necessitating further exploration to unveil its molecular intricacies and discover effective treatments. Our focus was to delve into the potential therapeutic impact of ZBiotic, a specially engineered strain of probiotic B. subtilis, in managing NAFP by targeting specific genes linked with necroptosis and the TNF signaling pathway, including TNF, ZBP1, HSPA1B, and MAPK3, along with their upstream epigenetic regulator, miR-5192, identified through bioinformatics. METHODS: Rats were subjected to either a standard or high-fat, high-sucrose diet (HFHS) for eight weeks. Subsequently, they were divided into groups: NAFP model, and two additional groups receiving daily doses of ZBiotic (0.5 ml and 1 ml/kg), and the original B. subtilis strain group (1 ml/kg) for four weeks, alongside the HFHS diet. RESULTS: ZBiotic exhibited remarkable efficacy in modulating gene expression, leading to the downregulation of miR-5192 and its target mRNAs (p < 0.001). Treatment resulted in the reversal of fibrosis, inflammation, and insulin resistance, evidenced by reductions in body weight, serum amylase, and lipase levels (p < 0.001), and decreased percentages of Caspase and Nuclear Factor Kappa-positive cells in pancreatic sections (p < 0.01). Notably, high-dose ZBiotic displayed superior efficacy compared to the original B. subtilis strain, highlighting its potential in mitigating NAFP progression by regulating pivotal pancreatic genes. CONCLUSION: ZBiotic holds promise in curbing NAFP advancement, curbing fibrosis and inflammation while alleviating metabolic and pathological irregularities observed in the NAFP animal model. This impact was intricately linked to the modulation of necroptosis/TNF-mediated pathway-related signatures.

Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

Background: Inflammation-mediated insulin resistance in type 2 diabetes mellitus (T2DM) increases complications, necessitating investigation of its mechanism to find new safe therapies. This study investigated the effect of rosavin on the autophagy and the cGAS-STING pathway-related signatures (ZBP1, STING1, DDX58, LC3B, TNF-α) and on their epigenetic modifiers (miR-1976 and lncRNA AC074117.2) that were identified from in silico analysis in T2DM animals. Methods: A T2DM rat model was established by combining a high-fat diet (HFD) and streptozotocin (STZ). After four weeks from T2DM induction, HFD/STZ-induced T2DM rats were subdivided into an untreated group (T2DM group) and three treated groups which received 10, 20, or 30 mg per kg of R. rosea daily for 4 weeks. Results: The study found that rosavin can affect the cGAS-STING pathway-related RNA signatures by decreasing the expressions of ZBP1, STING1, DDX58, and miR-1976 while increasing the lncRNA AC074117.2 level in the liver, kidney, and adipose tissues. Rosavin prevented further weight loss, reduced serum insulin and glucose, improved insulin resistance and the lipid panel, and mitigated liver and kidney damage compared to the untreated T2DM group. The treatment also resulted in reduced inflammation levels and improved autophagy manifested by decreased immunostaining of TNF-α and increased immunostaining of LC3B in the liver and kidneys of the treated T2DM rats. Conclusion: Rosavin has shown potential in attenuating T2DM, inhibiting inflammation in the liver and kidneys, and improving metabolic disturbances in a T2DM animal model. The observed effect was linked to the activation of autophagy and suppression of the cGAS-STING pathway.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.