catena-Poly[[(2,2′-bipyidine-2κ2 N,N′)-μ-cyanido-1:2κ2 N:C-dicopper(I)]-μ-bromido-[(2,2′-bipyidine-2κ2 N,N′)-μ-cyanido-1:2κ2 N:C-dicopper(I)]-μ-cyanido-κ2 N:C]

In the title complex, [Cu4Br(CN)3(C10H8N2)2]n, the four independent CuI atoms are all in distorted trigonal-planar geometries. One is formed by one N atom and one C atom from two cyanide groups and one Br atom, one is formed by two N atoms from two cyanide groups and one Br atom, and the other two are formed by two N atoms from a chelating 2,2′-bipyridine (bpy) ligand and one C atom from a cyanide group. The structure exhibits a zigzag chain backbone along [101] constructed by bromide and cyanide anions bridging the CuI atoms, with the [Cu(bpy)(CN)] units pointing laterally

Value of radiomics-based two-dimensional ultrasound for diagnosing early diabetic nephropathy

Abstract Despite efforts to diagnose diabetic nephropathy (DN) using biochemical data or ultrasound imaging separately, a significant gap exists regarding the development of integrated models combining both modalities for enhanced early DN diagnosis. Therefore, we aimed to assess the ability of machine learning models containing two-dimensional ultrasound imaging and biochemical data to diagnose early DN in patients with type 2 diabetes mellitus (T2DM). This retrospective study included 219 patients, divided into a training or test group at an 8:2 ratio. Features were selected using minimum redundancy maximum relevance and random forest-recursive feature elimination. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for sensitivity, specificity, Matthews Correlation Coefficient, F1 score, and accuracy. K-nearest neighbor, support vector machine, and logistic regression models could diagnose early DN, with AUC values of 0.94, 0.85, and 0.85 in the training cohort and 0.91, 0.84, and 0.84 in the test cohort, respectively. Early DN diagnosing using two-dimensional ultrasound-based radiomics models can potentially revolutionize T2DM patient care by enabling proactive interventions, ultimately improving patient outcomes. Our integrated approach showcases the power of artificial intelligence in medical imaging, enhancing early disease detection strategies with far-reaching applications across medical disciplines

Long noncoding RNAs as potential diagnostic biomarkers for diabetes mellitus and complications: A systematic review and meta‐analysis

Abstract Aims Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences. Materials and Methods We performed a systematic review and meta‐analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022. We evaluated human case–control or cohort studies on differential lncRNA expression in type 2 diabetes mellitus or its associated comorbidities. We excluded studies if they were non‐peer reviewed or published in languages other than English. From 2387 identified studies, we included 17 (4685 participants). Results Analysis of the pooled data showed that lncRNAs had a diagnostic area under the curve (AUC) of 0.84 (95% CI: 0.80–0.87), with a sensitivity of 0.79 (95% CI: 0.74–0.83) and a specificity of 0.75 (95% CI: 0.69–0.80). LncRNAs had an AUC of 0.65 for the diagnosis of prediabetes, with 82% sensitivity and 65% specificity. Conclusions LncRNAs may be promising diagnostic markers for type 2 diabetes mellitus and its complications