Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

Microscopic signs indicative of drowning are not specific to drowning but also to any other form of suffocation where mechanical obstruction is involved. Our study aimed to evaluate both macroscopic and microscopic findings across different groups sharing a common mechanism of death but differing causes and to compare the diatom test with pathohistological examination.Twenty-nine adult Wistar rats, weighing within recommended ranges, were divided into four groups (L1-L4). The diatom test followed established guidelines for diatoms in water from the Bosna River. Microscopic examination revealed diatoms in the lungs of rats in L3 and L4 groups. Pathohistological findings showed varying degrees of changes including consolidation and inflammatory cell infiltration, dominated by lymphocytes and macrophages, with some samples also showing eosinophilic leukocytes.Significant differences were observed between animals whose cause of death was mechanical asphyxia (suffocatio) and those that were submersed for1 hour versus those that were submersed for 72 hours after death. Diatoms identified in group L4 samples 3, 4, and 5 included Navicula sp. (U3 and U6) and Ulnaria ulna (U4).Our findings suggest combining the diatom test with pathohistological analysis to support a drowning diagnosis. Further examination of other organs could enhance result reliability.

Syringocystadenoma papilliferum is a benign cutaneous adnexal tumor of eccrine and apocrine glands, with a warty appearance that is usually found on the scalp, neck and face, much less frequently appears in the chest or abdomen and extremely rarely on the female genital organs, i.e. the vulva. We present a case of Syringocystadenoma papilliferum on the vulva of a 64-year-old woman. This case illustrates the atypical location of this rare disease and adds to the differential diagnosis of lesions on the vulva.

Inherited kidney diseases (IKD) and congenital anomalies of the kidney and urinary tract (CAKUT) are causes of kidney failure requiring kidney replacement therapy (KRT) that major renal registries usually amalgamate into the primary renal disease (PRD) category ‘miscellaneous’ or in the glomerulonephritis or pyelonephritis categories. This makes IKDs invisible (except for polycystic kidney disease) and may negatively influence the use of genetic testing, which may identify a cause for IKDs and some CAKUT. We have re-examined the etiology of KRT by composing a separate IKD and CAKUT PRD group using data from the European Renal Association (ERA) Registry. In 2019, IKD-CAKUT was the fourth most common cause of kidney failure among incident KRT patients, accounting for 8.9% of cases (IKD 7.4% [including 5.0% ADPKD], CAKUT 1.5%), behind diabetes (23.0%), hypertension (14.4%) and glomerulonephritis (10.6%). IKD-CAKUT was the most common cause of kidney failure among patients younger than 20 years (41.0% of cases), but their incidence rate was highest among those aged 45–74 years (22.5 per million age-related population). Among prevalent KRT patients, IKD-CAKUT (18.5%) and glomerulonephritis (18.7%) were the two most common causes of kidney failure overall, while IKD-CAKUT was the most common cause in women (21.6%) and in patients younger than 45 years (29.1%). IKD and CAKUT are common causes of kidney failure among KRT patients. Distinct categorization of IKD and CAKUT better characterizes the epidemiology of the causes of chronic kidney disease, and highlights the importance of genetic testing in the diagnostic workup of CKD.

In the literature, auditory attention is explored through neural speech tracking, primarily entailing modeling and analyzing electroencephalography (EEG) responses to natural speech via linear filtering. Our study takes a novel approach, introducing an enhanced coherence estimation technique to assess the strength of neural speech tracking. This enables effective discrimination between attended and ignored speech. To mitigate the impact of colored noise in EEG, we address two biases–overall coherence-level bias and spectral peak-shifting bias. In a listening study involving 32 participants with hearing impairment, tasked with attending to competing talkers in background noise, our coherence-based method effectively discerns EEG representations of attended and ignored speech. We comprehensively analyze frequency bands, individual frequencies, and EEG channels. Frequency bands of importance are shown to be delta, theta and alpha, and the important EEG channels are the central. Lastly, we showcase coherence differences across different noise reduction settings implemented in hearing aids (HAs), underscoring our method's potential to objectively assess auditory attention and enhance HA efficacy.

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with $R_\ell$ and $R_t$, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee's $Z$-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current $B$-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.

Numerous studies suggest that common genetic and epigenetic factors such as p53, histone deacetylase (HDAC), brain-derived neurotrophic factor (BDNF), the (Ataxia Telangiectasia mutated) ATM gene, cyclin-dependent kinase 5 (CDK5), glycogen synthase kinase 3 (GSK3) and altered expression of microRNA (miRNA) play a crucial role in cancer and neurodegeneration. As there is growing evidence that epigenetic aberrations in cancer and neurological diseases lead to complex pathophysiological changes, the simultaneous targeting of epigenetic and other related pathways by dual-target inhibitors may contribute to the discovery of more effective and personalized therapeutic options. Computer-Aided Drug Design (CADD) provides comprehensive bioinformatic, chemoinformatic, and chemometric approaches for the design of novel chemotypes of epigenetic dual-target inhibitors, enabling efficient discovery of new drug candidates for innovative treatments of these multifactorial diseases. The detailed anticancer mechanisms by which the epigenetic dual-target inhibitors alter metastatic and tumorigenic properties, influence the tumor microenvironment, or regulate the immune response are also presented and discussed in the review. To improve our understanding of the pathogenesis of cancer and neurodegeneration, this review discusses novel therapeutic agents targeting different molecular mechanisms involved in these multifactorial diseases.

Precision medicine is a developing trend in oncology, and it includes the prognosis and treatment of advanced-stage ccRCC. New predictive factors and therapeutic targets for this disease are steadily needed. The aim of this study was to explore the tumor expression of inversin as a potential prognostic factor and/or therapeutic target in ccRCC. We compared the expression of inversin between primary ccRCC and normal renal tissues by using immunohistochemistry and rtPCR in our cohort, and we also analyzed publicly available data from the TCGA-KIRC cohort. We found that the expression of inversin was significantly lower in primary tumor tissue, in comparison to solid normal tissue. Data from the KIRC study confirmed that a lower INVS expression level in ccRCC was significantly related with the overall and disease-specific survival, as well as with a shorter progression-free interval (p < 0.05). Four out of ten inversin interactome partners were significantly related with the overall and disease-specific survival in ccRCC. A lower expression of ANKS6 was a negative survival predictor, while a higher expression of NPHP3, DVL1, or DVL3 was related with a lower survival. The expression of INVS and its interactome partners in ccRCC was correlated with the differentiation of the tumor and metastasis. The expression of INVS and its partners was also correlated with tumor leukocyte infiltration and the expression of immune checkpoint genes. The results of this study point to inversin and a distinguished group of its interactome partners as potential prognostic factors in ccRCC, with their predominant involvement in the modulation of the inflammatory infiltration of the tumor microenvironment and a strong relationship with the metastatic potential of the tumor.

ABSTRACT The triglyceride/high-density lipoprotein (TG/HDL) ratio emerges as a promising marker for cardiovascular risk. However, the relationship between overall serum lipid levels and hemorrhagic stroke (HS) remains uncertain. Therefore, our study aims to explore the association between this novel index and mortality in HS patients. Utilizing a retrospective-prospective framework from January 2020 to August 2023, we scrutinized data from 104 hospitalized patients diagnosed with HS, with particular attention to their medical backgrounds and lipid profiles. Age (odds ratio [OR], 1.078; 95% confidence interval [CI], 1.032–1.125; P = 0.001), atrial fibrillation (OR, 0.237; 95% CI, 0.074–0.760; P = 0.015), glucose level (OR, 1.121; 95% CI, 1.007–1.247; P = 0.037), and TG/HDL index (OR, 0.368; 95% CI, 0.173–0.863; P = 0.020) emerged as independent predictors for in-hospital mortality, as determined by both univariable and multivariable logistic regression analyses. Our results add weight to the growing evidence backing the utility of the TG/HDL index in assessing cardiovascular risk among HS patients. They emphasize the necessity of adopting a comprehensive risk assessment and management strategy that incorporates both traditional markers and novel indicators.

Background and Objectives: This study aimed to investigate the novel adiponectin–resistin (AR) index as a predictor of the development of metabolic syndrome (MetS) in individuals with type 2 diabetes mellitus (T2DM). MetS is common in T2DM and increases cardiovascular risk. Adiponectin and resistin, adipokines with opposing effects on insulin sensitivity and inflammation, make the AR index a potential marker for metabolic risk. Materials and Methods: This prospective observational study included 80 T2DM participants (ages 30–60) from Sarajevo, Bosnia and Herzegovina, over 24 months. The participants were divided into two groups: T2DM with MetS (n = 48) and T2DM without MetS (n = 32). Anthropometric data, biochemical analyses, and serum levels of adiponectin and resistin were measured at baseline and every six months. The AR index was calculated using the formula AR = 1 + log10(R) − 1 + log10(A), where R and A represent resistin and adiponectin concentrations. Logistic regression identified predictors of MetS. Results: T2DM patients who developed MetS showed a significant decline in adiponectin levels (40.19 to 32.49 ng/mL, p = 0.02) and a rise in resistin levels (284.50 to 315.21 pg/mL, p = 0.001). The AR index increased from 2.85 to 2.98 (p = 0.001). The AR index and resistin were independent predictors of MetS after 18 months, with the AR index showing a stronger predictive value (p = 0.007; EXP(B) = 1.265). Conclusions: The AR index is a practical marker for predicting MetS development in T2DM participants, improving metabolic risk stratification. Incorporating it into clinical assessments may enhance early detection and treatment strategies.