Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting.

Background: Infections caused by soil-transmitted helminths (STHs) are the most prevalent neglected tropical diseases and result in a major disease burden in low- and middle-income countries, especially in school-aged children. Improved diagnostic methods, especially for light intensity infections, are needed for efficient, control and elimination of STHs as a public health problem, as well as STH management. Image-based artificial intelligence (AI) has shown promise for STH detection in digitized stool samples.

Gut Microbiome Changes in Patients With Idiopathic Normal Pressure Hydrocephalus.

Background: The gut microbiome is a complex system within the human gastrointestinal tract. The bacteria play a significant role in human health, and some can promote inflammation and pathologic processes through chemical interactions or metabolites. Gut microbiome dysbiosis has been linked to some neurological and other diseases.

Imputation of missing values in lipidomic datasets.

Lipidomic data often exhibit missing data points, which can be categorized as missing completely at random (MCAR), missing at random, or missing not at random (MNAR). In order to utilize statistical methods that require complete datasets or to improve the identification of potential effects in statistical comparisons, imputation techniques can be employed. In this study, we investigate commonly used methods such as zero, half-minimum, mean, and median imputation, as well as more advanced techniques such as k-nearest neighbor and random forest imputation.

Low-dose decitabine enhances the efficacy of viral cancer vaccines for immunotherapy.

Cancer immunotherapy requires a specific antitumor CD8 T cell-driven immune response; however, upon genetic and epigenetic alterations of the antigen processing and presenting components, cancer cells escape the CD8 T cell recognition. As a result, poorly immunogenic tumors are refractory to conventional immunotherapy. In this context, the use of viral cancer vaccines in combination with hypomethylating agents represents a promising strategy to prevent cancer from escaping immune system recognition.

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images.

Detection and measurement of amyloid-beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD").

Characterization of circulating leptin-receptor levels following acute sleep restriction: A pilot study on healthy adult females.

Background: Insufficient sleep adversely affects energy homeostasis by decreasing leptin levels. The underlying physiological mechanisms; however, remain unclear. Circulating leptin is well described to be regulated by its soluble receptor (sOB-R).

Targeting CD33+ Acute Myeloid Leukemia with GLK-33, a Lintuzumab-Auristatin Conjugate with a Wide Therapeutic Window.

CD33 (Siglec-3) is a cell surface receptor expressed in approximately 90% of acute myeloid leukemia (AML) blasts, making it an attractive target for therapy of AML. Although previous CD33-targeting antibody-drug conjugates (ADC) like gemtuzumab ozogamicin (GO, Mylotarg) have shown efficacy in AML treatment, they have suffered from toxicity and narrow therapeutic window. This study aimed to develop a novelADCwith improved tolerability and a wider therapeutic window.

Genetic contribution to disease-course severity and progression in the SUPER-Finland study, a cohort of 10,403 individuals with psychotic disorders.

Genetic factors contribute to the susceptibility of psychotic disorders, but less is known how they affect psychotic disease-course development. Utilizing polygenic scores (PGSs) in combination with longitudinal healthcare data with decades of follow-up we investigated the contributing genetics to psychotic disease-course severity and diagnostic shifts in the SUPER-Finland study, encompassing 10 403 genotyped individuals with a psychotic disorder. To longitudinally track the study participants' past disease-course severity, we created a psychiatric hospitalization burden metric using the full-coverage and nation-wide Finnish in-hospital registry (data from 1969 and onwards).

Age-related macular degeneration discordance in monozygotic twin pairs.

Purpose: To examine age-related macular degeneration (AMD) and retinal pigment epithelium (RPE)-Bruch's membrane (BrM) complex volume associations in monozygotic twin pairs.

Methods: In this study, 106 elderly twins (53 twin pairs) from the Finnish Twin Cohort study were recruited. Each participant underwent dilated 35-degree digital colour fundus photography (CFP), and spectral domain optical coherence tomography (OCT) and replied to a structured study questionnaire.

Outline and background for the EU-OS solubility prediction challenge.

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility.

SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy.

Numerous imaging techniques are available for observing and interrogating biological samples, and several of them can be used consecutively to enable correlative analysis of different image modalities with varying resolutions and the inclusion of structural or molecular information. Achieving accurate registration of multimodal images is essential for the correlative analysis process, but it remains a challenging computer vision task with no widely accepted solution. Moreover, supervised registration methods require annotated data produced by experts, which is limited.

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity.

Importance: Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects.

Objective: To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort.

Impact of physical activity on preeclampsia and angiogenic markers in the Finnish Genetics of Pre-eclampsia Consortium (FINNPEC) cohort.

Introduction: Effect of physical activity in pregnancy on preeclampsia (PE) and angiogenic markers is not well understood. We studied the association of physical activity and PE in a case-control setting and assessed whether exercise in PE and non-PE women associate with maternal serum concentrations of soluble fms-like tyrosine kinase 1 (s-Flt-1), placental growth factor (PlGF) and soluble endoglin (sEng) and sFlt-1/PlGF ratio in the Finnish Genetics of Pre-eclampsia Consortium (FINNPEC) cohort.

Materials And Methods: Participants completed a questionnaire on their background information and serum samples were collected from a subset.

Genome-wide association studies highlight novel risk loci for septal defects and left-sided congenital heart defects.

Background: Congenital heart defects (CHD) are structural defects of the heart affecting approximately 1% of newborns. They exhibit low penetrance and non-Mendelian patterns of inheritance as varied and complex traits. While genetic factors are known to play an important role in the development of CHD, the specific genetics remain unknown for the majority of patients.

α-Melanocyte-stimulating hormone alleviates pathological cardiac remodeling via melanocortin 5 receptor.

α-Melanocyte-stimulating hormone (α-MSH) regulates diverse physiological functions by activating melanocortin receptors (MC-R). However, the role of α-MSH and its possible target receptors in the heart remain completely unknown. Here we investigate whether α-MSH could be involved in pathological cardiac remodeling.

Genome-Wide Polygenic Score for Muscle Strength Predicts Risk for Common Diseases and Lifespan: A Prospective Cohort Study.

Background: We used a polygenic score for hand grip strength (PGS HGS) to investigate whether genetic predisposition for higher muscle strength predicts age-related noncommunicable diseases, survival from acute adverse health events, and mortality.

Methods: This study consisted of 342 443 Finnish biobank participants from FinnGen Data Freeze 10 (53% women) aged 40-108 with combined genotype and health registry data. Associations between PGS HGS and a total of 27 clinical endpoints were explored with linear or Cox regression models.

Sex differences in kidney metabolism may reflect sex-dependent outcomes in human diabetic kidney disease.

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease (CKD) and progresses faster in males than in females. We identify sex-based differences in kidney metabolism and in the blood metabolome of male and female individuals with diabetes. Primary human proximal tubular epithelial cells (PTECs) from healthy males displayed increased mitochondrial respiration, oxidative stress, apoptosis, and greater injury when exposed to high glucose compared with PTECs from healthy females.

Tutorial on survival modeling with applications to omics data.

Motivation: Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.