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healthcare system might be able to detect and help these women, which would be beneficial for both mother and child.

Auriculocondylar syndrome (ACS) is a rare disorder characterized by micrognathia, mandibular condyle hypoplasia, and auricular abnormalities. see more Only 6 pathogenic variants of GNAI3 have been identified associated with ACS so far. Here, we report a case of prenatal genetic diagnosis of ACS carrying a novel GNAI3 variant.

A woman with 30 weeks of gestation was referred to genetic counseling for polyhydramnios and fetal craniofacial anomaly. Severe micrognathia and mandibular hypoplasia were identified on ultrasonography. The mandibular length was 2.4 cm, which was markedly smaller than the 95th percentile. The ears were low-set with no cleft or notching between the lobe and helix. The face was round with prominent cheeks. Whole-exome sequencing identified a novel de novo missense variant of c.140G > A in the GNAI3 gene. This mutation caused an amino acid substitution of p.Ser47Asn in the highly conserved G1 motif, which was predicted to impair the guanine nucleotide-binding function. All ACS cases with GNAI3 mutations were literature reviewed, revealing female-dominated severe cases and right-side-prone deformities.

Severe micrognathia and mandibular hypoplasia accompanied by polyhydramnios are prenatal indicators of ACS. We expanded the mutation spectrum of GNAI3 and summarized clinical features to promote awareness of ACS.

Severe micrognathia and mandibular hypoplasia accompanied by polyhydramnios are prenatal indicators of ACS. We expanded the mutation spectrum of GNAI3 and summarized clinical features to promote awareness of ACS.

Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. link2 Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research.

A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets.

After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism.

To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.

To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed.

Maternal obesity is a public health issue that could affect both women's and children's health. This qualitative study aimed to identify barriers to weight management of pregnant women with obesity and low socioeconomic backgrounds.

The current qualitative study has been conducted using a grounded theory approach by analyzing data collected from in-depth interviews with clients of Tehran's public health care centers for prenatal care. The criteria for selecting participants were excessive weight gain during the first two trimesters of pregnancy, low socioeconomic status, and willingness to share their experiences. A semi-structured guide consisting of open-ended questions was asked in a private room. Open, axial, and selective coding were applied to the data.

Four main themes emerged from data, each of which has some subcategories 1) personal factors (unpleasant emotions and feelings, personal tastes/hobbies, workload and responsibilities, and history of diseases), 2) pregnancy status (unintended and high-risk pregnancy), 3) interpersonal relationships and support (lack of a spouse's support and unhealthy role modeling of relatives), 4) socio-cultural factors/influences (social norms and values, lack of access to health services, and unreliable information channels).

This study provides an overview of the barriers to the weight management of pregnant women from low socioeconomic backgrounds. The results could help develop appropriate health strategies for low socioeconomic women with obesity. Also, health care providers for this group of women could use these findings as a guide to consider their conditions and background.

This study provides an overview of the barriers to the weight management of pregnant women from low socioeconomic backgrounds. The results could help develop appropriate health strategies for low socioeconomic women with obesity. Also, health care providers for this group of women could use these findings as a guide to consider their conditions and background.

Hemodialysis patients are among high-risk groups for COVID-19. Africa is the continent with the lowest number of cases in the general population but we have little information about the disease burden in dialysis patients.

This study aimed to describe the seroprevalence of SARS-CoV-2 antibodies in the hemodialysis population of Senegal.

We conducted a multicenter cross-sectional survey, between June and September 2020 involving 10 public dialysis units randomly selected in eight regions of Senegal. After seeking their consent, we included 303 patients aged ≥ 18 years and hemodialysis for ≥ 3 months. Clinical symptoms and biological parameters were collected from medical records. Patients' blood samples were tested with Abbott SARS-CoV-2 Ig G assay using an Architect system. Statistical tests were performed with STATA 12.0.

Seroprevalence of SARS-CoV-2 antibodies was 21.1% (95% CI = 16.7-26.1%). We noticed a wide variability in SARS-CoV-2 seroprevalence between regions ranging from 5.6 to 51.7%. Among the 38 patients who underwent nasal swab testing, only six had a PCR-confirmed infection and all of them did seroconvert. Suggestive clinical symptoms were reported by 28.1% of seropositive patients and the majority of them presented asymptomatic disease. After multivariate analysis, a previous contact with a confirmed case and living in a high population density region were associated with the presence of SARS-CoV-2 antibodies.

This study presents to our knowledge the first seroprevalence data in African hemodialysis patients. Compared to data from other continents, we found a higher proportion of patients with SARS-CoV-2 antibodies but a lower lethality rate.

This study presents to our knowledge the first seroprevalence data in African hemodialysis patients. Compared to data from other continents, we found a higher proportion of patients with SARS-CoV-2 antibodies but a lower lethality rate.

Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug-target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates.

In this paper, a new approach based on unifying matrix factorization and nuclear norm minimization is proposed to find a low-rank interaction. In this combined method, to solve the low-rank matrix approximation, the terms in the DTI problem are used in such a way that the nuclear norm regularized problem is optimized by a bilinear factorization based on Rank-Restricted Soft Singular Value Decomposition (RRSSVD). In the proposed method, adjacencies between drugs and targets are encoded by graphs. Drug-target interaction, drug-drug similarity, target-target, and combination of similarities have also been used as input.

The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.

The proposed method is evaluated on four benchmark datasets known as Enzymes (E), Ion channels (ICs), G protein-coupled receptors (GPCRs) and nuclear receptors (NRs) based on AUC, AUPR, and time measure. The results show an improvement in the performance of the proposed method compared to the state-of-the-art techniques.

The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes (T2D) and early-stage chronic kidney disease (CKD). The following studies describe the analytical validation of the KidneyIntelX assay including impact of observed methodologic variability on the composite risk score.

Analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2) based on Clinical Laboratory Standards Institute (CLSI) guidelines. Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. Analysis of cross-reactivitythe clinical validity of the reported results.

The set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. Notably, reproducibility of the composite risk score demonstrated that expected analytical laboratory variation did not impact the assigned risk category, and therefore, the clinical validity of the reported results.

SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not all of them are thoroughly quantified.

We assembled an ensemble of public datasets of published CNV calls and raw data for the well-studied Genome in a Bottle individual NA12878. link3 This assembly represents a variety of methods and pipelines used for CNV calling from array, short- and long-read technologies. We then performed cross-technology comparisons regarding their ability to call CNVs. Different from other studies, we refrained from using the golden standard. Instead, we attempted to validate the CNV calls by the raw data of each technology.

Our study confirms that long-read platforms enable recalling CNVs in genomic regions inaccessible to arrays or short reads. We also found that the reproducibility of a CNV by different pipelines within each technology is strongly linked to other CNV evidence measures.