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Findings from family and twin studies support a genetic contribution to the development of sexual orientation in men. However, previous studies have yielded conflicting evidence for linkage to chromosome Xq28. We conducted a genome-wide linkage scan on 409 independent pairs of homosexual brothers (908 analyzed individuals in 384 families), by far the largest study of its kind to date. We identified two regions of linkage: the pericentromeric region on chromosome 8 (maximum two-point LOD = 4.08, maximum multipoint LOD = 2.59), which overlaps with the second strongest region from a previous separate linkage scan of 155 brother pairs; and Xq28 (maximum two-point LOD = 2.99, maximum multipoint LOD = 2.76), which was also implicated in prior research. Results, especially in the context of past studies, support the existence of genes on pericentromeric chromosome 8 and chromosome Xq28 influencing development of male sexual orientation.

Nano fluids are widely used today for various energy-related applications such as coolants, refrigerants, and fuel additives. New coolants and design modifications are being explored due to renewed interest in improving the working fluid properties of heat exchangers. Several studies have investigated nanofluids to enhance radiator and heat exchanger performance. A new class of coolants includes single, binary, and tertiary nanoparticle-based hybrid nano-coolants using ethylene glycol/deionized water combinations as base fluids infused with different nanoparticles. This review article focuses on the hydrothermal behavior of heat exchangers (radiators for engine applications) with mono/hybrid nanofluids. The first part of the review focuses on the preparation of hybrid nanofluids, highlighting the working fluid properties such as density, viscosity, specific heat, and thermal conductivity. The second part discusses innovative methodologies adopted for accomplishing higher heat transfer rates with relatively low-pressure drop and pump work. The third part discusses the applications of mono and hybrid nanofluids in engine radiators and fuel additives in diesel and biodiesel blends. The last part is devoted to a summary of the research and future directions using mono and hybrid nanofluids for various cooling applications.

Background The potential for PPAR agonists to positively affect risk of cardiovascular disease in patients with type 2 diabetes (T2DM) is of persistent attention. The PRESS XII study primarily aimed to evaluate the efficacy and safety of saroglitazar (2 mg and 4 mg) as compared to pioglitazone 30 mg on glycemic control in patients with type 2 diabetes mellitus. Methods In this randomized double-blind study, patients with T2DM [glycosylated hemoglobin (HbA1c) ≥ 7.5%] were enrolled from 39 sites in India. Patients received once-daily doses of either saroglitazar or pioglitazone (1:1:1 allocation ratio) for a total of 24 weeks. Patients were continued in a double blind extension period for an additional 32 weeks. Efficacy evaluations of glycemic parameters [HbA1c (Primary endpoint at week 24), FPG and PPG] and other lipid parameters (TG, LDL-C, VLDL-C, HDL-C, TC, Non HDL-C, Apo A1 and Apo B) were conducted at week 12, 24 and 56 and compared to the baseline levels. The efficacy analyses were performed by using paired t-test and ANCOVA model. Results A total of 1155 patients were enrolled in this study. The baseline characteristics were similar between the three treatment groups. The within group mean (± SD) change in HbA1c (%) from baseline of the saroglitazar (2 mg and 4 mg) and pioglitazone treatment groups at week 24 were: − 1.38 ± 1.99 for saroglitazar 2 mg; − 1.47 ± 1.92 for saroglitazar 4 mg and − 1.41 ± 1.86 for pioglitazone, respectively. Statistically significant reduction from baseline in HbA1c was observed in each treatment group at week 24 with p-value < 0.016. There was a significant reduction in TG, LDL-C, VLDL-C, TC and Non HDL-C with a significant increase in HDL-C from baseline levels (< 0.016). Most of the AE’s were ‘mild’ to ‘moderate’ in severity and were resolved by the completion of the study. Conclusions Saroglitazar effectively improved glycemic control and lipid parameters over 56 weeks in patients of T2DM receiving background metformin therapy and has a promising potential to reduce the cardiovascular risk in T2DM patients. Trial registration CTRI/2015/09/006203, dated 22/09/2015

In this work, cobalt-doped Cadmium oxide thin films are prepared by sol-gel spin coating technique on the glass substrate. The effects of annealing temperature and Co concentration on Structural, optical properties of the thin films are studied. XRD pattern indicates that a CdO single phase with a cubic polycrystalline structure is formed in all the samples. Prepared thin film samples were revealed by EDX analysis. Optical measurements show that the optical transmission of the layer is reduced and the optical-band gap decreases due to the increase in molar concentrations. The optical-band gap is decreasing while increasing of Co concentration and annealing temperature.

More than half of patients with oral cancer recur even after multimodality treatment and recurrent oral cancers carry a poorer prognosis when compared to other sites of head and neck. The best survival outcome in a recurrent setting is achieved by salvage surgery; however, objective criteria to select an ideal candidate for salvage surgery is difficult to frame, as the outcome depends on various treatment-, tumor-, and patient-related factors. The following is summarizes various tumor- and treatment-related factors that guide our decision-making to optimize oncologic and functional outcomes in surgical salvage for recurrent oral cancers. Short disease-free interval, advanced tumor stage (recurrent and primary), extracapsular spread and positive tumor margins in a recurrent tumor, regional recurrence, and multimodality treatment of primary tumor all portend worse outcomes after surgical salvage. Quality of life after surgical intervention has shown improvement over 1 year with a drastic drop in pain scores. Various trials are underway evaluating the combination of immunotherapy and surgical salvage in recurrent head and neck tumors, including oral cavity, which may widen our indications for salvage surgery with improved survival and preserved organ function.

Family and twin studies suggest that genes play a role in male sexual orientation. We conducted a genome-wide association study (GWAS) of male sexual orientation on a primarily European ancestry sample of 1,077 homosexual men and 1,231 heterosexual men using Affymetrix single nucleotide polymorphism (SNP) arrays. We identified several SNPs with p  < 10 −5 , including regions of multiple supporting SNPs on chromosomes 13 (minimum p  = 7.5 × 10 −7 ) and 14 ( p  = 4.7 × 10 −7 ). The genes nearest to these peaks have functions plausibly relevant to the development of sexual orientation. On chromosome 13, SLITRK6 is a neurodevelopmental gene mostly expressed in the diencephalon, which contains a region previously reported as differing in size in men by sexual orientation. On chromosome 14, TSHR genetic variants in intron 1 could conceivably help explain past findings relating familial atypical thyroid function and male homosexuality. Furthermore, skewed X chromosome inactivation has been found in the thyroid condition, Graves’ disease, as well as in mothers of homosexual men. On pericentromeric chromosome 8 within our previously reported linkage peak, we found support ( p  = 4.1 × 10 −3 ) for a SNP association previously reported (rs77013977, p  = 7.1 × 10 −8 ), with the combined analysis yielding p  = 6.7 × 10 −9 , i.e., a genome-wide significant association.

Histopathology analysis plays a pivotal role in the detection and classification of breast cancer, offering crucial insights for precise diagnosis and treatment planning. While recent advancements in deep learning architectures have shown promise in breast cancer detection, they often face limitations due to dataset constraints, rendering them less adaptable to different datasets. To address this issue, this research introduces a novel architecture called the Deep Ensemble Graph Network (DEGN). DEGN encompasses distinct stages and architectures to bolster the model’s robustness. The architecture comprises a Structural Graph Module (SGM) responsible for identification and feature extraction. This module is integrated into an ensemble graph network, which combines the SGM with graph-based representations for classification. The effectiveness of DEGN is evaluated using two distinct datasets: the BCSS (Breast Cancer Semantic Segmentation) dataset for binary classification and the BACH (Breast Cancer Histology dataset) dataset for both binary and multiclass classification tasks. DEGN’s performance is assessed and compared against state-of-the-art deep learning techniques, demonstrating its efficiency. The results indicate consistent enhancements for both datasets.

Breast Cancer is one of the fatal diseases and leading cause of mortality in women all over the world; moreover, early detection of breast cancer can minimize the risk of death, however accurate detection and classification of breast cancer is critical task. Histopathology is a technique used for a breast cancer diagnosis; histopathological images comprise rich phenotypic information which is utilized for monitoring underlying techniques contributing to patient survival and disease progression outcomes. In recent years, deep learning has achieved success in the medical domain, and further, it has become a primary methodological choice for interpreting and analyzing histology images. The existing approach of histopathological image classification requires a huge amount of labeled data to achieve satisfactory results which face the challenge due to limited annotated data due to cost constraints. A promising mechanism is required to be designed for binary classification; Thus in this research work, Dual Deep Network architecture (DDNA) is designed for lesion identification and binary classification; Dual Deep Network comprises two novel networks i.e. PSNet1 and PSNet2; PSNet1 is designed to extract the dynamic feature and identify the lesion. PSNet2 is designed for binary classification using the PSNet1 feature; further attention module is used for feature mapping and enhancing the feature extraction and network optimization is carried out to enhance the performance. DDNA is evaluated on the BreakHis dataset on image level and patient-level considering the different metrics; also comparative analysis is carried out with various deep learning techniques and varying magnification factors as 40X, 100X, 200X, and 400X. Moreover, the evaluation shows the model’s efficiency which ranges between 99 and 100% considering image level and patient level.

Global warming is causing increased temperatures that are affecting crop yield and sustainability. To harness the diversity present in rice germplasm, 1100 rice genotypes from the north-eastern Himalayan region (NEHR) were evaluated for high yield stability and marker-trait associations using SSR markers under ambient and elevated temperature conditions in the CO2-temperature gradient chamber (CTGC). Compared to ambient temperature, most agro-morphological traits showed a decreasing trend under elevated temperature, while PCV, GCV, heritability, and GAM tended to increase with elevated temperature. Pollen fertility percentage ranged from 5.4 to 99.4% at ambient temperature and from 3.4 to 98.6% at elevated temperature. The yield per plant (YPP) was significantly positively associated with traits like plant height (PH), panicle length (PL), number of tillers per plant (NT), number of effective tillers per plant (NET), number of filled grains per panicle (NFGPP), spikelet fertility percentage (SFP), and panicle weight (PW), under ambient and elevated temperature conditions. The polymorphism information content (PIC) values of markers associated with heat tolerance ranged from 0.02 to 0.77, with an average of 0.26. A total of 91 alleles were detected, ranging from 2 (RM570, RM225) to 11 (RM364), with an average of 3.14 alleles per marker. The AMOVA results showed that a significant percentage of variation was attributed to the genotypes within the population (67.50%). Based on the FST value (6.42%), genotypes from Meghalaya and Tripura were significantly differed from those outside the NEHR. Model-based clustering revealed that 86 genotypes had more than 80% genetic purity to their respective ancestry. In comparison, seven genotypes (Sojkyrleh, RCPL 1–123, RCPL 1–136, RCPL 1–74, RCPL 1–188, Sialkathi and Bapnah) had less than 80% genetic purity and were considered admixtures. Two markers, RM234 and RM7364, were found to be significantly associated with yield per plant under elevated temperature conditions using both the general linear model (GLM) and mixed linear model (MLM) approach. Genotypes such as TRC-2016–291, TRC-2007–1, TRC-2016–426, Daya, Badjhania, TRC-2016–624, TRC-2016–33, Nania, TRC-2016–51, Aati Ghal and TRC-2016–153, which exhibited superior performance for yield and component traits were selected as high yielding for the elevated temperature conditions of NEHR.

Human genome sequencing has transformed our understanding of genomic variation and its relevance to health and disease, and is now starting to enter clinical practice for the diagnosis of rare diseases. The question of whether and how some categories of genomic findings should be shared with individual research participants is currently a topic of international debate, and development of robust analytical workflows to identify and communicate clinically relevant variants is paramount. The Deciphering Developmental Disorders (DDD) study has developed a UK-wide patient recruitment network involving over 180 clinicians across all 24 regional genetics services, and has performed genome-wide microarray and whole exome sequencing on children with undiagnosed developmental disorders and their parents. After data analysis, pertinent genomic variants were returned to individual research participants via their local clinical genetics team. Around 80 000 genomic variants were identified from exome sequencing and microarray analysis in each individual, of which on average 400 were rare and predicted to be protein altering. By focusing only on de novo and segregating variants in known developmental disorder genes, we achieved a diagnostic yield of 27% among 1133 previously investigated yet undiagnosed children with developmental disorders, whilst minimising incidental findings. In families with developmentally normal parents, whole exome sequencing of the child and both parents resulted in a 10-fold reduction in the number of potential causal variants that needed clinical evaluation compared to sequencing only the child. Most diagnostic variants identified in known genes were novel and not present in current databases of known disease variation. Implementation of a robust translational genomics workflow is achievable within a large-scale rare disease research study to allow feedback of potentially diagnostic findings to clinicians and research participants. Systematic recording of relevant clinical data, curation of a gene–phenotype knowledge base, and development of clinical decision support software are needed in addition to automated exclusion of almost all variants, which is crucial for scalable prioritisation and review of possible diagnostic variants. However, the resource requirements of development and maintenance of a clinical reporting system within a research setting are substantial. Health Innovation Challenge Fund, a parallel funding partnership between the Wellcome Trust and the UK Department of Health.