Search Results (10)

Objectives: Few studies have examined the role of prostate MRI in patients with suspicious digital rectal examination (DRE) and/or PSA > 10 ng/mL. In a cohort of non-screened patients with suspicious DRE, we investigated the rate of avoidable prostate biopsies and potentially missed clinically significant prostate cancer (csPCa) with negative MRI, and the concordance between targeted biopsy (TBx) and systematic biopsy (SBx) in detecting csPCa with positive MRI. Methods: We retrospectively examined 199 biopsy-naïve patients with suspicious DRE who underwent prostate MRI before transperineal biopsy. Prostate Imaging—Reporting and Data System (PI-RADS) v2.1 ≥ 3 category of the index lesion defined a positive MRI. Combined TBx/SBx and SBx alone were performed for positive and negative MRI, respectively. An International Society of Urogenital Pathology Grade Group ≥ 2 defined csPCa. We calculated the csPCa detection rate of TBx, SBx, and combined TBx/SBx. The Cohen kappa statistic was used to measure the concordance between TBx and SBx. Results: Ninety-one (45.7%) csPCa cases were detected. MRI was positive in 153 (76.9%) patients. In the 46 patients with negative MRI, SBx detected 5 (10.9%) csPCa cases. Prostate biopsy could, thus, be avoided in 41/199 (20.6%) patients at the cost of missing 5/91 (5.5%) csPCa cases. The concordance between TBx and SBx in detecting csPCa with positive mpMRI was substantial (k = 0.70). Specifically, 6/86 (6.9%) csPCa cases were detected with TBx, and 17/86 (19.7%) with SBx alone. Concordance was almost perfect (k = 0.82) in patients with PSA > 10 ng/mL. Only 4/38 (10.5%) csPCa cases were missed by TBx, and only 1 (2.6%) csPCa case was identified by TBx alone. Conclusions: MRI in patients with suspicious DRE could avoid roughly 21% of unnecessary biopsies at the cost of missing approximately 6% of csPCa cases. Moreover, MRI and TBx complemented SBx in detecting csPCa in the subgroup with PSA > 10 ng/mL. Full article

T-box genes encode transcription factors, which control developmental processes and promote cancer if deregulated. Recently, we described the lymphoid TBX-code, which collates T-box gene activities in normal lymphopoiesis, enabling identification of members deregulated in lymphoid malignancies. Here, we have extended this analysis to cover myelopoiesis, compiling the myeloid TBX-code and, thus, highlighting which of these genes might be deregulated in myeloid tumor types. We analyzed public T-box gene expression datasets bioinformatically for normal and malignant cells. Candidate T-box-gene-expressing model cell lines were identified and examined by RQ-PCR, Western Blotting, genomic profiling, and siRNA-mediated knockdown combined with RNA-seq analysis and live-cell imaging. The established myeloid TBX-code comprised 10 T-box genes, including progenitor-cell-restricted TBX1. Accordingly, we detected aberrant expression of TBX1 in 10% of stem/progenitor-cell-derived chronic myeloid leukemia (CML) patients. The classic CML cell line K-562 expressed TBX1 at high levels and served as a model to identify TBX1 activators, including transcription factor GATA1 and genomic amplification of the TBX1 locus at 22q11; inhibitors, including BCR::ABL1 fusion and downregulated GNAI2, as well as BMP, FGF2, and WNT signaling; and the target genes CDKN1A, MIR17HG, NAV1, and TMEM38A. The establishment of the myeloid TBX-code permitted identification of aberrant TBX1 expression in subsets of CML patients and cell lines. TBX1 forms an integral part of an oncogenic regulatory network impacting proliferation, survival, and differentiation. Thus, the data spotlight novel diagnostic markers and potential therapeutic targets for this malignancy. Full article

In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model’s performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model’s performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications. Full article

Cancer is a genomic disease, with driver mutations contributing to tumorigenesis. These potentially heritable variants influence risk and underlie familial breast cancer (BC). This study evaluated associations between BC risk and 13 SNPs in driver genes MAP3K1, SF3B1, SMAD4, ARID2, ATR, KMT2C, MAP3K13, NCOR1, and TBX3, in BRCA1/2-negative Chilean families. SNPs were genotyped using TaqMan Assay in 492 cases and 1285 controls. There were no associations between rs75704921:C>T (ARID2); rs2229032:A>C (ATR); rs3735156:C>G (KMT2C); rs2276738:G>C, rs2293906:C>T, rs4075943T:>A, rs13091808:C>T (MAP3K13); rs178831:G>A (NCOR1); or rs3759173:C>A (TBX3) and risk. The MAP3K1 rs832583 A allele (C/A+A/A) showed a protective effect in families with moderate BC history (OR = 0.7 [95% CI 0.5–0.9] p = 0.01). SF3B1 rs16865677-T (G/T+T/T) increased risk in sporadic early-onset BC (OR = 1.4 [95% CI 1.0–2.0] p = 0.01). SMAD4 rs3819122-C (A/C+C/C) increased risk in cases with moderate family history (OR = 2.0 [95% CI 1.3–2.9] p ≤ 0.0001) and sporadic cases diagnosed ≤50 years (OR = 1.6 [95% CI 1.1–2.2] p = 0.006). SMAD4 rs12456284:A>G increased BC risk in G-allele carriers (A/G + G/G) in cases with ≥2 BC/OC cases and early-onset cases (OR = 1.2 [95% CI 1.0–1.6] p = 0.04 and OR = 1.4 [95% CI 1.0–1.9] p = 0.03, respectively). Our study suggests that specific germline variants in driver genes MAP3K1, SF3B1, and SMAD4 contribute to BC risk in Chilean population. Full article

Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those pretrained on stock photography images. This character helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localization, postprocessed into an ROI mask, from a DL classifier trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections, including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution and cross-institutional collections (p < 0.05). We believe that this is the first study to i) use CXR modality-specific U-Nets for semantic segmentation of TB-consistent ROIs and ii) evaluate the segmentation performance while augmenting the training data with weak TB-consistent localizations. Full article

Tm-Tb co-substituted Co-Ni nanospinel ferrites (NSFs) as (Co_0.5Ni_0.5) [Tm_xTb_xFe_2−2x]O₄ (x = 0.00–0.05) NSFs were attained via the ultrasound irradiation technique. The phase identification and morphologies of the NSFs were explored using X-rays diffraction (XRD), selected area electron diffraction (SAED), and transmission and scanning electronic microscopes (TEM and SEM). The magnetization measurements against the applied magnetic field (M-H) were made at 300 and 10 K with a vibrating sample magnetometer (VSM). The various prepared nanoparticles revealed a ferrimagnetic character at both 300 and 10 K. The saturation magnetization (M_s), the remanence (M_r), and magneton number ($n_B$) were found to decrease upon the Tb-Tm substitution effect. On the other hand, the coercivity (H_c) was found to diminish with increasing x up to 0.03 and then begins to increase with further rising Tb-Tm content. The H_c values are in the range of 346.7–441.7 Oe at 300 K to 4044.4–5378.7 Oe at 10 K. The variations in magnetic parameters were described based on redistribution of cations, crystallites and/or grains size, canting effects, surface spins effects, super-exchange interaction strength, etc. The observed magnetic results indicated that the synthesized (Co_0.5Ni_0.5)[Tm_xTb_xFe_2−x]O₄ NSFs could be considered as promising candidates to be used for room temperature magnetic applications and magnetic recording media. Full article

The potential role of miRNAs in the silencing mechanisms of pituitary neuroendocrine tumors (PitNETs) has not been addressed. The aim of the present study was to evaluate the expression levels and the potential associated role of some miRNAs, pathways, and transcription factors in the silencing mechanisms of corticotroph tumors (CTs). Accordingly, the expression of miR-375, miR-383, miR-488, miR-200a and miR-103; of PKA, MAP3K8, MEK, MAPK3, NGFIB, NURR1, PITX1, and STAT3 were analyzed via qRT-PCR in 23 silent and 24 functioning CTs. miR-200a and miR-103 showed significantly higher expression in silent than in functioning CTs, even after eliminating the bias of tumor size, therefore enabling the differentiation between the two variants. Additionally, miR-383 correlated negatively with TBX19 in silent CTs, a transcription factor related with the processing of POMC that can participate in the silencing mechanisms of CTs. Finally, the gene expression levels of miR-488, miR-200a, and miR-103 were significantly higher in macroadenomas (functioning and silent) than in microadenomas. The evidence from this study indicates that miRNAs could be involved in the pathophysiology of CTs. The translational implications of these findings suggest that pharmacological treatments specifically targeting these miRNAs could become a promising therapeutic option for these patients. Full article

The genetic variations responsible for tumorigenesis are called driver mutations. In breast cancer (BC), two studies have demonstrated that germline mutations in driver genes linked to sporadic tumors may also influence BC risk. The present study evaluates the association between SNPs and SNP-SNP interaction in driver genes TTN (rs10497520), TBX3 (rs2242442), KMT2D (rs11168827), and MAP3K1 (rs702688 and rs702689) with BC risk in BRCA1/2-negative Chilean families. The SNPs were genotyped in 489 BC cases and 1078 controls by TaqMan Assay. Our data do not support an association between rs702688: A>G or rs702689: G>A and BC risk. The rs10497520-T allele was associated with a decreased risk in patients with family history of BC or early-onset BC (OR = 0.6, p < 0.0001 and OR = 0.7, p = 0.05, respectively). rs2242442-G was associated with a protective effect and rs11168827-C was associated with increased BC risk in families with a strong history of BC (OR = 0.6, p = 0.02 and OR = 1.4, p = 0.05, respectively). As rs10497520-T and rs2242442-G seemed to protect against BC risk, we then evaluated their combined effect. Familial BC risk decreased in a dose-dependent manner with the protective allele count, reflecting an additive effect (p-trend < 10⁻⁴). To our knowledge, this is the first association study of BC driver gene germline variations in a Chilean population. Full article

In the pig industry, reproductive traits constantly influence the production efficiency. To identify markers and candidate genes underlying porcine reproductive traits, a genome-wide association study (GWAS) was performed in a Duroc pig population. In total, 1067 pigs were genotyped using single-nucleotide polymorphism (SNP) chips, and four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW), were examined. The results showed that 20 potential SNPs reached the level of suggestive significance and were associated with these traits of interest. Several important candidate genes, including TXN2, KCNA1, ENSSSCG00000003546, ZDHHC18, MAP2K6, BICC1, FAM135B, EPHB2, SEMA4D, ST3GAL1, KCTD3, FAM110A, TMEM132D, TBX3, and FAM110A, were identified and might compose the underlying genetic architecture of porcine reproductive traits. These findings help to understand the genetic basis of porcine reproductive traits and provide important information for molecular breeding in pigs. Full article

In this paper, the magnetocaloric properties of Gd_1−xTb_x alloys were studied and the optimum composition was determined to be Gd_0.73Tb_0.27. On the basis of Gd_0.73Tb_0.27, the influence of different Fe-doping content was discussed and the effect of heat treatment was also investigated. The adiabatic temperature change (ΔT_ad) obtained by the direct measurement method (under a low magnetic field of 1.2 T) and specific heat capacity calculation method (indirect measurement) was used to characterize the magnetocaloric properties of Gd_1−xTb_x (x = 0~0.4) and (Gd_0.73Tb_0.27)_1−yFe_y (y = 0~0.15), and the isothermal magnetic entropy (ΔS_M) was also used as a reference parameter for evaluating the magnetocaloric properties of samples together with ΔT_ad. In Gd_1−xTb_x alloys, the Curie temperature (T_c) decreased from 293 K (x = 0) to 257 K (x = 0.4) with increasing Tb content, and the Gd_0.73Tb_0.27 alloy obtained the best adiabatic temperature change, which was ~3.5 K in a magnetic field up to 1.2 T (T_c = 276 K). When the doping content of Fe increased from y = 0 to y = 0.15, the T_c of (Gd_0.73Tb_0.27)_1−yFe_y (y = 0~0.15) alloys increased significantly from 276 K (y = 0) to 281 K (y = 0.15), and a good magnetocaloric effect was maintained. The annealing of alloys (Gd_0.73Tb_0.27)_1−yFe_y (y = 0~0.15) at 1073 K for 10 h resulted in an average increase of 0.3 K in the maximum adiabatic temperature change and a slight increase in T_c. This study is of great significance for the study of magnetic refrigeration materials with adjustable Curie temperature in a low magnetic field. Full article