ATI stabilized germylene cation as a cyanosilylation catalyst for aldehydes and ketones.

The aminotroponiminate (ATI) ligand stabilized germylene cation [(i-Bu)2ATIGe][B(C6F5)4] (2) is found to be an efficient low-valent main-group catalyst for the cyanosilylation of aldehydes and ketones (ATI = aminotroponiminate). It was synthesized by reacting [(i-Bu)2ATIGeCl] (1) with Na[B(C6F5)4]. The catalytic cyanosilylation of diverse aliphatic and aromatic carbonyl compounds (aldehydes and ketones) using 0.075-0.75 mol% of compound 2 was completed within 5-45 min. The catalytic efficiency seen with aliphatic aldehydes was around 15,800 h-1, making compound 2 a capable low-valent main-group catalyst for the aldehyde and ketone cyanosilylation reactions.

Endoscopic ultrasound-guided gastrointestinal anastomosis: Are we there yet?

Endoscopic ultrasound (EUS) is increasingly used as a therapeutic approach for gastrointestinal diseases, especially with the advent of lumen-apposing metal stents (LAMS). This has led to a rise in of EUS-guided gastrointestinal anastomosis procedures. Due to the reliability of intestinal conduits with LAMS, indications for EUS-guided gastrointestinal anastomosis are becoming more common and trend to potentially be standard care for gastric outlet obstruction, afferent loop syndrome, and EUS-directed transgastric interventions such as EUS-directed endoscopic retrograde cholangiopancreatography. Retrospective and prospective data indicate that the procedure is becoming widely adopted with promising outcomes. This article aims to review the existing literature on EUS-guided gastrointestinal anastomosis and predict its future developments.

Immunophenotypic characterization of leukemic stem cells in acute myeloid leukemia using single tube 10-colour panel by multiparametric flow cytometry: Deciphering the spectrum, complexity and immunophenotypic heterogeneity.

INTRODUCTION: Despite extensive research, comprehensive characterization of leukaemic stem cells (LSC) and information on their immunophenotypic differences from normal haematopoietic stem cells (HSC) is lacking. Herein, we attempted to unravel the immunophenotypic (IPT) characteristics and heterogeneity of LSC using multiparametric flow cytometry (MFC) and single-cell sequencing. MATERIALS AND METHODS: Bone marrow aspirate samples from patients with acute myeloid leukaemia (AML) were evaluated using MFC at diagnostic and post induction time points using a single tube-10-colour-panel containing LSC-associated antibodies CD123, CD45RA, CD44, CD33 and COMPOSITE (CLL-1, TIM-3, CD25, CD11b, CD22, CD7, CD56) with backbone markers that is, CD45, CD34, CD38, CD117, sCD3. Single-cell sequencing of the whole transcriptome was also done in a bone marrow sample. RESULTS: LSCs and HSCs were identified in 225/255 (88.2%) and 183/255 (71.6%) samples, respectively. Significantly higher expression was noted for COMPOSITE, CD45RA, CD123, CD33, and CD44 in LSCs than HSCs (p < 0.0001). On comparing the LSC specific antigen expressions between CD34+ (n = 184) and CD34- LSCs (n = 41), no difference was observed between the groups. More than one sub-population of LSC was demonstrated in 4.4% of cases, which further revealed high concordance between MFC and single cell transcriptomic analysis in one of the cases displaying three LSC subpopulations by both methods. CONCLUSION: A single tube-10-colour MFC panel is proposed as an easy and reproducible tool to identify and discriminate LSCs from HSCs. LSCs display both inter- and intra-sample heterogeneity in terms of antigen expressions, which opens the facets for single cell molecular analysis to elucidate the role of subpopulations of LSCs in AML progression.

Chemical-Vapor-Deposition-Synthesized Two-Dimensional Non-Stoichiometric Copper Selenide (ß-Cu2-xSe) for Ultra-Fast Tetracycline Hydrochloride Degradation under Solar Light.

The high concentration of antibiotics in aquatic environments is a serious environmental issue. In response, researchers have explored photocatalytic degradation as a potential solution. Through chemical vapor deposition (CVD), we synthesized copper selenide (ß-Cu2-xSe) and found it an effective catalyst for degrading tetracycline hydrochloride (TC-HCl). The catalyst demonstrated an impressive degradation efficiency of approximately 98% and a reaction rate constant of 3.14 × 10-2 min-1. Its layered structure, which exposes reactive sites, contributes to excellent stability, interfacial charge transfer efficiency, and visible light absorption capacity. Our investigations confirmed that the principal active species produced by the catalyst comprises O2- radicals, which we verified through trapping experiments and electron paramagnetic resonance (EPR). We also verified the TC-HCl degradation mechanism using high-performance liquid chromatography-mass spectrometry (LC-MS). Our results provide valuable insights into developing the ß-Cu2-xSe catalyst using CVD and its potential applications in environmental remediation.

True-T - Improving T-cell response quantification with holistic artificial intelligence based prediction in immunohistochemistry images.

The immune response associated with oncogenesis and potential oncological ther- apeutic interventions has dominated the field of cancer research over the last decade. T-cell lymphocytes in the tumor microenvironment are a crucial aspect of cancer's adaptive immunity, and the quantification of T-cells in specific can- cer types has been suggested as a potential diagnostic aid. However, this is cur- rently not part of routine diagnostics. To address this challenge, we present a new method called True-T, which employs artificial intelligence-based techniques to quantify T-cells in colorectal cancer (CRC) using immunohistochemistry (IHC) images. True-T analyses the chromogenic tissue hybridization signal of three widely recognized T-cell markers (CD3, CD4, and CD8). Our method employs a pipeline consisting of three stages: T-cell segmentation, density estimation from the segmented mask, and prediction of individual five-year survival rates. In the first stage, we utilize the U-Net method, where a pre-trained ResNet-34 is em- ployed as an encoder to extract clinically relevant T-cell features. The segmenta- tion model is trained and evaluated individually, demonstrating its generalization in detecting the CD3, CD4, and CD8 biomarkers in IHC images. In the second stage, the density of T-cells is estimated using the predicted mask, which serves as a crucial indicator for patient survival statistics in the third stage. This ap- proach was developed and tested in 1041 patients from four reference diagnostic institutions, ensuring broad applicability. The clinical effectiveness of True-T is demonstrated in stages II-IV CRC by offering valuable prognostic information that surpasses previous quantitative gold standards, opening possibilities for po- tential clinical applications. Finally, to evaluate the robustness and broader ap- plicability of our approach without additional training, we assessed the universal accuracy of the CD3 component of the True-T algorithm across 13 distinct solid tumors.

Recurrent tracheo-esophageal fistula- management challenges: Case report and review of current literature.

Recurrent tracheoesophageal fistula is a rare complication of esophageal atresia surgery with an incidence of 3-15%. The presentation is subtle and is often missed, presenting as choking episodes during feed and recurrent chest infections. It is both a diagnostic and management challenge and requires a dedicated multidisciplinary pediatric surgical setup with adequate infrastructure for optimal management. We present a case of recurrent tracheoesophageal fistula which was diagnosed at our center. The patient underwent successful surgical management and is thriving well at six months follow-up period.

COMFormer: Classification of Maternal-Fetal and Brain Anatomy Using a Residual Cross-Covariance Attention Guided Transformer in Ultrasound.

Monitoring the healthy development of a fetus requires accurate and timely identification of different maternal-fetal structures as they grow. To facilitate this objective in an automated fashion, we propose a deep-learning-based image classification architecture called the COMFormer to classify maternal-fetal and brain anatomical structures present in 2-D fetal ultrasound (US) images. The proposed architecture classifies the two subcategories separately: maternal-fetal (abdomen, brain, femur, thorax, mother's cervix (MC), and others) and brain anatomical structures [trans-thalamic (TT), trans-cerebellum (TC), trans-ventricular (TV), and non-brain (NB)]. Our proposed architecture relies on a transformer-based approach that leverages spatial and global features using a newly designed residual cross-variance attention block. This block introduces an advanced cross-covariance attention (XCA) mechanism to capture a long-range representation from the input using spatial (e.g., shape, texture, intensity) and global features. To build COMFormer, we used a large publicly available dataset (BCNatal) consisting of 12 400 images from 1792 subjects. Experimental results prove that COMFormer outperforms the recent CNN and transformer-based models by achieving 95.64% and 96.33% classification accuracy on maternal-fetal and brain anatomy, respectively.

Secondary metabolites from endophytic fungi: Production, methods of analysis, and diverse pharmaceutical potential.

The synthesis of secondary metabolites is a constantly functioning metabolic pathway in all living systems. Secondary metabolites can be broken down into numerous classes, including alkaloids, coumarins, flavonoids, lignans, saponins, terpenes, quinones, xanthones, and others. However, animals lack the routes of synthesis of these compounds, while plants, fungi, and bacteria all synthesize them. The primary function of bioactive metabolites (BM) synthesized from endophytic fungi (EF) is to make the host plants resistant to pathogens. EF is a group of fungal communities that colonize host tissues' intracellular or intercellular spaces. EF serves as a storehouse of the above-mentioned bioactive metabolites, providing beneficial effects to their hosts. BM of EF could be promising candidates for anti-cancer, anti-malarial, anti-tuberculosis, antiviral, anti-inflammatory, etc. because EF is regarded as an unexploited and untapped source of novel BM for effective drug candidates. Due to the emergence of drug resistance, there is an urgent need to search for new bioactive compounds that combat resistance. This article summarizes the production of BM from EF, high throughput methods for analysis, and their pharmaceutical application. The emphasis is on the diversity of metabolic products from EF, yield, method of purification/characterization, and various functions/activities of EF. Discussed information led to the development of new drugs and food additives that were more effective in the treatment of disease. This review shed light on the pharmacological potential of the fungal bioactive metabolites and emphasizes to exploit them in the future for therapeutic purposes.

Integrated single-cell transcriptome analysis of CD34 + enriched leukemic stem cells revealed intra- and inter-patient transcriptional heterogeneity in pediatric acute myeloid leukemia.

To gain insights into the idiosyncrasies of CD34 + enriched leukemic stem cells, we investigated the nature and extent of transcriptional heterogeneity by single-cell sequencing in pediatric AML. Whole transcriptome analysis of 28,029 AML single cells was performed using the nanowell cartridge-based barcoding technology. Integrated transcriptional analysis identified unique leukemic stem cell clusters of each patient and intra-patient heterogeneity was revealed by multiple LSC-enriched clusters differing in their cell cycle processes and BCL2 expression. All LSC-enriched clusters exhibited gene expression profile of dormancy and self-renewal. Upregulation of genes involved in non-coding RNA processing and ribonucleoprotein assembly were observed in LSC-enriched clusters relative to HSC. The genes involved in regulation of apoptotic processes, response to cytokine stimulus, and negative regulation of transcription were upregulated in LSC-enriched clusters as compared to the blasts. Validation of top altered genes in LSC-enriched clusters confirmed upregulation of TCF7L2, JUP, ARHGAP25, LPAR6, and PRDX1 genes, and serine/threonine kinases (STK24, STK26). Upregulation of LPAR6 showed trend towards MRD positive status (Odds ratio = 0.126; 95% CI = 0.0144-1.10; p = 0.067) and increased expression of STK26 significantly correlated with higher RFS (HR = 0.231; 95% CI = 0.0506-1.052; p = 0.04). Our findings addressed the inter- and intra-patient diversity within AML LSC and potential signaling and chemoresistance-associated targets that warrant investigation in larger cohort that may guide precision medicine in the near future.

Halophytes for the sustainable remediation of heavy metal-contaminated sites: Recent developments and future perspectives.

Increasing land degradation by high level of metal wastes is of prime concern for the global research communities. In this respect, halophytes having specific features like salt glands, exclusion of excess ions, heavy metals (HMs) compartmentalization, large pool of antioxidants, and associations with metal-tolerant microbes are of great promise in the sustainable clean-up of contaminated sites. However, sustainable clean-up of HMs by a particular halophyte plant species is governed considerably by physico-chemical characteristics of soil and associated microbial communities. The present review has shed light on the superiority of halophytes over non-halophytes, mechanisms of metal-remediation, recent developments and future perspectives pertaining to the utilization of halophytes in management of HM-contaminated sites with the aid of bibliometric analysis. The results revealed that the research field is receiving considerable attention in the last 5-10 years by publishing ∼50-90% documents with an annual growth rate of 15.41% and citations per document of 29.72. Asian (viz., China, India, and Pakistan) and European (viz., Spain, Portugal, Belgium, Argentina) countries have been emerged as the major regions conducting and publishing extensive research on this topic. The investigations conducted both under in vitro and field conditions have reflected the inherent potential of halophyte as sustainable research tool for successfully restoring the HM-contaminated sites. The findings revealed that the microbial association with halophytes under different challenging conditions is a win-win approach for metal remediation. Therefore, exploration of new halophyte species and associated microorganisms (endophytic and rhizospheric) from different geographical locations, and identification of genes conferring tolerance and phytoremediation of metal contaminants would further advance the intervention of halophytes for sustainable ecological restoration.

EVI1 upregulates PTGS1 (COX1) and decreases the action of tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia cells.

Tyrosine kinase inhibitors (TKIs) are highly effective in treating chronic myelogenous leukemia (CML). However, primary and acquired drug resistance to TKIs have been reported. In this study, we used RNA sequencing followed by RQ-PCR to show that the proto-oncogene EVI1 targets the drug-metabolizing gene PTGS1 in CML. The PTGS1 promoter element had an EVI1 binding site, and CHIP assay confirmed its presence. Data from a publicly available CML microarray dataset and an independent set of CML samples showed a significant positive correlation between EVI1 and PTGS1 expression in CML. Downregulation of EVI1 in K562 cells and subsequent treatment with TKIs resulted in a lower IC50 in the control cells. Furthermore, combined inhibition of BCR-ABL with imatinib and PTGS1 with FR122047 (PTGS1 inhibitor) synergistically reduced the viability of imatinib-resistant K562 cells. We conclude that elevated EVI1 expression contributes to TKIs resistance and that combined inhibition of PTGS1 and BCR-ABL may represent a novel therapeutic approach.

Industrial heat island mitigation in Angul-Talcher region of India: Evaluation using modified WRF-Single Urban Canopy Model.

Linkages of urban and industrial cooling with sustainable development goals and climate change perspectives are well acknowledged, mainly for developing economies in tropical climates. Angul-Talcher region is one of the oldest industrial clusters of India, and the region experiences higher atmospheric heat island intensities with magnitudes of 7 to 9 °C attributed to the Industrial Heat Island (IHI) effect. In the present study, various measures for mitigating heat island effect in the region and assessed their impact using an Improved Weather Research and Forecasting model coupled with the Single-Layer Urban Canopy Model. The improved framework includes the release of industrial emissions at stack height and sector-wise diurnal profiles of anthropogenic heat (AH) released from vehicles, residential, and industry/power. The mitigation measures comprised strategies like alteration in building materials and conversion of landuse-landcover (LULC) of selected grid cells in the model domain to more vegetation or water bodies. It was noted that the cool roofs and walls together reduced IHIs by 0.5 °C, while green roofs and cool pavements achieved a reduction of 0.3 °C and 0.1 °C, respectively. The introduction of water bodies showed maximum reduction in IHIs by 3 to 5 °C during daytime and 1 to 2 °C over mining and industrial stations. During night-time, conversion to mixed forests was more effective (ΔT ≈ 1 °C) than conversion to water bodies. A combination of cool roofs with the introduction of water bodies in the mining areas and mixed forest patches in industry stations was found to be the most effective mitigation strategy for mitigating the industrial heat island effect over the Angul-Talcher region. These mitigation scenarios can/should serve as a theoretical reference for implementing actual mitigation measures, which would require consideration of economic, social, and policy aspects apart from scientific ones for practical application.

Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network.

Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose a staining-invariant encoder (SIE) that includes convolution and transformers blocks. We also propose the WHDC block allowing the network to learn multi-scale nuclei-relevant features to handle the variation in the sizes and shapes of nuclei. The SIE network is trained on five unlabeled WSIs datasets using self-supervised contrastive learning and then used as a backbone for the downstream nuclei segmentation network. Our method outperforms existing approaches in challenging multiple WSI datasets without stain color normalization.

HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy.

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles' diameter is usually in the range of 2-10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

Stannylene cyanide and its use as a cyanosilylation catalyst.

Two routes can offer the first stannylene cyanide [(L)SnCN] (5); the substitution reaction of either stannylene amide [(i-Bu)2ATISnN(SiMe3)2] (3) or stannylene pyrrolide [(i-Bu)2ATISn(NC4H4)] (4) using an excess of trimethylsilyl cyanide (L = aminotroponiminate (ATI)). Using 0.1-2.0 mol% of compound 5, catalytic cyanosilylation of a variety of aliphatic and aromatic aldehydes was achieved at rt-50 °C in 0.33-2.0 h. The mechanism of this catalytic reaction is authenticated by the isolation of a structurally characterized intermediate.

ICOSeg: Real-Time ICOS Protein Expression Segmentation from Immunohistochemistry Slides Using a Lightweight Conv-Transformer Network.

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell's salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.

Stacked dilated convolutions and asymmetric architecture for U-Net-based medical image segmentation.

Deep learning has been widely utilized for medical image segmentation. The most commonly used U-Net and its variants often share two common characteristics but lack solid evidence for the effectiveness. First, each block (i.e., consecutive convolutions of feature maps of the same resolution) outputs feature maps from the last convolution, limiting the variety of the receptive fields. Second, the network has a symmetric structure where the encoder and the decoder paths have similar numbers of channels. We explored two novel revisions: a stacked dilated operation that outputs feature maps from multi-scale receptive fields to replace the consecutive convolutions; an asymmetric architecture with fewer channels in the decoder path. Two novel models were developed: U-Net using the stacked dilated operation (SDU-Net) and asymmetric SDU-Net (ASDU-Net). We used both publicly available and private datasets to assess the efficacy of the proposed models. Extensive experiments confirmed SDU-Net outperformed or achieved performance similar to the state-of-the-art while using fewer parameters (40% of U-Net). ASDU-Net further reduced the model parameters to 20% of U-Net with performance comparable to SDU-Net. In conclusion, the stacked dilated operation and the asymmetric structure are promising for improving the performance of U-Net and its variants.

Response to Visceral Leishmaniasis Cases through Active Case Detection and Vector Control in Low-Endemic Hilly Districts of Nepal.

The visceral leishmaniasis (VL) elimination program in Nepal has largely completed the attack phase and is moving toward consolidation and maintenance phases. New VL foci are, however, appearing in Nepal, and therefore new innovative community-centered strategies need to be developed and tested. We conducted early case detection by an index case-based approach and assessed the feasibility, efficacy, and cost of an intervention for sandfly control through indoor residual spraying (IRS) or insecticidal wall painting (IWP) in new and low-endemic districts Palpa and Surkhet. IRS was performed in 236 households and IWP in 178 households. We screened 1,239 and 596 persons in Palpa and Surkhet, respectively, resulting in the detection of one VL case in Palpa. Both IWP and IRS were well accepted, and the percentage reductions in sandfly density after 1, 9, and 12 months of intervention were 90%, 81%, and 75%, respectively, for IWP and 81%, 59%, and 63% respectively for IRS. The cost per household protected per year was USD 10.3 for IRS and 32.8 for IWP, although over a 2-year period, IWP was more cost-effective than IRS. Active case detection combined with sandfly control through IWP or IRS can support to VL elimination in the consolidation and maintenance phase.

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences.

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

Targeting delta-endotoxin (Cry1Ac) of Bacillus thuringiensis to subcellular compartments increases the protein expression, stability, and biological activity.

Evolving insect resistance to delta-endotoxins can be delayed by using a few strategies like high dosage, refugia, and gene stacking which require the expression of delta-endotoxins at sufficiently high levels to kill the resistant insects. In this study, we comparatively analyzed the efficacy of targeting truncated cry1Ac protein to the cytoplasm, endoplasmic reticulum (ER), and chloroplast to obtain high protein expression. mRNA and protein profiling of cry1Ac showed that both ER and chloroplast are efficient targets for expressing high levels of truncated cry1Ac. A maximum of 0.8, 1.6, and 2.0% cry1Ac of total soluble protein were obtained when the truncated cry1Ac was expressed in the cytoplasm, routed through ER, and targeted to the chloroplast. We further showed that not only the protein content but also the biological activity of truncated cry1Ac increases by sub-cellular targeting and the biological activity is slightly greater in the ER routed transgenic lines by conducting different bioassays on Helicoverpa armigera. Using native Western analysis, we demonstrated that the truncated cry1Ac protein could exist as oligomers in plant cells and this oligomerization capability is low in the cytoplasm. In conclusion, routing of delta endotoxins through ER is the first choice to obtain high protein expression and bioactivity.