The spatial distribution of intermediate fibroblasts and myeloid-derived cells dictate lymph node metastasis dynamics in oral cancer.

BACKGROUND: Oral cancer poses a significant health challenge due to limited treatment protocols and therapeutic targets. We aimed to investigate the invasive margins of gingivo-buccal oral squamous cell carcinoma (GB-OSCC) tumors in terms of the localization of genes and cell types within the margins at various distances that could lead to nodal metastasis. METHODS: We collected tumor tissues from 23 resected GB-OSCC samples for gene expression profiling using digital spatial transcriptomics. We monitored differential gene expression at varying distances between the tumor and its microenvironvent (TME), and performed a deconvulation study and immunohistochemistry to identify the cells and genes regulating the TME. RESULTS: We found that the tumor-stromal interface (a distance up to 200 µm between tumor and immune cells) is the most active region for disease progression in GB-OSCC. The most differentially expressed apex genes, such as FN1 and COL5A1, were located at the stromal ends of the margins, and together with enrichment of the extracellular matrix (ECM) and an immune-suppressed microenvironment, were associated with lymph node metastasis. Intermediate fibroblasts, myocytes, and neutrophils were enriched at the tumor ends, while cancer-associated fibroblasts (CAFs) were enriched at the stromal ends. The intermediate fibroblasts transformed into CAFs and relocated to the adjacent stromal ends where they participated in FN1-mediated ECM modulation. CONCLUSION: We have generated a functional organization of the tumor-stromal interface in GB-OSCC and identified spatially located genes that contribute to nodal metastasis and disease progression. Our dataset might now be mined to discover suitable molecular targets in oral cancer.

The influential responsibility of sirtuins in senescence and associated diseases: A review.

Aging is a process of time-associated depletion in the physiological functions, essential for the survival and reproducibility of living beings. Some age-related disorders can be successfully controlled with some biomedical techniques or pharmaceutical approaches. There are some precise remedies that demonstrate conspicuous promise in the preclinical and clinical setup of extending lifespan or enhancing health by altering natural senescence. The sirtuin family of proteins is one of the most favorable targets for antiaging strategies. Sirtuins were initially identified as transcription repressors in yeast, but today they are known to exist in bacteria and eukaryotes, as well as humans. The SIRT (1-7) family of proteins in humans is made up of seven members, each of which has either mono-ADP ribosyl transferase or deacetylase activity. Researchers suggest that sirtuins are essential for cell metabolism and play a major role in how cells react to various stimuli, such as oxidative or genotoxic stress. A healthy lifestyle, which includes exercise and a balanced diet, has been demonstrated to impact health span by adjusting the levels of sirtuins, suggesting the involvement of sirtuins in extending human longevity. The hunt for sirtuin activators is among the most extensive and comprehensive research subjects in the present scenario. Some optimism has been generated to investigate antiaging therapies by natural compounds, such as curcumin and others. This review article highlights the role of sirtuins in native senescence and their primordial roles in the progression of several life-threatening diseases. Further, it also provides recent information on the sirtuin activators and inhibitors and their therapeutic benefits.

Demonstration of photonics-based D-band integrated localization and communication.

The terahertz spectrum has the ability to provide high-speed communication and millimeter-level resolution. As a result, terahertz-integrated sensing and communication (ISAC) has been identified as a key enabler for 6G wireless networks. This work discusses a photonics-based D-band communication system for integrated high-resolution localization and high-speed wireless communication. Our empirical results show that a communication rate of 5 Gbps over a distance of 1.5 m and location identification of the target with millimeter-level (<4m m) range resolution can be conducted simultaneously using the same signal. We also show that the error due to the thickness of the beam splitter can be eliminated, while the quantization error and the random drift errors are the limiting factors of the resolution achieved. This experimental demonstration using D-band communication indicates that terahertz ISAC can be realized for 6G networks while considering the underlying system restrictions (e.g., bandwidth limit and lens diameter).

Terahertz imaging: a diagnostic technology for prevention of grass seed infestation.

One of the most significant problems the Australian sheep and lamb industry faces today is grass seed infestation (GSI), which occurs when seeds accumulate in the sheep's fleece and penetrate the skin, causing infection. Meat & Livestock Australia estimates that the yearly losses caused due to GSI are around AUD$47.5 M (in Australia alone). Here, we demonstrate that terahertz spectroscopy and imaging can be utilized for early detection of GSI. This is possible because terahertz waves can penetrate through sheep wool and have the appropriate wavelength for identifying the seed. Moreover, terahertz waves have non-invasive and non-ionizing properties and are ideal for non-contact and standoff detection. This work demonstrates that terahertz waves can be utilized for the early detection of seeds in the animal fleece or on the pelt as a precursor tool for the prevention of GSI.

Nested DWT-Based CNN Architecture for Monocular Depth Estimation.

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill-posed and non-linear. Such networks are computationally and time-wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder-decoder network based on discrete wavelet decomposition and trainable coefficients (LL, LH, HL, HH). Our Nested Wavelet-Net (NDWTN) preserves the high-frequency information that is otherwise lost during the downsampling process in the encoder. Furthermore, we study the effect of activation functions, batch normalization, convolution layers, skip, etc., in our models. The network is trained with NYU datasets. Our network trains faster with good results.

Co-occurrence patterns and environmental factors associated with rapid onset of Microcystis aeruginosa bloom in a tropical coastal lagoon.

The environmental factors contributing to the Microcystis aeruginosa bloom (hereafter referred to as Microcystis bloom) are still debatable as they vary with season and geographic settings. We examined the environmental factors that triggered Microcystis bloom outbreak in India's largest brackish water coastal lagoon, Chilika. The warmer water temperature (25.31-32.48 °C), higher dissolved inorganic nitrogen (DIN) loading (10.15-13.53 µmol L-1), strong P-limitation (N:P ratio 138.47-246.86), higher water transparency (46.62-73.38 cm), and low-salinity (5.45-9.15) exerted a strong positive influence on blooming process. During the bloom outbreak, M. aeruginosa proliferated, replaced diatoms, and constituted 70-88% of the total phytoplankton population. The abundances of M. aeruginosa increased from 0.89 × 104 cells L-1 in September to 1.85 × 104 cells L-1 in November and reduced drastically during bloom collapse (6.22 × 103 cells L-1) by the late November of year 2017. The decrease in M. aeruginosa during bloom collapse was associated with a decline in DIN loading (2.97 µmol L-1) and N:P ratio (73.95). Sentinel-3 OLCI-based satellite monitoring corroborated the field observations showing Cyanophyta Index (CI) > 0.01 in September, indicative of intense bloom and CI < 0.0001 during late November, suggesting bloom collapse. The presence of M. aeruginosa altered the phytoplankton community composition. Furthermore, co-occurrence network indicated that bloom resulted in a less stable community with low diversity, inter-connectedness, and prominence of a negative association between phytoplankton taxa. Variance partitioning analysis revealed that TSM (16.63%), salinity (6.99%), DIN (5.21%), and transparency (5.15%) were the most influential environmental factors controlling the phytoplankton composition. This study provides new insight into the phytoplankton co-occurrences and combination of environmental factors triggering the rapid onset of Microcystis bloom and influencing the phytoplankton composition dynamics of a large coastal lagoon. These findings would be valuable for future bloom forecast modeling and aid in the management of the lagoon.

Giant Congenital Melanocytic Nevus with Congenital Neurofibroma: A Case Report.

BACKGROUND: Giant congenital melanocytic nevus (GCMN) is characterized by its large size and potential for transformation into melanoma. It can be associated with other neural cristopathies, including neurofibroma, however, it has not previously been described with a congenital neurofibroma. CASE REPORT: A newborn girl presented with a large congenital neurofibroma arising in a bathing trunk type of giant congenital melanocytic nevus. CONCLUSION: Congenital neurofibromas can be associated with (or a component of) a GCMN.

BERM: a Belowground Ecosystem Resiliency Model for estimating Spartina alterniflora belowground biomass.

Spatiotemporal patterns of Spartina alterniflora belowground biomass (BGB) are important for evaluating salt marsh resiliency. To solve this, we created the BERM (Belowground Ecosystem Resiliency Model), which estimates monthly BGB (30-m spatial resolution) from freely available data such as Landsat-8 and Daymet climate summaries. Our modeling framework relied on extreme gradient boosting, and used field observations from four Georgia salt marshes as ground-truth data. Model predictors included estimated tidal inundation, elevation, leaf area index, foliar nitrogen, chlorophyll, surface temperature, phenology, and climate data. The final model included 33 variables, and the most important variables were elevation, vapor pressure from the previous four months, Normalized Difference Vegetation Index (NDVI) from the previous five months, and inundation. Root mean squared error for BGB from testing data was 313 g m-2 (11% of the field data range), explained variance (R2 ) was 0.62-0.77. Testing data results were unbiased across BGB values and were positively correlated with ground-truth data across all sites and years (r = 0.56-0.82 and 0.45-0.95, respectively). BERM can estimate BGB within Spartina alterniflora salt marshes where environmental parameters are within the training data range, and can be readily extended through a reproducible workflow. This provides a powerful approach for evaluating spatiotemporal BGB and associated ecosystem function.

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments.

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR-SWIR, 400-2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450-520 nm) and NIR (band 4; 770-900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Structurally modified compounds of hydroxychloroquine, remdesivir and tetrahydrocannabinol against main protease of SARS-CoV-2, a possible hope for COVID-19: Docking and molecular dynamics simulation studies.

Now a days, more than 200 countries faces the health crisis due to epidemiological disease COVID-19 caused by SARS-CoV-2 virus. It will cause a very high impact on world's economy and global health sector. Earlier the structure of main protease (Mpro) protein was deposited in the RCSB protein repository. Hydroxychloroquine (HCQ) and remdesivir were found to effective in treatment of COVID-19 patients. Here we have performed docking and molecule dynamic (MD) simulation study of HCQ and remdesivir with Mpro protein which gave promising results to inhibit Mpro protein in SARS-CoV-2. On the basis of results obtained we designed structurally modified 18 novel derivatives of HCQ, remdesivir and tetrahydrocannabinol (THC) and carried out docking studies of all the derivatives. From the docking studies six molecules DK4, DK7, DK10, DK16, DK17 and DK19 gave promising results and can be use as inhibitor for Mpro of SARS-CoV-2 to control COVID-19 very effectively. Further, molecular dynamics simulation of one derivative of HCQ and one derivative of tetrahydrocannabinol showing excellent docking score was performed along with the respective parent molecules. The two derivatives gave excellent docking score and higher stability than the parent molecule as validated with molecular dynamics (MD) simulation for the binding affinities towards Mpro of SARS-CoV-2 thus represented as strong inhibitors at very low concentration.

Efficacy of ondansetron and palonosetron in prevention of shivering under spinal anesthesia: A prospective randomized double-blind study in patients undergoing elective LSCS.

BACKGROUND AND AIMS: Postanesthesia shivering (PAS) is a common, distressing experience. Ondansetron, the classical 5HT3 antagonist has been in use for its prevention since long. Palonosetron, a newly introduced potent antiemetic drug with better pharmacodynamics is currently in use by clinicians. Hence, a study was conducted to compare the efficacy of ondansetron and palonosetron in preventing PAS in patients undergoing elective lower segment caesarean section (LSCS) under spinal anaesthesia. MATERIAL AND METHODS: A total of 84 patients scheduled for elective LSCS under spinal anesthesia were randomly allocated to one of the two study groups (Group O & P). Accordingly, 8 mg of ondansetron or 0.075 mg palonosetron was administered in the same volume intravenously 30 min preoperatively. Sublingual temperature was recorded regularly. All patients were observed for 90 min postspinal for PAS. Observations were analyzed statiscally. RESULTS: No statistically significant intergroup difference was observed in the duration of surgery, and sublingual temperature. However, statistically significant difference was recorded for PAS (23.8% in ondansetron group, 9.5% in palonosetron group). CONCLUSION: Prophylactic administration of palonosetron significantly reduced incidence of PAS compared to ondansetron. However, further studies with larger sample size and more heterogeneous groups are suggested.

Translationally controlled tumor protein (TCTP) is required for TGF-ß1 induced epithelial to mesenchymal transition and influences cytoskeletal reorganization.

Epithelial-mesenchymal transition (EMT) is a programed course of developmental changes resulting in the acquisition of invasiveness and mobility in cells. In cancer, this course is used by epithelial cells to attain movability. Translationally controlled tumor protein (TCTP) has been extensively characterized following the observation on tumor reversion ensuing its depletion. However, the role of TCTP in cancer progression is still elusive. Here, we demonstrate for the first time that TCTP is a target of transforming growth factor-ß1 (TGF-ß1), a key regulator of EMT in A549 cells. We here present changes in expression patterns of intermediate filament markers (vimentin and cytokeratin 18a) of EMT following TCTP knockdown or over expression. The TCTP over-expression in cancer cells is associated with mesenchymal characters, while downregulation promotes the epithelial markers in the cells. Interaction of TCTP with ß-catenin seems to stabilize ß-catenin, preparative to its nuclear localization highlighting a role for ß-catenin signaling in EMT. Moreover, the induction of urokinase plasminogen activator (uPA) following ectopic expression of TCTP leads to destabilization of ECM. The cells knocked down for TCTP show diminished invasiveness and migration under TGF-ß1 treatment. The present results for the first time demonstrate that TGF-ß1 dependent TCTP expression is required for EMT in cells.

Efficacy and safety of a bortezomib and reduced-intensity cytarabine-based protocol, TMC ALLR1, for relapsed childhood ALL in India.

The feasibility of bortezomib (BZB) in induction and reduced cytarabine doses in intensification was evaluated in children with relapsed acute lymphoblastic leukaemia (rALL) at a single centre in India. Of 55 children with rALL, 23 received supportive care and 7 refused treatment, with a median survival of 2 (interquartile range 1-6) months. Twenty-two (88%) of 25 children who were treated achieved second remission and 9 (69%) of 13 had end-of-induction minimal residual disease of <10-4 . The lower cytarabine dose was associated with decreased hospitalisation. One-year event-free and overall survival for the treated group was 74·7% (95% confidence interval 52-88) and 79·6% (58-91) respectively.

Zebrafish model of amyloid light chain cardiotoxicity: regeneration versus degeneration.

Cardiac dysfunction is the most frequent cause of morbidity and mortality in amyloid light chain (AL) amyloidosis caused by a clonal immunoglobulin light chain (LC). Previously published transgenic animal models of AL amyloidosis have not recapitulated the key phenotype of cardiac dysfunction seen in AL amyloidosis, which has limited our understanding of the disease mechanisms in vivo, as well as the development of targeted AL therapeutics. We have developed a transgenic zebrafish model in which a λ LC derived from a patient with AL amyloidosis is conditionally expressed in the liver under the control of the Gal4 upstream activation sequence enhancer system. Circulating LC levels of 125 µg/ml in these transgenic zebrafish are comparable to median pathological serum LC levels. Functional analysis links abnormal contractile function with evidence of cellular and molecular proteotoxicity in the heart, including increased cell death and autophagy. However, despite pathological and functional phenotypes analogous to human AL, the lifespan of the transgenic fish is comparable to control fish without the expressed AL-LC transgene. Nuclear labeling experiments suggest increased cardiac proliferation in the transgenic fish, which can be counteracted by treatment with a small molecule proliferation inhibitor leading to increased zebrafish mortality because of cardiac apoptosis and functional deterioration. This transgenic zebrafish model provides a platform to study underlying AL disease mechanisms in vivo further. NEW & NOTEWORTHY Heart failure is a major cause of mortality in amyloid light (AL) amyloidosis, yet it has been difficult to model in animals. We report the generation of a transgenic zebrafish model for AL amyloidosis with pathological concentration of circulating human light chain protein that results in cardiac dysfunction. The light chain toxicity triggers regeneration in the zebrafish heart resulting in functional compensation early in life, but with age develops into cardiac dysfunction.

Screening of over 1000 Indian patients with breast and/or ovarian cancer with a multi-gene panel: prevalence of BRCA1/2 and non-BRCA mutations.

PURPOSE: Breast and/or ovarian cancers are among the most common cancers in women across the world. In the Indian population, the healthcare burden of breast and/or ovarian cancers has been steadily rising, thus stressing the need for early detection, surveillance, and disease management measures. However, the burden attributable to inherited mutations is not well characterized. METHODS: We sequenced 1010 unrelated patients and families from across India with an indication of breast and/or ovarian cancers, using the TruSight Cancer panel which includes 14 genes, strongly associated with risk of hereditary breast and/or ovarian cancers. Genetic variations were identified using the StrandNGS software and interpreted using the StrandOmics platform. RESULTS: We were able to detect mutations in 304 (30.1%) cases, of which, 56 mutations were novel. A majority (84.9%) of the mutations were detected in the BRCA1/2 genes as compared to non-BRCA genes (15.1%). When the cases were stratified on the basis of age at diagnosis and family history of cancer, the high rate of 75% of detection of hereditary variants was observed in patients whose age at diagnosis was below 40 years and had first-degree family member(s) affected by breast and/or ovarian cancers. Our findings indicate that in the Indian population, there is a high prevalence of mutations in the high-risk breast cancer genes: BRCA1, BRCA2, TP53, and PALB2. CONCLUSION: In India, socioeconomic inequality limiting access to treatment is a major factor towards increased cancer burden; therefore, incorporation of a cost-effective and comprehensive multi-gene test will be helpful in ensuring widespread implementation of genetic screening in the clinical practice for hereditary breast and/or ovarian cancers.

A triple-probe FISH screening strategy for risk-stratified therapy of acute lymphoblastic leukaemia in low-resource settings.

Karyotyping along with a 3-probe fluorescence in situ hybridization (FISH) strategy was used to risk stratify therapy in 303 children with B-cell precursor acute lymphoblastic leukaemia. Of the 166 patients risk stratified, karyotype identified 91 (55%). FISH identified all karyotypes accurately, with the exception of hypodiploidy, and risk stratified an additional 75 patients. The frequency of ETV6-RUNX1 is lower and high hyperdiploidy, higher than reported in the west. An adapted 3-probe FISH strategy identified two patients with ETV6-ABL1 fusion who received imatinib. In limited-resource settings, a 3-probe FISH approach provides a practical approach for risk-stratified therapy in childhood ALL.

Spatio-Temporal Dynamics of Inherent Optical Properties in Oligotrophic Northern Gulf of Mexico Estuaries.

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3's Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m-1 (13.95% NRMSE) for total absorption (a t ), 0.21 m-1 (7.61% NRMSE) for detritus-gelbstoff absorption (a dg ), and 0.09 m-1 (22.77% NRMSE) for phytoplankton pigment absorption (aphi). Results showed that absorption by detritus and gelbstoff (adg) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.

Cytopathological image analysis using deep-learning networks in microfluidic microscopy.

Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing process. Several neural network architectures were designed to provide human expertise to machines. In this paper, we explore and propose the feasibility of using deep-learning networks for cytopathologic analysis by performing the classification of three important unlabeled, unstained leukemia cell lines (K562, MOLT, and HL60). The cell images used in the classification are captured using a low-cost, high-throughput cell imaging technique: microfluidics-based imaging flow cytometry. We demonstrate that without any conventional fine segmentation followed by explicit feature extraction, the proposed deep-learning algorithms effectively classify the coarsely localized cell lines. We show that the designed deep belief network as well as the deeply pretrained convolutional neural network outperform the conventionally used decision systems and are important in the medical domain, where the availability of labeled data is limited for training. We hope that our work enables the development of a clinically significant high-throughput microfluidic microscopy-based tool for disease screening/triaging, especially in resource-limited settings.