EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function.

PURPOSE: The purpose of this study is to develop an automated method using deep learning for the reliable and precise quantification of left ventricle structure and function from echocardiogram videos, eliminating the need to identify end-systolic and end-diastolic frames. This addresses the variability and potential inaccuracies associated with manual quantification, aiming to improve the diagnosis and management of cardiovascular conditions. METHODS: A single, fully automated multitask network, the EchoFused Network (EFNet) is introduced that simultaneously addresses both left ventricle segmentation and ejection fraction estimation tasks through cross-module fusion. Our proposed approach utilizes semi-supervised learning to estimate the ejection fraction from the entire cardiac cycle, yielding more dependable estimations and obviating the need to identify specific frames. To facilitate joint optimization, the losses from task-specific modules are combined using a normalization technique, ensuring commensurability on a comparable scale. RESULTS: The assessment of the proposed model on a publicly available dataset, EchoNet-Dynamic, shows significant performance improvement, achieving an MAE of 4.35% for ejection fraction estimation and DSC values of 0.9309 (end-diastolic) and 0.9135 (end-systolic) for left ventricle segmentation. CONCLUSIONS: The study demonstrates the efficacy of EFNet, a multitask deep learning network, in simultaneously quantifying left ventricle structure and function through cross-module fusion on echocardiogram data.

Child Sexual Abuse in Pakistan: A Phenomenological Study on Parental Understanding and Prevention Strategies for Child Protection.

Child Sexual Abuse (CSA) is widespread and pervasive in Pakistan, presenting a significant threat to a child's physical health, psychological well-being, and overall development. This study aimed to empirically substantiate the understanding of parents regarding CSA and their utilized preventive strategies. The study utilized Social Ecological Theory to gain a deeper comprehension of the phenomenon. Data was collected through in-depth semi-structured interviews with 18 parents from Islamabad capital city, using a qualitative phenomenological research approach along with thematic analysis. Results reveal that most of the parents were highly aware of the most common aspects of sexual assault and molestation, but they were unaware of the more manipulative subtle forms such as "grooming." Additionally, they had a limited understanding of the long-term consequences of sexual abuse. While many parents knew about the option of involving the police in cases of abuse, they were unaware of helpline services and the availability of psychological support. In terms of prevention, most parents employed an integrated approach involving various preventive measures, such as educating their children about appropriate and inappropriate touch. These findings underscore the importance of implementing public-focused initiatives and community-based programs to enhance parental awareness, knowledge, and capabilities in preventing CSA in Pakistan.

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods.

Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

Latcripin-7A from Lentinula edodes C91-3 induces apoptosis, autophagy, and cell cycle arrest at G1 phase in human gastric cancer cells via inhibiting PI3K/Akt/mTOR signaling.

Gastric cancer (G.C) is one of the most lethal cancer types worldwide. Current treatment requires surgery along with chemotherapy, which causes obstacles for speedy recovery. The discovery of novel drugs is needed for better treatment of G.C with minimum side effects. Latcripin-7A (LP-7A) is a newly discovered peptide extracted from Lentinula edodes. It is recently studied for its anti-cancer activity. In this study, LP-7A was modeled using a phyre2 server. Anti-proliferation effects of LP-7A on G.C cells were examined via CCK-8, colony formation, and morphology assay. Apoptosis of LP-7A treated G.C cells was evaluated via Hoechst Stain, western blot and flow cytometry. Autophagy was assessed via acridine orange staining and western blot. The cell cycle was assessed via flow cytometry assay and western blot. Pathway was studied via western blot and STRING database. Anti-migratory effects of LP-7A treated G.C cells were analyzed via wound healing, western blot, and migration and invasion assay. LP-7A effectively inhibited the growth of G.C cells by inhibiting the PI3K/Akt/mTOR pathway. G.C cells treated with LP-7A arrested the cell cycle at the G1 phase, contributing to the inhibition of migration and invasion. Furthermore, LP-7A induced apoptosis and autophagy in gastric cancer cells. These results indicated that LP-7A is a promising anti-cancer agent. It affected the proliferation and growth of G.C cells (SGC-7901 and BGC-823) by inducing apoptosis, autophagy, and inhibiting cell cycle at the G1 phase in G.C cells.

LP1 from Lentinula edodes C91-3 Induces Autophagy, Apoptosis and Reduces Metastasis in Human Gastric Cancer Cell Line SGC-7901.

Present study aimed to elucidate the anticancer effect and the possible molecular mechanism underlying the action of Latcripin 1 (LP1), from the mushroom Lentinula edodes strain C91-3 against gastric cancer cell lines SGC-7901 and BGC-823. Cell viability was measured by Cell Counting Kit-8 (CCK-8); morphological changes were observed by phase contrast microscope; autophagy was determined by transmission electron microscope and fluorescence microscope. Apoptosis and cell cycle were assessed by flow cytometer; wound-healing, transwell migration and invasion assays were performed to investigate the effect of LP1 on gastric cancer cell's migration and invasion. Herein, we found that LP1 resulted in the induction of autophagy by the formation of autophagosomes and conversion of light chain 3 (LC3I into LC3II. LP1 up-regulated the expression level of autophagy-related gene (Atg7, Atg5, Atg12, Atg14) and Beclin1; increased and decreased the expression level of pro-apoptotic (Bax) and anti-apoptotic (Bcl-2) proteins respectively, along with the activation of Caspase-3. At lower-doses, LP1 have shown to arrest cells in the S phase of the cell cycle and decreased the expression level of matrix metalloproteinase MMP-2 and MMP-9. In addition, it has also been shown to regulate the phosphorylation of one of the most hampered gastric cancer pathway, that is, protein kinase B/mammalian target of rapamycin (Akt/mTOR) channel and resulted in cell death. These findings suggested LP1 as a potential natural anti-cancer agent, for exploring the gastric cancer therapies and as a contender for further in vitro and in vivo investigations.

Comparative study to develop a single method for retrieving wide class of recombinant proteins from classical inclusion bodies.

The formation of inclusion bodies (IBs) is considered as an Achilles heel of heterologous protein expression in bacterial hosts. Wide array of techniques has been developed to recover biochemically challenging proteins from IBs. However, acquiring the active state even from the same protein family was found to be an independent of single established method. Here, we present a new strategy for the recovery of wide sub-classes of recombinant protein from harsh IBs. We found that numerous methods and their combinations for reducing IB formation and producing soluble proteins were not effective, if the inclusion bodies were harsh in nature. On the other hand, different practices with mild solubilization buffers were able to solubilize IBs completely, yet the recovery of active protein requires large screening of refolding buffers. With the integration of previously reported mild solubilization techniques, we proposed an improved method, which comprised low sarkosyl concentration, ranging from 0.05 to 0.1% coupled with slow freezing (- 1 °C/min) and fast thaw (room temperature), resulting in greater solubility and the integrity of solubilized protein. Dilution method was employed with single buffer to restore activity for every sub-class of recombinant protein. Results showed that the recovered protein's activity was significantly higher compared with traditional solubilization/refolding approach. Solubilization of IBs by the described method was proved milder in nature, which restored native-like conformation of proteins within IBs.

A Preclinical Evaluation of the Antitumor Activities of Edible and Medicinal Mushrooms: A Molecular Insight.

Cancer is the leading cause of morbidity and mortality around the globe. For certain types of cancer, chemotherapy drugs have been extensively used for treatment. However, severe side effects and the development of resistance are the drawbacks of these agents. Therefore, development of new agents with no or minimal side effects is of utmost importance. In this regard, natural compounds are well recognized as drugs in several human ailments, including cancer. One class of fungi, "mushrooms," contains numerous compounds that exhibit interesting biological activities, including antitumor activity. Many researchers, including our own group, are focusing on the anticancer potential of different mushrooms and the underlying molecular mechanism behind their action. The aim of this review is to discuss PI3K/AKT, Wnt-CTNNB1, and NF-κB signaling pathways, the occurrence of genetic alterations in them, the association of these aberrations with different human cancers and how different nodes of these pathways are targeted by various substances of mushroom origin. We have given evidence to propose the therapeutic attributes and possible mode of molecular actions of various mushroom-originated compounds. However, anticancer effects were typically demonstrated in in vitro and in vivo models and very limited number of studies have been conducted in the human population. It is our belief that this review will help the research community in designing concrete preclinical and clinical studies to test the anticancer potential of mushroom-originated compounds on different cancers harboring particular genetic alteration(s).