Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models' efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study's results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest's classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.

Impacts of climate change-induced natural hazards on women and their human rights implications: A study in the southwest coast of Bangladesh.

Women in coastal areas are particularly vulnerable to climate change impacts due to existing gender inequality and socio-cultural norms in Bangladesh. This research aims to explore the vulnerability of women to climate change-induced natural hazards, the challenges they face due to the chain impacts of climate change, and the resulting violation of women's rights. Quantitative and qualitative data were used in this study, where 260 structured questionnaire surveys and 15 Focus Group Discussions were performed at Mongla and Shyamnagar Upazilas in Bagerhat and Satkhira districts, respectively. The quantitative data were analyzed using SPSS 26.0 software, and qualitative data was analyzed thematically. The results disclose that most respondents in Mongla and Shyamnagar reported an increase in the occurrence rate of climate change-induced natural hazards, with cyclones, salinity, and riverbank erosion being the most devastating in Mongla and cyclones, salinity, and storm surges in Shyamnagar. It affects the lives and livelihoods of people, with women being particularly vulnerable due to limited access to education, healthcare, and economic opportunities, and women's rights are violated in these areas. Half of the study area's population has been suffering from infections or inflammation in reproductive organs, especially among fisherwomen. The findings of this study recommend that provisions for alternative livelihoods should be made for women, and all women must be brought under the umbrella of fair social safety net programs.

Evolutionary expansion, functional diversification, and transcript profiling of plant Glutathione Peroxidases.

Glutathione peroxidases (GPXs) play a crucial role in combating activated oxygen species and have been widely studied for their involvement in stress responses. In addition to their stress-related functions, GPXs exhibit diverse roles such as immunological response, and involvement in growth and development. These enzymes are found in both animals and plants, with multiple families identified in the evolutionarily diverse species. These families consist of conserved genes as well as unique members, highlighting the evolutionary diversification of GPX members. While animals have eight GPX families, plants possess five families. Notably, plant genomes undergo duplication and expansion events, leading to an increase in the number of GPX genes and the overall size of the GPX superfamily. This expansion suggests a wide range of functional roles for GPX. In this study, the evolutionary diversification, family expansion, and diverse functional roles of GPX enzymes have been investigated. Additionally, the expression profile of Arabidopsis and Oryza sativa GPX genes were analyzed in different developmental stages, tissues, and abiotic stress conditions. Further extensive research has been required to unravel the intricate interplay between GPX and other proteins, to gain the comprehensive mechanism governing the physiological and developmental roles of GPX.

Financial Difficulties Correlate With Mental Health Among Bangladeshi Residents Amid COVID-19 Pandemic: Findings From a Cross-Sectional Survey.

Background: The COVID-19 pandemic is a global threat which has challenged mental resilience and impacted the psychological well-being of people across all age groups globally. The present study aimed to investigate how financial difficulties during the pandemic correlate with mental health among residents of Bangladesh. Methods: A cross-sectional survey was conducted on 4,020 residents from different parts of Bangladesh between July and September 2020, during a period of elevated risk of COVID-19 infection. A self-reported online questionnaire comprising socio-demographic, financial difficulties and psychometric measures (to assess depression, anxiety and stress) was used to gather information from participants. Multivariable logistic regression analysis was performed to determine the factors associated with mental health consequences. Results: The prevalence of depression, anxiety, and stress in the sample were 71.1%, 62.3%, and 56.7%, respectively. Levels of depression, anxiety, and stress were significantly higher among participants who reported female sex, being unmarried, smaller families, higher monthly family income, poor self-perceived health status, living near people who had been infected by COVID-19, probability of decreased income, food scarcity (both during the pandemic and in the future) and the possibility of unemployment. However, due to the nature of the cross-sectional study performed with a convenience sampling method, the causal relationship between variables cannot be justified. Conclusions: After several months of the COVID-19 pandemic in Bangladesh, more than half of the respondents rated their mental health concerns as moderate to severe. The findings highlight the contributing factors of poor mental health which warrant the creation of interventions that address the economic, financial and mental health impacts of the pandemic.

FROZEN! Intracellular multi-electrolyte analysis measures millimolar lithium in mammalian cells.

Lithium salts are commonly used as medication for Bipolar Disorder (BD) and depression. However, there are limited methods to quantify intracellular lithium. Most methods to analyze intracellular electrolytes require tedious sample processing, specialized and often expensive machinery, sometimes involving harmful chemicals, and a bulk amount of the sample. In this work, we report a novel method (FROZEN!) based on cell isolation (from the surrounding medium) through rapid de-ionized water cleaning, followed by flash freezing for preservation. SKOV3 cells were cultured in normal medium and a medium containing 1.0 mM lithium. Lithium and other intracellular electrolytes in the isolated and preserved cells were simultaneously analyzed with laser-induced breakdown spectroscopy (LIBS) and X-ray fluorescence spectroscopy (XRF). Key electrolytes such as sodium, potassium, and magnesium, along with lithium, were detectable at the single-cell level. We found that cells cultured in the lithium medium have an intracellular lithium concentration of 0.5 mM. Concurrently, the intracellular concentrations of other positively charged electrolytes (sodium, potassium, and magnesium) were reduced by the presence of lithium. FROZEN! will greatly facilitate research in intracellular electrolyte balance during drug treatment, or other physiological stresses. In particular, the cell isolation and preservation steps can be easily performed by many laboratories worldwide, after which the samples are sent to an analytical laboratory for electrolyte analysis.

Onabotulinumtoxin Type A Dosage for Upper Face Expression Lines in Males: A Systematic Review of Current Recommendations.

BACKGROUND: Botulinum toxin injection is the most commonly performed minimally invasive aesthetic procedure in men. Despite various recommendations by experts on the use of onabotulinumtoxin type A in the literature, distinct guidelines for its use in males and females do not exist. OBJECTIVES: The aim of this study was to delineate safe and effective doses for the use of onabotulinumtoxin type A in males for correction of upper facial expression lines. METHODS: PubMed (MEDLINE), Embase, the Cochrane database, and Google Scholar were searched from 2002 to 2019 inclusive. Three researchers independently assessed trials for inclusion, extracted data, checked for accuracy, and assessed the evidence with AGREE II. RESULTS: Average dosing recommended for the treatment of upper face expression lines was specific to glabellar frown lines, crow's feet, and horizontal forehead lines. Changing trends and gender variation was noted in each of the studies. Six of the 11 recommendations suggest the need to alter dosing in male patients, although do not give separate recommendations. CONCLUSIONS: There is an urgent need for up-to-date recommendations for the use of onabotulinumtoxin type A in upper face expression lines of male patients.

Double cascade dressed MOSFET from doped Eu3+and Pr3+ in a host YPO4.

In this paper, we study double cascade dressed optical metal oxide semiconductor field-effect transistor (MOSFET) by exploiting enhancement and suppression for mixed-phase (hexagonal + tetragonal) of Eu3+:YPO4 and different phases (hexagonal + tetragonal and pure tetragonal) of Pr3+:YPO4 crystals. We report variation of fine structure energy levels in different doped ions (Eu3+ and Pr3+) in the host YPO crystal. We compared multi-level energy transition from a single dressing laser with single level energy transition from double cascade dressing lasers. Gate delay facilitates multi-energy level dressed transition and is modeled through a Hamiltonian. Based on the results of double cascade dressing, we have realized MOSFET for logic gates (inverter and logic not and gate) with a switching contrast of about 92% using a mixed phase of Pr3+:YPO4.